Numerical simulation study on the effects of liquid water atomization on the flow field and performance of aluminum-based water ramjet engines

In order to investigate the effects of different water inlet droplet diameters on the performance of aluminum-based water ramjet engines, the internal flow field of the engine was analyzed through numerical simulation. The results showed that by selecting a suitable water droplet diameter at the water inlet and controlling the time required for water droplet evaporation and heat absorption, the working range of aluminum-water combustion reaction can be expanded and the specific impulse of the engine can be increased. In engine design and practical application, the design of the water injection nozzle upstream of the engine is critical, and the droplet diameter at the water inlet should be controlled within a suitable range. A diameter that is too large will reduce the evaporation efficiency and hinder the further diffusion of combustion reaction. Droplet sizes that are too small will rapidly evaporate, causing the temperature in the flow field to decrease rapidly, leading to a large range of low-temperature regions in the main reaction zone of the combustion chamber, thereby reducing the overall aluminum-water reaction rate of the engine. In addition, the variation of droplet diameter in the downstream water atomization nozzle has little effect on the aluminum-water reaction in the main combustion zone. However, reducing the droplet diameter can facilitate the downstream diffusion of the combustion reaction, further expanding the combustion range and increasing the specific impulse. Furthermore, it can also reduce the temperature near the wall, which is beneficial for reducing the overall thermal protection requirements of the engine

PRL2/PTP4A2 phosphatase is important for hematopoietic stem cell self-renewal

Hematopoietic stem cell (HSC) self-renewal is tightly controlled by cytokines and other signals in the microenvironment. While stem cell factor (SCF) is an early acting cytokine that activates the receptor tyrosine kinase KIT and promotes HSC maintenance, how SCF/KIT signaling is regulated in HSCs is poorly understood. The protein tyrosine phosphatase 4A (PTP4A) family (aka PRL [phosphatase of regenerating liver] phosphatases), consisting of PTP4A1/PRL1, PTP4A2/PRL2, and PTP4A3/PRL3, represents an intriguing group of phosphatases implicated in cell proliferation and tumorigenesis. However, the role of PTP4A in hematopoiesis remains elusive. To define the role of PTP4A in hematopoiesis, we analyzed HSC behavior in Ptp4a2 (Prl2) deficient mice. We found that Ptp4a2 deficiency impairs HSC self-renewal as revealed by serial bone marrow transplantation assays. Moreover, we observed that Ptp4a2 null hematopoietic stem and progenitor cells (HSPCs) are more quiescent and show reduced activation of the AKT and ERK signaling. Importantly, we discovered that the ability of PTP4A2 to enhance HSPC proliferation and activation of AKT and ERK signaling depends on its phosphatase activity. Furthermore, we found that PTP4A2 is important for SCF-mediated HSPC proliferation and loss of Ptp4a2 decreased the ability of oncogenic KIT/D814V mutant in promoting hematopoietic progenitor cell proliferation. Thus, PTP4A2 plays critical roles in regulating HSC self-renewal and mediating SCF/KIT signaling

Trust toward humans and trust toward artificial intelligence are not associated: Initial insights from self-report and neurostructural brain imaging

The present study examines whether self-reported trust in humans and self-reported trust in [(different) products with built-in] artificial intelligence (AI) are associated with one another and with brain structure. We sampled 90 healthy participants who provided self-reported trust in humans and AI and underwent brain structural magnetic resonance imaging assessment. We found that trust in humans, as measured by the trust facet of the personality inventory NEO-PI-R, and trust in AI products, as measured by items assessing attitudes toward AI and by a composite score based on items assessing trust toward products with in-built AI, were not significantly correlated. We also used a concomitant dimensional neuroimaging approach employing a data-driven source-based morphometry (SBM) analysis of gray-matter-density to investigate neurostructural associations with each trust domain. We found that trust in humans was negatively (and significantly) correlated with an SBM component encompassing striato-thalamic and prefrontal regions. We did not observe significant brain structural association with trust in AI. The present findings provide evidence that trust in humans and trust in AI seem to be dissociable constructs. While the personal disposition to trust in humans might be “hardwired” to the brain’s neurostructural architecture (at least from an individual differences perspective), a corresponding significant link for the disposition to trust AI was not observed. These findings represent an initial step toward elucidating how different forms of trust might be processed on the behavioral and brain level

Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery

Monitoring and discriminating co-epidemic diseases and pests at regional scales are of practical importance in guiding differential treatment. A combination of vegetation and environmental parameters could improve the accuracy for discriminating crop diseases and pests. Different diseases and pests could cause similar stresses and symptoms during the same crop growth period, so combining growth period information can be useful for discerning different changes in crop diseases and pests. Additionally, problems associated with imbalanced data often have detrimental effects on the performance of image classification. In this study, we developed an approach for discriminating crop diseases and pests based on bi-temporal Landsat-8 satellite imagery integrating both crop growth and environmental parameters. As a case study, the approach was applied to data during a period of typical co-epidemic outbreak of winter wheat powdery mildew and aphids in the Shijiazhuang area of Hebei Province, China. Firstly, bi-temporal remotely sensed features characterizing growth indices and environmental factors were calculated based on two Landsat-8 images. The synthetic minority oversampling technique (SMOTE) algorithm was used to resample the imbalanced training data set before model construction. Then, a back propagation neural network (BPNN) based on a new training data set balanced by the SMOTE approach (SMOTE-BPNN) was developed to generate the regional wheat disease and pest distribution maps. The original training data set-based BPNN and support vector machine (SVM) methods were used for comparison and testing of the initial results. Our findings suggest that the proposed approach incorporating both growth and environmental parameters of different crop periods could distinguish wheat powdery mildew and aphids at the regional scale. The bi-temporal growth indices and environmental factors-based SMOTE-BPNN, BPNN, and SVM models all had an overall accuracy high than 80%. Meanwhile, the SMOTE-BPNN method had the highest G-means among the three methods. These results revealed that the combination of bi-temporal crop growth and environmental parameters is essential for improving the accuracy of the crop disease and pest discriminating models. The combination of SMOTE and BPNN could effectively improve the discrimination accuracy of the minor disease or pest

Protein Tyrosine Phosphatase PRL2 Mediates Notch and Kit Signals in Early T Cell Progenitors

The molecular pathways regulating lymphoid priming, fate, and development of multipotent bone marrow hematopoietic stem and progenitor cells (HSPCs) that continuously feed thymic progenitors remain largely unknown. While Notch signal is indispensable for T cell specification and differentiation, the downstream effectors are not well understood. PRL2, a protein tyrosine phosphatase that regulates hematopoietic stem cell proliferation and self-renewal, is highly expressed in murine thymocyte progenitors. Here we demonstrate that protein tyrosine phosphatase PRL2 and receptor tyrosine kinase c-Kit are critical downstream targets and effectors of the canonical Notch/RBPJ pathway in early T cell progenitors. While PRL2 deficiency resulted in moderate defects of thymopoiesis in the steady state, de novo generation of T cells from Prl2 null hematopoietic stem cells was significantly reduced following transplantation. Prl2 null HSPCs also showed impaired T cell differentiation in vitro. We found that Notch/RBPJ signaling upregulated PRL2 as well as c-Kit expression in T cell progenitors. Further, PRL2 sustains Notch-mediated c-Kit expression and enhances stem cell factor/c-Kit signaling in T cell progenitors, promoting effective DN1-DN2 transition. Thus, we have identified a critical role for PRL2 phosphatase in mediating Notch and c-Kit signals in early T cell progenitors

Acetic Acid Influences BRL-3A Cell Lipid Metabolism via the AMPK Signalling Pathway

Background/Aims: Acetic acid (AcOH), a short-chain fatty acid, is reported to have some beneficial effects on metabolism. Therefore, the aim of this study was to investigate the regulatory mechanism of acetic acid on hepatic lipid metabolism in BRL-3A cells. Methods: We cultured and treated BRL-3A cells with different concentrations of sodium acetate (neutralized acetic acid) and BML-275 (an AMPKα inhibitor). The total lipid droplet area was measured by oil red O staining, and the triglyceride content was determined by a triglyceride detection kit. We detected mRNA and protein levels of lipid metabolism-related signalling molecules by RT-PCR and Western blot. Results: Acetic acid treatment increased AMPKα phosphorylation, which subsequently increased the expression and transcriptional activity of peroxisome proliferator-activated receptor α and upregulated the expression of lipid oxidation genes. These changes ultimate led to increasing levels of lipid oxidation in BRL-3A cells. Furthermore, elevated AMPKα phosphorylation reduced the expression and transcriptional activity of the sterol regulatory element-binding protein 1c, which reduced the expression of lipogenic genes, thereby decreasing lipid biosynthesis in BRL-3A cells. Consequently, triglyceride content in acetate-treated BRL-3A cells was significantly decreased. Conclusions: These results indicate that acetic acid activates the AMPKα signalling pathway, leading to increased lipid oxidation and decreased lipid synthesis in BRL-3A cells, thereby reducing liver fat accumulation in vitro

Integrating Early Growth Information to Monitor Winter Wheat Powdery Mildew Using Multi-Temporal Landsat-8 Imagery

Powdery mildew is one of the dominant diseases in winter wheat. The accurate monitoring of powdery mildew is important for crop management and production. Satellite-based remote sensing monitoring has been proven as an efficient tool for regional disease detection and monitoring. However, the information provided by single-date satellite scene is hard to achieve acceptable accuracy for powdery mildew disease, and incorporation of early period contextual information of winter wheat can improve this situation. In this study, a multi-temporal satellite data based powdery mildew detecting approach had been developed for regional disease mapping. Firstly, the Lansat-8 scenes that covered six winter wheat growth periods (expressed in chronological order as periods 1 to 6) were collected to calculate typical vegetation indices (VIs), which include disease water stress index (DSWI), optimized soil adjusted vegetation index (OSAVI), shortwave infrared water stress index (SIWSI), and triangular vegetation index (TVI). A multi-temporal VIs-based k-nearest neighbors (KNN) approach was then developed to produce the regional disease distribution. Meanwhile, a backward stepwise elimination method was used to confirm the optimal multi-temporal combination for KNN monitoring model. A classification and regression tree (CART) and back propagation neural networks (BPNN) approaches were used for comparison and validation of initial results. VIs of all periods except 1 and 3 provided the best multi-temporal data set for winter wheat powdery mildew monitoring. Compared with the traditional single-date (period 6) image, the multi-temporal images based KNN approach provided more disease information during the disease development, and had an accuracy of 84.6%. Meanwhile, the accuracy of the proposed approach had 11.5% and 3.8% higher than the multi-temporal images-based CART and BPNN models’, respectively. These results suggest that the use of satellite images for early critical disease infection periods is essential for improving the accuracy of monitoring models. Additionally, satellite imagery also assists in monitoring powdery mildew in late wheat growth periods

Identification of fusarium head blight in winter wheat ears using continuous wavelet analysis

Fusarium head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem aecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of Fusarium head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination R2 between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the R2 values and the statistically significant (p-value of t-test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest R2 values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coecient of correlation R obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying Fusarium head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of Fusarium head blight in winter wheat ears