Water temperature significantly affects the physiological balance of aquatic organisms like crustaceans, and heat shock proteins (HSPs) are crucial for stress resistance and pathogen defense. This study conducted a genome-wide analysis to explore the functional characteristics of the Hsp70 gene family in Procambarus clarkii. Fifteen Hsp70 family members were identified, with several genes showing upregulation under non-lethal heat shock (NLHS) and pathogen challenges. RNA-Seq and qPCR analyses confirmed increased expression of certain PcHsp70s during NLHS, indicating NLHS activation of the Hsp70 family to enhance immune regulation. dsRNA-mediated silencing of Hsp70 led to downregulation of TLR pathway genes (e.g., TLR1, TLR6), suggesting Hsp70 regulates the TLR signaling pathway for immune responses. These findings reveal that NLHS-induced Hsp70 upregulation improves pathogen resistance, offering insights for addressing temperature fluctuations and disease outbreaks in aquaculture to optimize management practices.

In present paper, to effectively improve the accuracy of burnt scar mapping in hilly areas, a new method by combing multiple spectral indices with Support Vector Machines (SVM) has been put forward. Firstly, the Landsat TM image was geometrically and atmospherically corrected. Secondly, two widely used thematic-oriented spectral indices, namely, the vegetation index SAVI and the water index MNDWI were derived. Thirdly, to minimize the confusion between burned areas and low reflectance objects (e.g., water and shadows) before next step, by adopting a looser criterion, the above mentioned indices were used to mask out as much as possible water and unburned vegetated pixels. Finally, images of four widely and successfully used vegetation indices specially designed for burnt area mapping, including BAI, VI3, NBR, and GEMI, together with SAVI and MNDWI, were gathered and stacked as input, and the SVM was applied to extract burnt scars. The resultant image was evaluated and a sound performance was achieved, with an overall accuracy of 90.1% and a Kappa coefficient of 0.8034.