Evaluation and Analysis of Soil Temperature Data over Poyang Lake Basin, China

Soil temperature reflects the impact of local factors, such as the vegetation, soil, and atmosphere of a region. Therefore, it is important to understand the regional variation of soil temperature. However, given the lack of observations with adequate spatial and/or temporal coverage, it is often difficult to use observational data to study the regional variation. Based on the observational data from Nanchang and Ganzhou stations and ERA-Interim/Land reanalysis data, this study analyzed the spatiotemporal distribution characteristics of soil temperature over Poyang Lake Basin. Four soil depths were examined, 0–7, 7–28, 28–100, and 100–289 cm, recorded as ST1, ST2, ST3, and ST4, respectively. The results showed close correlations between observation data and reanalysis data at different depths. Reanalysis data could reproduce the main spatiotemporal distributions of soil temperature over the Poyang Lake Basin but generally underestimated their magnitudes. Temporally, there was a clear warming trend in the basin. Seasonally, the temperature increase was the most rapid in spring and the slowest in summer, except for ST4, which increased the fastest in spring and the slowest in winter. The temperature increase was faster for ST1 than the other depths. The warming trend was almost the same for ST2, ST3, and ST4. An abrupt change of annual soil temperature at all depths occurred in 1997, and annual soil temperatures at all depths were abnormally low in 1984. Spatially, annual soil temperature decreased with latitude, except for the summer ST1. Because of the high temperature and precipitation in summer, the ST1 values were higher around the lake and the river. The climatic trend of soil temperature generally increased from south to north, which was opposite to the distribution of soil temperature. These findings provide a basis for understanding and assessing the variation of soil temperature in the Poyang Lake Basin

Study of optimal layout based on integrated probabilistic framework (IPF): Case of a crude oil tank farm

International audienceThis paper gives an integrated probabilistic framework (IPF) that deals with the optimal layout of facilities in an industrial plant. The specific case of a crude oil tank farm is detailed in this present paper, which includes the tank fire as well as the corresponding optimal layout based on inherent safety and evacuation.The tank fire can be caused by the oxidative self-heating of pyrophoric iron sulfides which extensively exist on the inner wall of the crude oil tank, especially in the respiratory/safety valves. Oxidative self-heating, or spontaneous combustion of iron sulfides is a process of oxidation and generally influenced by five external factors including water content, mass per unit area of iron sulfides, operating temperature of tank, flow rate and concentration of oxygen facing the iron sulfides. According to the previous literature about self-heating process of iron sulfides, the maximum temperature (TmaxTmax) of solid phase is a vital indicator representing the pyrophoric feature of iron sulfides in specific circumstances. And the maximum temperature (TmaxTmax) can be predicted by the model developed from support vector machine (SVM) technique. While the predicted maximum temperature (TmaxTmax) is compared with a defined threshold value, it can be revealed whether the oxidative self-heating of iron sulfides will lead to explosion and then cause tank fire. On this grounds, the probability of tank fire due to the oxidative self-heating of iron sulfides can be obtained by Monte Carlo simulations.For tank fire, the major physical damage to the surrounding tanks and workers is thermal radiation rather than overpressure or missile projection. Considering the worst case scenario, that is the vapor-liquid interface in the tank covered with fire, then the thermal radiation flux passing through a receiver at a specified distance away from the tank can be derived. In reverse, the critical horizontal distance between tank and receiver can be obtained if the critical thermal radiation flux through a receiver is given. Assuming that the minimum and maximum risks of thermal radiation to a receiver are separately 0 and 1 corresponding to different thermal radiation fluxes, then the risk of a tank or worker receiving a given thermal radiation flux can be determined by the thermal radiation flux equation.In a crude oil tank farm containing more than one tank, the potential thermal radiation flux received by an object at an arbitrary location is the superposition of those from different tanks. For the optimization of space collocation and floor area of tank farm from inherently safe design, if the damage risk of an object from other overall tanks equals to the critical acceptable damage risk, and the corresponding floor area of tank farm is the minimum, it will certainly result in an optimal space collocation. The handling method for the mentioned problem inherently reduces property loss and casualty to some extent

Integration of Small RNA and Degradome Sequencing Reveals the Regulatory Network of Al-Induced Programmed Cell Death in Peanut

Peanut is one of the most important oil crops in the world. In China, the peanut is highly produced in its southern part, in which the arable land is dominated by acid soil. At present, miRNAs have been identified in stress response, but their roles and mechanisms are not clear, and no miRNA studies have been found related to aluminum (Al)-induced programmed cell death (PCD). In the present study, transcriptomics, sRNAs, and degradome analysis in the root tips of two peanut cultivars ZH2 (Al-sensitive, S) and 99-1507 (Al-tolerant, T) were carried out. Here, we generated a comprehensive resource focused on identifying key regulatory miRNA-target circuits that regulate PCD under Al stress. Through deep sequencing, 2284 miRNAs were identified and 147 miRNAs were differentially expressed under Al stress. Furthermore, 19237 target genes of 749 miRNAs were validated by degradome sequencing. GO and KEGG analyses of differential miRNA targets showed that the pathways of synthesis and degradation of ketone bodies, citrate cycle (TCA cycle), and peroxisome were responded to Al stress. The combined analysis of the degradome data sets revealed 89 miRNA-mRNA interactions that may regulate PCD under Al stress. Ubiquitination may be involved in Al-induced PCD in peanut. The regulatory networks were constructed based on the differentially expressed miRNAs and their targets related to PCD. Our results will provide a useful platform to research on PCD induced by Al and new insights into the genetic engineering for plant stress response

Comparative Transcriptome Analysis Provides Insights into the Resistance in Pueraria [<i>Pueraria lobata (Willd.) Ohwi</i>] in Response to Pseudo-Rust Disease

Pueraria lobata is an important medicinal and edible homologous plant that is widely cultivated in Asian countries. However, its production and quality are seriously threatened by its susceptibility to pseudo-rust disease. The underlying molecular mechanisms are poorly known, particularly from a transcriptional perspective. Pseudo-rust disease is a major disease in pueraria, primarily caused by Synchytrium puerariae Miy (SpM). In this study, transcriptomic profiles were analyzed and compared between two pueraria varieties: the disease-resistant variety (GUIGE18) and the susceptible variety (GUIGE8). The results suggest that the number of DEGs in GUIGE18 is always more than in GUIGE8 at each of the three time points after SpM infection, indicating that their responses to SpM infection may be different, and that the active response of GUIGE18 to SpM infection may occur earlier than that of GUIGE8. A total of 7044 differentially expressed genes (DEGs) were identified, and 406 co-expressed DEGs were screened out. Transcription factor analysis among the DEGs revealed that the bHLH, WRKY, ERF, and MYB families may play an important role in the interaction between pueraria and pathogens. A GO and KEGG enrichment analysis of these DEGs showed that they were mainly involved in the following pathways: metabolic, defense response, plant hormone signal transduction, MAPK signaling pathway-plant, plant pathogen interaction, flavonoid biosynthesis, phenylpropanoid biosynthesis, and secondary metabolite biosynthesis. The CPK, CESA, PME, and CYP gene families may play important roles in the early stages after SpM infection. The DEGs that encode antioxidase (CAT, XDH, and SOD) were much more up-regulated. Defense enzyme activity, endogenous hormones, and flavonoid content changed significantly in the two varieties at the three infection stages. Finally, we speculated on the regulatory pathways of pueraria pseudo-rust and found that an oxidation-reduction process, flavonoid biosynthesis, and ABA signaling genes may be associated with the response to SpM infection in pueraria. These results expand the understanding of pueraria resistance and physiological regulations by multiple pathways

Effect of applied stress level on anisotropy in creep-aging behavior of Al–Cu–Li alloy

In the present work, the anisotropic creep aging (CA) behavior and microstructural evolution of Al–Cu–Li alloy are investigated under different stress levels. The results indicate that as the stress level increases, the anisotropic creep behavior of Al–Cu–Li alloy changes significantly. As the applied stress level increases, the in-plane anisotropy (IPA) value of creep strain decreases and then increases. In addition, the strength also exhibits significant anisotropy, and as the applied stress level increases, there are differences in the strength changes of samples with different orientations at different creep aging times. Electron backscatter diffraction (EBSD), Energy-dispersive x-ray spectroscopy (EDS) and transmission electron microscopy (TEM) methods are used to study the evolution of texture and microstructure, as well as their effects on the anisotropy evolution of materials. Microscopic observation reveals that the anisotropy changes in creep and strength can be attributed to the difference in dislocation density and grain boundary slip phenomenon during the initial creep stage in samples with different orientations, as well as the stress orientation effect of precipitated phases, especially T1 phase. The study reveals a new mechanism of anisotropic CA mechanism in Al–Cu–Li alloy under different applied stress levels

Machine learning-based integration identifies the ferroptosis hub genes in nonalcoholic steatohepatitis

Abstract Background Ferroptosis, is characterized by lipid peroxidation of fatty acids in the presence of iron ions, which leads to cell apoptosis. This leads to the disruption of metabolic pathways, ultimately resulting in liver dysfunction. Although ferroptosis is linked to nonalcoholic steatohepatitis (NASH), understanding the key ferroptosis-related genes (FRGs) involved in NASH remains incomplete. NASH may be targeted therapeutically by identifying the genes responsible for ferroptosis. Methods To identify ferroptosis-related genes and develop a ferroptosis-related signature (FeRS), 113 machine-learning algorithm combinations were used. Results The FeRS constructed using the Generalized Linear Model Boosting algorithm and Gradient Boosting Machine algorithms exhibited the best prediction performance for NASH. Eight FRGs, with ZFP36 identified by the algorithms as the most crucial, were incorporated into in FeRS. ZFP36 is significantly enriched in various immune cell types and exhibits significant positive correlations with most immune signatures. Conclusion ZFP36 is a key FRG involved in NASH pathogenesis