Validation of Reference Genes for RT-qPCR Studies of Gene Expression in Preharvest and Postharvest Longan Fruits under Different Experimental Conditions

Reverse transcription quantitative PCR (RT-qPCR), a sensitive technique for quantifying gene expression, relies on stable reference gene(s) for data normalization. Although a few studies have been conducted on reference gene validation in fruit trees, none have been done on preharvest and postharvest longan fruits. In this study, 12 candidate reference genes, namely, CYP, RPL, GAPDH, TUA, TUB, Fe-SOD, Mn-SOD, Cu/Zn-SOD, 18SrRNA, Actin, Histone H3 and EF-1a, were selected. Expression stability of these genes in 150 longan samples was evaluated and analyzed using geNorm and NormFinder algorithms. Preharvest samples consisted of seven experimental sets, including different developmental stages, organs, hormone stimuli (NAA, 2,4-D and ethephon) and abiotic stresses (bagging and girdling with defoliation). Postharvest samples consisted of different temperature treatments (4 and 22 °C) and varieties. Our findings indicate that appropriate reference gene(s) should be picked for each experimental condition. Our data further showed that the commonly used reference gene Actin does not exhibit stable expression across experimental conditions in longan. Expression levels of the DlACO gene, which is a key gene involved in regulating fruit abscission under girdling with defoliation treatment, was evaluated to validate our findings. In conclusion, our data provide a useful framework for choice of suitable reference genes across different experimental conditions for RT-qPCR analysis of preharvest and postharvest longan fruits

A temperature gradient based Condition Estimation Method for IGBT Module

The paper presents a temperature gradient based method for device state evaluation, taking the insulated Gated Bipolar Transistor (IGBT) modules as an example investigation. Firstly, theoretical basis of this method is presented and the results from example calculation on temperature gradient indicate that the increased thermal resistance and power loss of IGBT modules would increase the temperature gradient. Then an electrical-thermal- mechanical finite element method (FEM) model of IGBT modules, which takes the material temperature-dependent characteristic into account, is utilized to estimate the temperature gradient distribution for both healthy and fatigue conditions. It is found that the temperature gradient varies with power loss. Furthermore, both the experimental and simulation investigation on the temperature gradient for different conditions were conducted, and it is concluded that the temperature gradient can not only track the change of power loss, but have a better sensitivity compared with temperature distribution. In addition, the temperature gradient can reflect the defects location and distinguish failures degree. In the end the influence on the temperature gradient distribution caused by solder fatigue, void and delamination are discussed

Digital village construction, human capital and the development of the rural older adult care service industry

BackgroundThe advancement of digital villages in China is shaped by the degree of human capital within the rural labor force, which not only restricts the potential of digital village but also influences the impact of digital empowerment on the progression of the rural older adult care service industry.Materials and methodsUsing panel data from 30 Chinese provinces between 2011 and 2020, we created benchmark and threshold regression models to investigate the influence of digital village construction on the development of the rural older adult care service industry and to delineate the threshold effects of human capital on it. We further scrutinized the correlation between the two systems, along with the factors that affect it, through a coupling coordination model.ResultsPreliminary, the baseline regression outcomes show that the digital village construction is conducive to the progression of the rural older adult care service industry (p < 0.05). Moreover, we identified a significant nonlinear threshold relationship between the digital village, human capital, and the advancement of the rural older adult care service industry (HUM1ST, p < 0.05; HUM2DT, p < 0.01; HUM3DT, p < 0.01). These results indicate that the digital technology’s effect on the development of the rural older adult care service industry is limited by the rural human capital level. Lastly, we found that higher levels of human capital enhance the coupling of the digital village with the rural older adult care service industry (p < 0.01), with the influence of per capita education level being the most pronounced (CoefHUM1 > CoefHUM2 > CoefHUM3).ConclusionThe digital village substantially empowers the rural older adult care service industry, with human capital exhibiting a significant threshold effect on this empowerment. Furthermore, variances in the level of human capital have a considerable impact on the integration of the digital village and the rural older adult care service industry

Shoot/Root Interactions Affect Soybean Photosynthetic Traits and Yield Formation: A Case Study of Grafting With Record-Yield Cultivars

Improvement of soybean [Glycine max (L.) Merr.] yield and photosynthesis physiology have been achieved over decades of cultivar breeding. Identification of the mechanisms involved in shoot-root interactions would be beneficial for the development of yield improvement breeding strategies. The objectives of this study were to investigate soybean shoot-root interactions with different-year released soybean cultivars and to evaluate their effects on grain yield and yield components. Soybean grafts used in this study were constructed with two record-yield cultivars Liaodou14 (L14) and Zhonghuang35 (Z35) and eleven cultivars released in 1966–2006 from the United States and Chinese. The grafting experiments were conducted as pot-culture experiments and repeated in 2014 and 2015. Our results showed that net photosynthesis rate (PN) was positively correlated to both root activity and root bleeding sap mass (RBSM) during the R6 reproductive stage. Moreover, different year-released soybean shoots had all exhibited capabilities of changing the root activity and architecture of L14 and Z35 rootstocks to “generation”-specific patterns during all reproductive stages. However, these influences were independent of the photosynthetic strength. Yield analysis had demonstrated that high-yielding root systems (L14 and Z35 rootstocks) could cause more than 15% of yield increase in seven out of eleven common scions in a scion-genotype-dependent manner. For Williams-descendant cultivar scions, L14 and Z35 rootstocks promoted yields mainly by increasing the seed number (SN), but those scions of Amsoy-descendent cultivars showed mainly seed weight (SW) increases when grafted onto L14 and Z35 rootstocks. On the other hand, although most tested common rootstocks did not show significant influence over the final yields in record-yield L14 and Z35 scions, they were obviously capable of shifting the formation of yield components when compared to L14 and Z35 self-grafting controls. Taken together, soybean shoots could influence the root physiology and played a crucial role in the determination of yield potentials. Synergistically with shoots, soybean roots played a more supportive role during the realization of yield potentials through root activities and by balancing the formation of yield components. These findings provided interesting insightful information for developing new breeding strategies which aim to pyramid elite physiological and yield traits by selecting specific parental combinations

Enabling high reliability power modules : a multidisciplinary task

Reliability of power electronic systems is a major concern for application engineers in the automotive and power system sectors. Power electronic modules are one of the main sources of failure in wind energy conversion systems. Power electronic converters used in wind turbine electric drive trains, railway traction, more-electric-aircrafts, marine propulsion and grid connected systems like FACTS/HVDC require reliable power devices and modules. Wide bandgap semiconductors like SiC have demonstrated enlarged electrothermal Safe-Operating-Areas compared with silicon devices. However, the reliability of SiC power modules and packages has been identified as an area of potential weakness. Traditional packaging systems have been developed for Si hence the different thermomechanical properties of SiC cause different stresses in the packaging thereby potentially causing reduced reliability. This paper identifies some of the key areas for the development of reliable power electronic systems using SiC. The focus is on condition monitoring, packaging system innovation and thermomechanical stress analysis as a function of the mechanical properties of Si and SiC. Power cycling experiments and finite element models have been used to support the analysis

Genome-wide association mapping for yield-related traits in soybean (Glycine max) under well-watered and drought-stressed conditions

Soybean (Glycine max) productivity is significantly reduced by drought stress. Breeders are aiming to improve soybean grain yields both under well-watered (WW) and drought-stressed (DS) conditions, however, little is known about the genetic architecture of yield-related traits. Here, a panel of 188 soybean germplasm was used in a genome wide association study (GWAS) to identify single nucleotide polymorphism (SNP) markers linked to yield-related traits including pod number per plant (PN), biomass per plant (BM) and seed weight per plant (SW). The SLAF-seq genotyping was conducted on the population and three phenotype traits were examined in WW and DS conditions in four environments. Based on best linear unbiased prediction (BLUP) data and individual environmental analyses, 39 SNPs were significantly associated with three soybean traits under two conditions, which were tagged to 26 genomic regions by linkage disequilibrium (LD) analysis. Of these, six QTLs qPN-WW19.1, qPN-DS8.8, qBM-WW1, qBM-DS17.4, qSW-WW4 and qSW-DS8 were identified controlling PN, BM and SW of soybean. There were larger proportions of favorable haplotypes for locus qPN-WW19.1 and qSW-WW4 rather than qBM-WW1, qBM-DS17.4, qPN-DS8.8 and qSW-DS8 in both landraces and improved cultivars. In addition, several putative candidate genes such as Glyma.19G211300, Glyma.17G057100 and Glyma.04G124800, encoding E3 ubiquitin-protein ligase BAH1, WRKY transcription factor 11 and protein zinc induced facilitator-like 1, respectively, were predicted. We propose that the further exploration of these locus will facilitate accelerating breeding for high-yield soybean cultivars

A leaf vein-like hierarchical silver grids transparent electrode towards high-performance flexible electrochromic smart windows

Abstract(#br)As essential components of numerous flexible and wearable optoelectronic devices, the flexible transparent conducting electrodes (TCEs) with sufficient optical transmittance and electric conductivity become more and more important. In this work, we fabricated a large-area flexible TCE based on leaf vein-like hierarchical metal grids (HMG) comprising of mesoscale “trunk” and microscale “branches”. The self-formed branched grids made the conducting paths distributing uniformly while the laser-etching trunk grids enabled to transport the collected electrons across long-distance. The Ag HMG exhibited high optical transmittance (~81%) with low sheet resistance (1.36 Ω sq –1 ), which could be simply optimized through adjusting the grids’ widths, spaces, and the sizes of the TiO 2 colloidal crackle patterns. In addition, on the basis of such advanced HMG electrode, flexible electrochromic devices (ECDs) with remarkable cyclic performance were fabricated. The HMG with high transparency, conductivity, and flexibility provides a promising TCE for the next-generation flexible and wearable optoelectronic devices

Screening biomarkers for Sjogren’s Syndrome by computer analysis and evaluating the expression correlations with the levels of immune cells

BackgroundSjögren’s syndrome (SS) is a systemic autoimmune disease that affects about 0.04-0.1% of the general population. SS diagnosis depends on symptoms, clinical signs, autoimmune serology, and even invasive histopathological examination. This study explored biomarkers for SS diagnosis.MethodsWe downloaded three datasets of SS patients’ and healthy pepole’s whole blood (GSE51092, GSE66795, and GSE140161) from the Gene Expression Omnibus (GEO) database. We used machine learning algorithm to mine possible diagnostic biomarkers for SS patients. Additionally, we assessed the biomarkers’ diagnostic value using the receiver operating characteristic (ROC) curve. Moreover, we confirmed the expression of the biomarkers through the reverse transcription quantitative polymerase chain reaction (RT-qPCR) using our own Chinese cohort. Eventually, the proportions of 22 immune cells in SS patients were calculated by CIBERSORT, and connections between the expression of the biomarkers and immune cell ratios were studied.ResultsWe obtained 43 DEGs that were mainly involved in immune-related pathways. Next, 11 candidate biomarkers were selected and validated by the validation cohort data set. Besides, the area under curves (AUC) of XAF1, STAT1, IFI27, HES4, TTC21A, and OTOF in the discovery and validation datasets were 0.903 and 0.877, respectively. Subsequently, eight genes, including HES4, IFI27, LY6E, OTOF, STAT1, TTC21A, XAF1, and ZCCHC2, were selected as prospective biomarkers and verified by RT-qPCR. Finally, we revealed the most relevant immune cells with the expression of HES4, IFI27, LY6E, OTOF, TTC21A, XAF1, and ZCCHC2.ConclusionIn this paper, we identified seven key biomarkers that have potential value for diagnosing Chinese SS patients

Comprehensive bulk and single-cell transcriptome profiling give useful insights into the characteristics of osteoarthritis associated synovial macrophages

BackgroundOsteoarthritis (OA) is a common chronic joint disease, but the association between molecular and cellular events and the pathogenic process of OA remains unclear.ObjectiveThe study aimed to identify key molecular and cellular events in the processes of immune infiltration of the synovium in OA and to provide potential diagnostic and therapeutic targets.MethodsTo identify the common differential expression genes and function analysis in OA, we compared the expression between normal and OA samples and analyzed the protein–protein interaction (PPI). Additionally, immune infiltration analysis was used to explore the differences in common immune cell types, and Gene Set Variation Analysis (GSVA) analysis was applied to analyze the status of pathways between OA and normal groups. Furthermore, the optimal diagnostic biomarkers for OA were identified by least absolute shrinkage and selection operator (LASSO) models. Finally, the key role of biomarkers in OA synovitis microenvironment was discussed through single cell and Scissor analysis.ResultsA total of 172 DEGs (differentially expressed genes) associated with osteoarticular synovitis were identified, and these genes mainly enriched eight functional categories. In addition, immune infiltration analysis found that four immune cell types, including Macrophage, B cell memory, B cell, and Mast cell were significantly correlated with OA, and LASSO analysis showed that Macrophage were the best diagnostic biomarkers of immune infiltration in OA. Furthermore, using scRNA-seq dataset, we also analyzed the cell communication patterns of Macrophage in the OA synovial inflammatory microenvironment and found that CCL, MIF, and TNF signaling pathways were the mainly cellular communication pathways. Finally, Scissor analysis identified a population of M2-like Macrophages with high expression of CD163 and LYVE1, which has strong anti-inflammatory ability and showed that the TNF gene may play an important role in the synovial microenvironment of OA.ConclusionOverall, Macrophage is the best diagnostic marker of immune infiltration in osteoarticular synovitis, and it can communicate with other cells mainly through CCL, TNF, and MIF signaling pathways in microenvironment. In addition, TNF gene may play an important role in the development of synovitis

Quantitative prediction of fluvial sandbodies by combining seismic attributes of neighboring zones

The geological and geophysical characterization of hydrocarbon-bearing sandstones of fluvial origin is a challenging task. Channel sandbodies occurring at different stratigraphic levels (i.e., in a reservoir interval of interest as well as in overlying and underlying stratigraphic intervals) but overlapping in planview usually cause significant seismic interference due to limitations in seismic resolution: this can produce significant error in the prediction of sand location and thickness using seismic attributes. To mitigate the effect of seismic interferences by zones neighboring a target reservoir interval, a new method is proposed that combines multiple seismic attributes of the target interval and of its interfering neighboring zones, implemented by a supervised machine learning algorithm using support vector regression (SVR). Since the thickness of neighboring intervals causing seismic interference has a constant value of a quarter of a wavelength (1/4 λ), the stratal slice corresponding with the top horizon of the target interval is taken as the base of a window of 1/4 λ to calculate seismic attributes for the overlying zone; similarly, the stratal slice corresponding with the bottom horizon is taken as the top of a window of 1/4 λ to calculate seismic attributes for the underlying zone. The proposed method was applied to a subsurface dataset (including a 3D seismic dataset and 255 wells) of the Chengdao oilfield, in the Bohai Bay Basin (China). The interval of interest is located in the Neogene Guantao Formation, whose successions are interpreted as fluvial in origin. This application demonstrates how the proposed method results in remarkably improved sandstone thickness prediction, and how consideration of multiple attributes further improves the accuracy of predicted values of sandstone thickness