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Abstract Pear ( Pyrus spp.) is one of the most consumed fruits in China, but the pear production has to confront the growing threat from fatal diseases. In this study, we report two incidences of stem canker and twig dieback disease on pear plants, which led to death of pear seedlings (approximately 10% of total plants) in Guangxi and Jiangsu provinces. Using a combination of morphological and molecular diagnoses, along with pathogenicity test, the causal agent of the disease in these two locations was identified to be the fungus Neofusicoccum parvum . However, the isolates were divided into two clades: CY-2 isolate and other four isolates including ZL-4, BM-9, BM-10 and BM-12 might split into two groups of N. parvum . Two representative isolates (CY-2 and ZL-4) were selected for further investigation. We observed that the optimal temperature for in vitro infection on pear trees of these two isolates was at round 25 °C. Both CY-2 and ZL-4 could infect different sand pear varieties and other horticultural plants in vitro, while CY-2 had a higher virulence on several pear varieties including Nanyue, Lvyun, Qiushui and Ningmenghuang . Furthermore, the efficacy of fungicides against these two isolates was evaluated, and carbendazim and flusilazole were found to be the most effective fungicides in inhibiting the growth of these fungal pathogens. Taken together, these findings redefine the N. parvum species and provide potential strategies for the future management of this disease. 

Automated experimentation has yielded data acquisition rates that supersede human processing capabilities. Artificial Intelligence offers new possibilities for automating data interpretation to generate large, high-quality datasets. Background subtraction is a long-standing challenge, particularly in settings where multiple sources of the background signal coexist, and automatic extraction of signals of interest from measured signals accelerates data interpretation. Herein, we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest. The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals. While the model can incorporate prior knowledge, it does not require knowledge of the signals since the shapes of the background signals, the noise levels, and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework. Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets, a transformative capability with many applications in the physical sciences and beyond.

 

Photonic crystals (PCs) constructed from colloidal building blocks have attracted increasing attention because their brilliant structural colors may find broad applications in paints, sensors, displays, and security devices. However, producing high‐quality structural colors on flexible substrates such as textiles in an efficient and scalable manner remains a challenge. Here a robust and ultrafast approach to produce industrial‐scale colloidal PCs by the shear‐induced assembly of liquid colloidal crystals of polystyrene beads pre‐formed spontaneously over a critical volume fraction is demonstrated. The pre‐crystallization of colloidal crystals allows their efficient assembly into large‐scale PCs on flexible fabric substrates under shear force. Further, by programming the wettability of the fabric substrate with hydrophilic–hydrophobic regions, this shear‐based assembly strategy can conveniently generate pre‐designed patterns of complex structural colors. This assembly strategy brings structural coloration to flexible fabrics at a scale suitable for commercial applications; therefore, it holds the potential to revolutionize the coloration technology in the textile industry.