Regulation of High-Temperature Stress Response by Small RNAs

Temperature extremes constitute one of the most common environmental stresses that adversely affect the growth and development of plants. Transcriptional regulation of temperature stress responses, particularly involving protein-coding gene networks, has been intensively studied in recent years. High-throughput sequencing technologies enabled the detection of a great number of small RNAs that have been found to change during and following temperature stress. The precise molecular action of some of these has been elucidated in detail. In the present chapter, we summarize the current understanding of small RNA-mediated modulation of high- temperature stress-regulatory pathways including basal stress responses, acclimation, and thermo-memory. We gather evidence that suggests that small RNA network changes, involving multiple upregulated and downregulated small RNAs, balance the trade-off between growth/development and stress responses, in order to ensure successful adaptation. We highlight specific characteristics of small RNA-based tem- perature stress regulation in crop plants. Finally, we explore the perspectives of the use of small RNAs in breeding to improve stress tolerance, which may be relevant for agriculture in the near future

ASPN Synergizes with HAPLN1 to Inhibit the Osteogenic Differentiation of Bone Marrow Mesenchymal Stromal Cells and Extracellular Matrix Mineralization of Osteoblasts

Objective Bone marrow mesenchymal stromal cells (BMSCs) are major sources of osteogenic precursor cells in bone remodeling, which directly participate in osteoporosis (OP) progression. However, the involved specific mechanisms of BMSCs in OP warrant mass investigations. Initially, our bioinformatics analysis uncovered the prominent up‐regulation of Asporin (ASPN) and proteoglycan link protein 1 (HAPLN1) in osteoblasts (OBs) of OP patients and their possible protein interaction. Hence, this study aimed to explore the effects of ASPN and HAPLN1 on osteogenic differentiation of BMSCs, extracellular matrix (ECM) mineralization of OBs, and osteoclastogenesis, hoping to offer research basis for OP treatment. Methods GSE156508 dataset was used for analysis and screening to acquire the differentially expressed genes in OBs of OP patients, followed by the predicative analysis via STRING. OP mouse models were induced by ovariectomy (OVX), and ASPN and HAPLN1 expression was determined. BMSCs and bone marrow macrophages (BMMs) were isolated from OVX mice and induced for osteogenic differentiation and osteoclastogenesis, respectively. After knockdown experiments, we assessed adipogenic differentiation and osteogenic differentiation in BMSCs. Osteogenic (OPN, OCN, and COL1A1) and osteoclast (Nfatc1 and c‐Fos) marker protein expression was determined. The binding of ASPN to HAPLN1 was analyzed. Results High expression of ASPN and HAPLN1 and their protein interaction were observed in OBs of OP patients via bioinformatics and in bone tissues of OVX mice. ASPN interacted with HAPLN1 in BMSCs of OVX mice. ASPN/HAPLN1 knockdown increased ALP, OPN, OCN, and COL1A1 protein expression and ECM mineralization in BMSCs while decreasing Nfatc1 and c‐Fos expression in BMMs. These effects were aggravated by the simultaneous knockdown of ASPN and HAPLN1. Conclusion Our results indicate that ASPN synergises with HAPLN1 to suppress the osteogenic differentiation of BMSCs and ECM mineralization of OBs and promote the osteoclastogenesis in OP

Hybrid inversion method and sensitivity analysis of inherent deformations of welded joints

In order to efficiently predict the welding deformation of large and complex welded structures based on the inherent deformation method, it is necessary to obtain the inherent deformations of each weld seam beforehand. In this paper, a new method was proposed to inverse inherent deformations by combining inherent deformation method and complex method. The validity of the proposed hybrid inversion method was proved by the comparison with the results of thermal-elastic-plastic (TEP) model the effectiveness of which had been demonstrated by the welding deformation and residual stress obtained from the butt-welded experiment. And the independence of the method on initial value is verified by inversion results using different initial complex shapes. Finally, the sensitivity analysis of inherent deformations was also carried out, and the analysis result shows that longitudinal shrinkage, transverse shrinkage and transverse curvature have a more significant effect on calculation accuracy than longitudinal curvature for the present welded plates

Suspension of Australian National Electricity Market in 2022 Necessitates Mechanism Evolution Ensuring Power Supply Security

The National Electricity Market (NEM) in Australia was suspended during June 15-23, 2022, with a primary attribution to the lack of available generation capacity. This incident is noteworthy because it was the first market suspension in NEM's history and took place in a major energy exporting country. In this letter, we review the outline and impacts of the incident. From the perspectives of market regulation, electricity supply, and electricity demand, we identify three underlying causes of the market suspension and offer four recommendations for the market mechanism evolution to ensure power supply security

A Dimensionality Reduction Algorithm for Unstructured Campus Big Data Fusion

Data modeling and dimensionality reduction are important research points in the field of big data. At present, there is no effective model to realize the consistent representation and fusion of different types of data of students in unstructured campus big data. In addition, in the process of big data processing, the amount of data is too large and the intermediate results are too complex, which seriously affects the efficiency of big data dimension reduction. To solve the above problems, this paper proposes an incremental high order singular value decomposition dimensionality (icHOSVD) reduction algorithm for unstructured campus big data. In this algorithm, the characteristics of audio, video, image and text data in unstructured campus student data are tensioned to form a sub-tensor model, and the semi-tensor product is used to fuse the sub-tensor model into a unified model as the individual student tensor model. On the basis of individual model fusion, the campus big data fusion model was segmented, and each segmented small tensor model was dimensioned by icHOSVD reduction to obtain an approximate tensor as the symmetric tensor that could replace the original tensor, so as to solve the problem of large volume of tensor fusion model and repeated calculation of intermediate results in data processing. The experimental results show that the proposed algorithm can effectively reduce the computational complexity and improve the performance compared with traditional data dimension reduction algorithms. The research results can be applied to campus big data analysis and decision-making

Recognition of visual-related non-driving activities using a dual-camera monitoring system

For a Level 3 automated vehicle, according to the SAE International Automation Levels definition (J3016), the identification of non-driving activities (NDAs) that the driver is engaging with is of great importance in the design of an intelligent take-over interface. Much of the existing literature focuses on the driver take-over strategy with associated Human-Machine Interaction design. This paper proposes a dual-camera based framework to identify and track NDAs that require visual attention. This is achieved by mapping the driver's gaze using a nonlinear system identification approach, on the object scene, recognised by a deep learning algorithm. A novel gaze-based region of interest (ROI) selection module is introduced and contributes about a 30% improvement in average success rate and about a 60% reduction in average processing time compared to the results without this module. This framework has been successfully demonstrated to identify five types of NDA required visual attention with an average success rate of 86.18%. The outcome of this research could be applicable to the identification of other NDAs and the tracking of NDAs within a certain time window could potentially be used to evaluate the driver's attention level for both automated and human-driving vehicle

Freshwater Water-Quality Criteria for Chloride and Guidance for the Revision of the Water-Quality Standard in China

The chloride in water frequently exceeds the standard; directly quoting foreign water-quality criteria (WQC) or standards will inevitably reduce the scientific value of the water-quality standard (WQS) in China. Additionally, this may lead to the under- or overprotection of water bodies. This study summarized the sources, distribution, pollution status, and hazards of chloride in China’s water bodies. Additionally, we compared and analyzed the basis for setting WQS limits for chloride in China; we systematically analyzed the basis for setting the WQC for chloride in foreign countries, especially the United States. Finally, we collected and screened data on the toxicity of chloride to aquatic organisms; we also used the species sensitivity distribution (SSD) method to derive the WQC value for chloride, which is 187.5 mg·L−1. We put forward a recommended value for freshwater WQS for chloride in China: less than 200 mg·L−1. The study of a freshwater WQC for chloride is not only a key point of environmental research, but also an urgent demand to ensure water ecological protection in China. The results of this study are of great significance for the environmental management of chloride, protection of aquatic organisms, and risk assessment, especially for the revision of WQSs

Morphology evolution of molten pool of Beta-Ti/Ti6Al4V based on the modified double ellipsoidal heat source model of SLM

The SLM (Selective Laser Melting) process involves a complex evolution. Experimental observation combined with numerical simulation analysis is an important way to study the evolution of molten pools. This study aims to improve the accuracy of numerical prediction for the evolution of microstructure (β grains and phases) during the cooling process of the molten pool, and combine with experimental results to gain a comprehensive understanding of the morphology characteristics of the melt pool and the micro mechanism of material forming. The β-Ti/Ti6Al4V specimens are prepared by using the selective laser melting equipment, and then the effect of process parameters on the molten pool micro-morphology is analyzed. A heat source model on the basis of experimental data is developed to provide the correlation between melt pool morphology and process. Numerical simulation and experimental investigation are applied to study the melt pool temperature field, velocity field and microstructure evolution. It is indicated that the shape of the molten pool after the modified heat source model agrees with the experimental methods. Furthermore, the analysis of the microstructure evolution of the molten pool during the solidification process is combined with the temperature field obtained from the simulation, and then the evolution of microstructure (β grains and phases) during the cooling process of the molten pool was also predicted. The accuracy of the analysis is verified by the experimental results.</p

Experimental and numerical investigation on ultimate strength of laser-welded stiffened plates considering welding deformation and residual stresses

The ultimate strength of laser-welded Al–Li alloy stiffened plates is experimentally and numerically analyzed in this study. The ultimate strength, failure mode, and strain evolution process of stiffened plates were measured through tension experiments combined with digital image correlation (DIC) and electrical measurements. Then, based on the established finite element model (FEM) considering material nonlinearity, geometry nonlinearity and ductile damage, the ultimate strength and failure mode of stiffened plates were simulated; and the influence of welding deformation and residual stress was investigated. The weld morphology, measured by metallographic experiment, validated the thermal–elastic–plastic model for predicting welding deformation and residual stresses. The results showed that the FEM considering welding deformation and residual stresses can accurately predict the failure modes and ultimate strength of stiffened plates.</p