Genome-wide identification and characterization of Respiratory Burst Oxidase Homolog genes in six Rosaceae species and an analysis of their effects on adventitious rooting in apple.

Adventitious root formation is essential for plant propagation, development, and response to various stresses. Reactive oxygen species (ROS) are essential for adventitious root formation. However, information on Respiratory Burst Oxidase Homolog (RBOH), a key enzyme that catalyzes the production ROS, remains limited in woody plants. Here, a total of 44 RBOH genes were identified from six Rosaceae species (Malus domestica, Prunus avium, Prunus dulcis 'Texas', Rubus occidentalis, Fragaria vesca and Rosa chinensis), including ten from M. domestica. Their phylogenetic relationships, conserved motifs and gene structures were analyzed. Exogenous treatment with the RBOH protein inhibitor diphenyleneiodonium (DPI) completely inhibited adventitious root formation, whereas exogenous H2O2 treatment enhanced adventitious root formation. In addition, we found that ROS accumulated during adventitious root primordium inducing process. The expression levels of MdRBOH-H, MdRBOH-J, MdRBOH-A, MdRBOH-E1 and MdRBOH-K increased more than two-fold at days 3 or 9 after auxin treatment. In addition, cis-acting element analysis revealed that the MdRBOH-E1 promoter contained an auxin-responsive element and the MdRBOH-K promoter contained a meristem expression element. Based on the combined results from exogenous DPI and H2O2 treatment, spatiotemporal expression profiling, and cis-element analysis, MdRBOH-E1 and MdRBOH-K appear to be candidates for the control of adventitious rooting in apple

Phosphoglycerate dehydrogenase promotes pancreatic cancer development by interacting with eIF4A1 and eIF4E

Abstract Background Pancreatic cancer is one of the most malignant cancers. The overall 5-year survival rate of its patients is 8%, the lowest among major cancer types. It is very urgent to study the development mechanisms of this cancer and provide potential targets for therapeutics design. Glucose, one of the most essential nutrients, is highly exploited for aerobic glycolysis in tumor cells to provide building blocks. However, the glucose consumption manner in pancreatic cancer cells is unclear. And the mechanism of the substantial metabolic pathway promoting pancreatic cancer development is also unrevealed. Methods 13C6 glucose was used to trace the glucose carbon flux and detected by mass spectrum. The expressions of PHGDH were determined in cells and pancreatic adenocarcinomas. Knockdown and overexpression were performed to investigate the roles of PHGDH on pancreatic cancer cell proliferation, colony formation and tumor growth. The mechanisms of PHGDH promoting pancreatic cancer development were studied by identifying the interacting proteins and detecting the regulatory functions on translation initiations. Results Pancreatic cancer cells PANC-1 consumed large amounts of glucose in the serine and glycine de novo synthesis. Phosphoglycerate dehydrogenase (PHGDH) highly expressed and controlled this pathway. Knockdown of PHGDH significantly attenuated the tumor growth and prolonged the survival of tumor bearing mice. The pancreatic adenocarcinoma patients with low PHGDH expression had better overall survival. Mechanistically, knockdown of PHGDH inhibited cell proliferation and tumorigenesis through disrupting the cell-cell tight junctions and the related proteins expression. Besides catalyzing serine synthesis to activate AKT pathway, PHGDH was found to interact with the translation initiation factors eIF4A1 and eIF4E and facilitated the assembly of the complex eIF4F on 5’ mRNA structure to promote the relevant proteins expression. Conclusion Besides catalyzing serine synthesis, PHGDH promotes pancreatic cancer development through enhancing the translation initiations by interacting with eIF4A1 and eIF4E. Inhibiting the interactions of PHGDH/eIF4A1 and PHGDH/eIF4E will provide potential targets for anti-tumor therapeutics development

Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization

Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative samples used for machine learning. Recurrent neural network (RNN) has a memory function, so when using an RNN for landslide susceptibility mapping purposes, the input order of the landslide-influencing factors affects the resulting quality of the model. The information value analysis calculates the landslide-influencing factors, determines the input order of data based on the importance of any specific factor in determining the landslide susceptibility, and improves the prediction potential of recurrent neural networks. The simple recurrent unit (SRU), a newly proposed variant of the recurrent neural network, is characterized by possessing a faster processing speed and currently has less application history in landslide susceptibility mapping. This study used recurrent neural networks optimized by information value analysis for landslide susceptibility mapping in Xinhui District, Jiangmen City, Guangdong Province, China. Four models were constructed: the RNN model with optimized negative sample selection, the SRU model with optimized negative sample selection, the RNN model, and the SRU model. The results show that the RNN model with optimized negative sample selection has the best performance in terms of AUC value (0.9280), followed by the SRU model with optimized negative sample selection (0.9057), the RNN model (0.7277), and the SRU model (0.6355). In addition, several objective measures of accuracy (0.8598), recall (0.8302), F1 score (0.8544), Matthews correlation coefficient (0.7206), and the receiver operating characteristic also show that the RNN model performs the best. Therefore, the information value analysis can be used to optimize negative sample selection in landslide sensitivity mapping in order to improve the model’s performance; second, SRU is a weaker method than RNN in terms of model performance

Progress and challenges of big data research on petrology and geochemistry

We are entering the era of big data, and big data achievements have benefited ordinary people. However, big data application in geochemistry research has not been really appreciated. Big data is a kind of method and a pattern of thought. It is different from traditional scientific research methods and thinking in that it starts from the data and adopts full data mode. This paper introduces basalt, andesite and continental marginal arc basalt tectonic environment discrimination diagrams made by the author and his collaborators using all the global rock geochemical data available. We have discussed the following scientific problems of petrology and geochemistry using big data research: 1. we found many better illustrations in the study of discriminant diagrams, mainly relying on the relationship among the major elements, transition elements, and metal elements. What the points between the relationships are? Why did they work? These new questions need to be studied by the geochemists. 2. Data mining found that mid-ocean ridge is extremely lack of intermediate-acidic rocks. Does it mean that the upper mantle is an acute shortage of water? 3. This study found that the Miocene is the most developed epoch of magmatic activities. The Miocene appeared many important geological events in the world. Is there a connection between them? 4. The Miocene adakite most developed in the world, according to adakite exposed. There may be a giant Eurasian plateau from the Tibetan plateau to the Carpathians. 5. According to the spatial and temporal distribution of Cenozoic picrite, we put forward how to realize a global hotspot issues, etc. The paper also put forward some suggestions of further research and emphasized that science has entered the era of big data. In the era of big data, scientific classification standards have changed: any discipline which can be expressed in data is called science, and which cannot be expressed in data is called non-science. Whether it can be expressed by data is the watershed of science and non-science. In the era of big data, the geology encountered an unprecedented crisis. According to our forecast, in the foreseeable future, geophysics will be far ahead of geology. Space science will boom, and geochemistry will lead the way for a long time in the field of geology. Keywords: Big data, Petrology, Geochemistry, Basalt, Andesite, Discrimination diagra

Influence of Laser Additive Manufacturing and Laser Polishing on Microstructures and Mechanical Properties of High-Strength Maraging Steel Metal Materials

To increase the surface quality of the high-strength maraging steel metal materials, a new method of executing the additive manufacturing process and subtraction polishing process of maraging steel metal materials was studied. The mechanical properties of maraging steel metal materials before and after laser powder bed fusion (LPBF) polishing were compared and analyzed. The influence of laser parameters on the formability of high-strength MS metal materials was studied, with MS additive parts successfully prepared. The initial surfaces had roughness values of 6.198–7.92 μm. The metal additive manufacturing parts were polished with double laser beams. Confocal microscopy, scanning electron microscopy, and X-ray diffraction were used to obtain the microstructure and phase composition of the microstructures. The microhardness of high-strength maraging forming parts by using a microhardness tester and the mechanical properties were analyzed. The results showed that the surface roughness was considerably reduced to lengthen the service life of the high-strength MS metal materials from an initial roughness of Sa = 6.3 μm to Sa = 0.98 μm, with the surface hardness increased and the martensite content decreased after using double-laser-beam polishing

Mitochondrial Lon protease at the crossroads of oxidative stress, ageing and cancer

Lon protease is a nuclear DNA-encoded mitochondrial enzyme highly conserved throughout evolution, involved in the degradation of damaged and oxidized proteins of the mitochondrial matrix, in the correct folding of proteins imported in mitochondria, and in the maintenance of mitochondrial DNA. Lon expression is induced by various stimuli, including hypoxia and reactive oxygen species, and provides protection against cell stress. Lon down-regulation is associated with ageing and with cell senescence, while up-regulation is observed in tumour cells, and is correlated with a more aggressive phenotype of cancer. Lon up-regulation contributes to metabolic reprogramming observed in cancer, favours the switch from a respiratory to a glycolytic metabolism, helping cancer cell survival in the tumour microenvironment, and contributes to epithelial to mesenchymal transition. Silencing of Lon, or pharmacological inhibition of its activity, causes cell death in various cancer cells. Thus, Lon can be included in the growing class of proteins that are not responsible for oncogenic transformation, but that are essential for survival and proliferation of cancer cells, and that can be considered as a new target for development of anticancer drugs

Effects of Sevoflurane versus Propofol on Endogenous Nitric Oxide Metabolism during Laparoscopic Surgery

For laparoscopic surgery, it is very difficult to assess the effect of different medicines used in the surgical procedure on the surgical results. In the past, doctors could use sevoflurane to numb and calm patients. For decades, this type of treatment has been fairly reliable and effective, but for laparoscopic surgery, the use of sevoflurane can lead to a wide range of blood glucose changes, so in recent years, sevoflurane compared to propofol in laparoscopic surgery on endogenous and nitrogen oxide metabolism has been studied more and more. In this paper, a variety of research methods were used to study the phenomenon of shock and excessive anesthesia encountered by patients in the treatment process. Through observation and drug experiment of patients in different treatment courses and treatment stages, patients were asked to use sevoflurane and propofol to conduct double-blind experiments on their own drug effects. At the same time, through the long-term observation of patients with different diseases and patients who need laparoscopic surgery, the nitrogen oxide metabolism in patients with sevoflurane compared with propofol endogenous was studied and analyzed. Through three groups of different conditions, the experimental group, the blind test group, and the control group were studied. To conclude, in laparoscopic surgery, the use of sevoflurane compared with propofol can have a good impact on the endogenous drug and nitrogen oxide metabolism. It can achieve a good effect on the anesthesia effect of surgery, the maintenance of patient’s physical signs and heart rate, which is very beneficial to the operation. Conclusion. Sevoflurane compared with propofol has a good effect on endogenous nitrogen oxide metabolism in laparoscopic surgery

Geochemical Fractal Characteristics of Deep-Sea REE-Rich Sediments in the Western Pacific

At present, the challenge for geochemical prospecting of deep-sea, rare, earth-rich sediments is the selection of exploration sites. Because of the unpredictability of offshore operations, the distribution and selection of survey line stations face great challenges. In this paper, we study the fact that the concentration distribution of geochemistry in space may be a part of a special complex process, which is called the multifractal. It requires a large number of indexes to characterize its scale characteristics. Based on the multifractal spectrum, 38 geochemical indices in the Pigafetta Basin region of the western Pacific Ocean are divided into three categories. The three indexes are distinguished by the multifractal parameters. The results of multifractals are in good agreement with those of the cluster analysis and principal component analysis. In addition, on the basis of principal component analysis, we further used the multifractal filtering method to extract element anomalies and delineate element enrichment regions. The first principal component clearly represents the potential element enrichment area. The spatial analysis technique and multifractal method proposed in the paper provide a new idea for geochemical prospecting of deep-sea, rare, earth-rich sediments