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Quantitative polymerase chain reaction (qPCR) is a commonly used technique for plant disease diagnosis, which generally requires DNA extraction from the plant samples. The efficiency of DNA extraction has a significant influence on the accuracy of qPCR. Thus, including a DNA extraction efficiency control is important in qPCR analyses. To develop and evaluate a DNA extraction efficiency control, a Stagonospora nodorum (syn. Phaeosphaeria nodorum , synonym and correct taxonomic name: Parastagonospora nodorum) strain named HUA-22 was created by transferring a green florescent protein (GFP) gene into the genome of the S. nodorum strain Sn15. HUA-22 showed a similar pathogenicity as Sn15 but carried a strong GFP activity. A qPCR primers/probe set named P-GFP, targeting the GFP sequence, was designed. Using P-GFP, qPCR analysis was conducted on DNA extracted from replicated samples of HUA-22 conidia, and confirmed that HUA-22 could be used to characterize the variation in replicated DNA extractions. Conidia of HUA-22 were used to spike soil samples inoculated with the canola clubroot pathogen Plasmodiophora brassicae , canola stem samples infected with the blackleg pathogens Leptosphaeria biglobosa and/or L. maculans and wheat/barley samples infected with Xanthomonas translucens pv. translucens (Xtt) or X. translucens pv. undulosa (Xtu). Duplex qPCR using P-GFP and a primers/probe set specific to P. brassicae , triplex qPCR using P-GFP and primers/probe sets specific to L. biglobosa and L. maculans , and triplex qPCR using P-GFP and primers/probe sets specific to Xtt and Xtu were conducted. The results indicated that HUA-22 could be used as a standard for DNA extraction efficiency in qPCR-based plant disease diagnosis. Adding HUA-22 conidia to plant or soil samples prior to DNA extraction, and subsequent use of the P-GFP detection control, provided an added control that could distinguish truly negative from false-negative qPCR results. • A GFP -labeled S. nodorum strain named HUA-22 was created by transformation. • GFP in HUA-22 can be used as a DNA control in qPCR diagnosis of plant diseases. • HUA-22 was tested in the diagnosis of plant diseases caused by five pathogens. [ABSTRACT FROM AUTHOR]

This study adapted the Chinese version of the Couple Skills Training intervention program to couples of young breast cancer patients in China and investigated its effects on fear of cancer recurrence (FCR), cancer-related communication, and level of hope among the couples. Ninety young breast cancer patients and their spouses were recruited and randomly assigned to the intervention group (45 couples) and the control group (45 couples). Couples in the intervention group received skills training and were assessed at baseline, post-intervention, and 3 months post-intervention to measure outcomes. Differences in scores between the two groups were analyzed using two-sample t-tests and generalized estimating equations (GEE) controlling for demographic and health-related variables. Couples' skills training intervention effectively reduced FCR and improved cancer-related communication in young breast cancer patients compared to the control group (both p < 0.001). Spouses' expectations significantly increased (p < 0.001). At 3 months post-intervention, couples in the intervention group showed significant improvements in FCR, cancer-related communication, and hope (all p < 0.001). Couples' skills training interventions are beneficial for helping young breast cancer patients cope with FCR. Couples-based interventions play a crucial role in addressing FCR in these patients and their spouses. Future research should consider larger samples and longer follow-up periods to enhance intervention effectiveness. This study has also been registered with the Chinese Clinical Trial Registry (No: ChiCTR2200063327). • First culturally adapted couple-based skills training program for young Chinese breast cancer patients and spouses, using an RCT study design. •)Our study confirms the effectiveness of couple-based skills training in reducing FCR among young Chinese breast cancer patients. • The study highlights the role of couple-based skills training in supporting young breast cancer patients and underscores spousal unity in their recovery. • The study's implementation effectively improved outcomes for young Chinese breast cancer patients, offering practical support for their reintegration into families and society. • In China, culturally adapted couple-based training reduced cancer fear in young breast cancer patients, emphasizing effectiveness and spousal support, with plans for larger studies. [ABSTRACT FROM AUTHOR]

A commonly used protocol for DNA extraction from Plasmodiophora brassicae was modified by adding an alkaline treatment step to increase the purity of resting spores. The quality of DNA extracted by the modified protocol was improved due to the removal of DNA contamination from host plant cells and other microorganisms. [ABSTRACT FROM AUTHOR]

Abstract: Canola seedling blight, caused by Rhizoctonia solani, and Fusarium spp., can result in large yield losses to canola (Brassica napus) at high inoculum pressure. The effect of inoculum density was studied by mixing different amounts of R. solani AG-2-1 and Fusarium avenaceum into a sterilized natural soil and soil-less mix (2:1, v:v) separately, and recording seedling emergence, damping-off and seedling height within ten days after seeding; root rot severity at 12 days after seeding and seed yield at harvest on canola cultivars ‘45H29’ and ‘73-77RR’. Root rot severity increased and emergence, plant height and seed yield decreased with increased inoculum density of both R. solani and F. avenaceum. For quantification of R. solani AG-2-1, a primer and TaqMan probe set (Rs21F/Rs21R/Rs21P) was designed based on the nuclear ribosomal internal transcribed spacer (ITS) region of R. solani AG-2-1. From a conventional PCR amplification, an 88-bp product was amplified from all isolates classified as AG-2-1 with the primers Rs21F and Rs21R. No product was amplified with DNA from isolates belonging to other anastomosis groups of R. solani, other pathogens or the host plant. By using quantitative PCR, DNA amounts as low as 100 fg of R. solani AG-2-1 were detected. The quantity of DNA from soil samples with different inoculum densities estimated using qPCR was highly correlated to the number of colony-forming units (cfu) obtained from the same soil samples for both R. solani AG-2-1 and F. avenaceum. [Copyright &y& Elsevier]

An increase in temperature and change of correlated color temperature (CCT) would affect cognitive performance and human physiology. Moreover, the effect of CCT on thermal sensation at different temperatures is controversial and needs to be further explored. In order to study the effects of CCT variations on students' short-term memory performance and thermal sensation, 20 subjects were exposed to a "neutral temperature" (26 °C) and two "slightly warm temperature" (28 °C, 30 °C) in different periods. In each period, short-term memory tests were assessed under different CCT (3000 K, 4500 K, and 6000 K), and subsequently completed the questionnaire about their thermal sensation. Subjects' short-term memory performance was assessed using the N-back memory tool, the thermal sensation was evaluated using a seven-point scale while undergoing electrocardiogram (ECG) continuous recording by PowerLab. Spectral analysis of ECG provided indices of heart rate variability (HRV). The results indicate the short-term memory performance was not significantly affected by temperature and CCT. In addition, the results indicate the short-term memory performance was not significantly affected by temperature and CCT. The effect of CCT over thermal sensation was weaker compared with the temperature effect. There was a significant negative correlation between CCT and thermal sensation at 26 °C and 28 °C. When the temperature difference is 2 °C, CCT would change the dominance of temperature on thermal sensation. When the temperature remained the same and the CCT difference reached 3000 K, there was a significant difference in thermal sensation. Moreover, the effect of CCT over thermal sensation was consistent with "Hue-heat" hypothesis. HRV could be used to evaluate short-term memory performance and thermal sensation. • The short-term memory is not affected by temperature and CCT within acceptable range. • A significant negative correlation exists between CCT and thermal sensation. • CCT could dominate the thermal sensation within comfort range (26 °C–28 °C). • HRV could be used to evaluate short-term memory performance and thermal sensation. • The thermal environment is accepted when the HRV value is close to 1. [ABSTRACT FROM AUTHOR]

Building Information Modelling (BIM) is a promising technology for building lifecycle management. Currently, BIM has been extensively used in both the sketch design (sketch) and detailed design (design) stages. Due to different purposes and different BIM tools, the reusability of sketch BIM models in the design stage is still challenging. To address this issue, this study proposes a building e lement i dentification scheme using a s egmentation- a ggregation strategy, termed EI-SA. Since Industry Foundation Classes (IFC) is the international standard of BIM, the EI-SA is built according to the IFC specification. Firstly, the element identification Problem is formally defined, followed by the overall framework of the EI-SA. Then, a presentation-level BIM segmentation algorithm is developed to divide a sketch BIM model into slices of geometric representations. Thirdly, a representation aggregation scheme is proposed to solve the element identification problem. The representation aggregation scheme employs a triangle-triangle intersection to detect the geometric connection and an object recognition algorithm to identify elements. Finally, experiments were conducted on several sketch BIM models. The experiment results showed that the EI-SA identified 82.22% elements on average, which verified the effectiveness of the EI-SA. To the best of our knowledge, this study is the first to solve the building element identification problem according to IFC. The proposed EI-SA will bridge the gap between sketch BIM models and design BIM models, smooth the reusability of data from the sketch stage to downstream stages, and improve the design efficiency by avoiding the re-designing of building elements. [ABSTRACT FROM AUTHOR]

Accurately predicting the power demand of large-scale electric vehicles (EVs) is one of the key tasks of power grid operation optimization. However, this task is difficult to complete due to insufficient data and high randomness of power demand. To address this issue, this paper proposes a transfer learning based hybrid method for power demand prediction of large-scale EVs. Firstly, the linear trend of power demand is extracted by Multiple Linear Regression (MLR), and the nonlinear residual error is obtained by removing the trend from the original data. Secondly, the residual error is predicted via the bidirectional long short-term memory network (BiLSTM). Meanwhile, transfer learning is employed to improve the prediction accuracy of BiLSTM. The BiLSTM is pre-trained using the residual error data from building energy consumption and fine-tuned by the residual error data from EV power demand. Finally, the extracted trend of large-scale EVs' power demand and the predicted residual errors are combined to obtain the final predicted results. To validate the proposed method, the power demand of a real EV charging station is predicted using the proposed method, and fifteen models are taken for comparison (including the most popular data-driven models). The experimental result indicates that the proposed method can improve the predictive performance by at least 14.23% compared with the comparative models in terms of symmetric mean absolute percentage error, effectively avoids negative transfer results, and significantly enhance the confidence level. • Proposing a new hybrid architecture for predicting the large-scale EVs' power demand. • Constructing a new transfer learning strategy to improve the prediction accuracy. • The proposed hybrid model significantly improves prediction accuracy. [ABSTRACT FROM AUTHOR]

Timely and accurate detection of crop water stress is vital for precision irrigation. Whether the accuracy of the prevailing diagnosis of crop water stress using vegetation indices (VIs) and spectral reflectance can be improved still remains to be investigated. The crop surface characteristics such as grayscale or color vary under different water stress, so in this study one more variable, image texture, was utilized together to diagnose water stress. For this end, the canopy image of winter wheat in bloom was obtained by unmanned aerial vehicle (UAV) equipped with multispectral sensor, and the effect of soil background was eliminated using vegetation index threshold method. On this basis, Grey level co-occurrence matrix (GLCM) was used to calculate the mean (MEA), variance (VAR), homogeneity (HOM), contrast (CON), dissimilarity (DIS), entropy (ENT), second moment (SEC) and correlation (COR) of the image texture under different spatial resolutions (0.008 m, 0.01 m, 0.02 m, 0.05 m, 0.1 m and 0.2 m). Next, the canopy vegetation indices were obtained by mathematical transformation of canopy reflectance, and then sensitive image texture and vegetation indices by full subset regression method. Finally, Cubist, BPNN (Back Propagation Neural Network) and ELM (Extreme Learning Machine) methods were adopted to build the estimation models of the stomatal conductance (Gs) of winter wheat (between the sensitive image texture and Gs, and between vegetation index and Gs), and the water stress map was plotted based on the optimal Gs estimation model. The result showed: (i) the image texture obtained from the high-resolution multispectral image had a high correlation with Gs, and the image texture (VAR, HOM, CON, DIS, ENT and SEC) at 550 nm had the most significant correlation; (ii) the higher the ground resolution, the higher the correlation between the Gs and the image texture, the vegetation indices, respectively. The image texture with a ground resolution of 0.008 m combined with VIs and Gs had the highest correlation, and combining image texture and vegetation index can significantly improve the estimation accuracy of winter wheat Gs; (iii) Among the three estimation models, the BPNN model constructed by combining the image texture and VIs (MEA, VAR, ENT, DWSI and EXG) had the best estimation performance (Calibration: R c 2 = 0.899, RMSE c = 0.01, MAE c = 0.006; Validation: R c 2 = 0.834, RMSE v =;0.018, MAE v = 0.014), and an accurate estimation could even be achieved at a lower Gs value. Compared with the BPNN model solely based on VIs or image texture, the R c 2 of the BPNN model based on the combined variables increased by 24% and 22.48%, respectively. Therefore, combining UAV multispectral image texture and VIs to estimate Gs provides a feasible and accurate method for water stress diagnosis of winter wheat. • The incorporation of texture improved the accuracy of water stress diagnoses with VIs. • BPNN model with VIs and texture provided the highest accuracy of Gs estimation. • UAV provides a feasible and accurate method for water stress diagnosis of winter wheat. • Removing soil background and screening methods improve the quality of remote sensing data. [ABSTRACT FROM AUTHOR]