Deblurring,artifact-free optical coherence tomography with deconvolution-random phase modulation

Deconvolution is a commonly employed technique for enhancing image quality in optical imaging methods.Unfortu-nately,its application in optical coherence tomography(OCT)is often hindered by sensitivity to noise,which leads to ad-ditive ringing artifacts.These artifacts considerably degrade the quality of deconvolved images,thereby limiting its effect-iveness in OCT imaging.In this study,we propose a framework that integrates numerical random phase masks into the deconvolution process,effectively eliminating these artifacts and enhancing image clarity.The optimized joint operation of an iterative Richardson-Lucy deconvolution and numerical synthesis of random phase masks(RPM),termed as De-conv-RPM,enables a 2.5-fold reduction in full width at half-maximum(FWHM).We demonstrate that the Deconv-RPM method significantly enhances image clarity,allowing for the discernment of previously unresolved cellular-level details in nonkeratinized epithelial cells ex vivo and moving blood cells in vivo.

Overexpression of ZxABCG11 from Zygophyllum xanthoxylum enhances tolerance to drought and heat in alfalfa by increasing cuticular wax deposition

Drought and heat stresses cause yield losses in alfalfa,a forage crop cultivated worldwide.Improving its drought and heat tolerance is desirable for maintaining alfalfa productivity in hot,arid regions.Cuticular wax forms a protective barrier on aerial surfaces of land plants against environmental stresses.ABCG11encodes an ATP binding cassette(ABC) transporter that functions in the cuticular wax transport pathway.In this study,Zx ABCG11 from the xerophyte Zygophyllum xanthoxylum was introduced into alfalfa by Agrobacterium tumefaciens-mediated transformation.Compared to the wild type(WT),transgenic alfalfa displayed faster growth,higher wax crystal density,and thicker cuticle on leaves under normal condition.Under either drought or heat treatment in greenhouse conditions,the plant height and shoot biomass of transgenic lines were significantly higher than those of the WT.Transgenic alfalfa showed excellent growth and 50% greater hay yield than WT under field conditions in a hot,arid region.Overexpression of Zx ABCG11 up-regulated wax-related genes and resulted in more cuticular wax deposition,which contributed to reduction of cuticle permeability and thus increased water retention and photosynthesis capacity of transgenic alfalfa.Thus,overexpression of Zx ABCG11 can simultaneously improve biomass yield,drought and heat tolerance in alfalfa by increasing cuticular wax deposition.Our study provides a promising avenue for developing novel forage cultivars suitable for planting in hot,arid,marginal lands.

Application of Big Data Deep Learning in Auxiliary Diagnosis of Lower Extremity Arteriosclerosis Obliterans

At present,the incidence rate of arteriosclerosis obliterans(LEASO)of the lower extremities is significantly increased by aging and lifestyle changes.It is of great importance to predict the LEASO effectively and accurately by analyzing the imaging data of the lower extremities⑴.At this stage,China has entered the era of big data and artificial intelligence.Medical institutions at all levels can produce a large number of lower limb vascular image data every day.Using big data deep learning technology to intelligently analyze a large number of image data,and then carry out auxiliary diagnosis,so as to improve the diagnosis and treatment effect of LEASO is the focus of clinical research.