Genome-wide analysis of HACD family genes and functional characterization of GhHACD2 for very long chain fatty acids biosynthesis in Gossypium hirsutum

Cotton (Gossypium spp.) not only serves as a primary textile fiber crop but also as a vital oilseed crop. It stands as the world’s fifth-largest oil crop and is rich in essential fatty acids. At present, the mechanisms underlying the biosynthesis of cottonseed oil have been extensively studied in cotton. 3-Hydroxyacyl-CoA dehydratase (HACD) is the third rate-limiting enzyme in the elongase complex, which plays a critical role in the biosynthesis of Very Long Chain Fatty Acids (VLCFA). However, the members of the HACD family and their roles in cottonseed oil remain uncharacterized in cotton. This study identified that G. arboreum and G. raimondii have two HACD genes, while four HACD genes exists in G. hirsutum, and G. barbadense. The phylogenetic relationships of the 12 HACD genes from the four cotton species further divided them into two subfamilies. Gene structure and conserved motif analysis revealed that members of the HACD family were relatively conserved during the evolution of cotton, but members within the same subfamily exhibited more similar structures. Homology and collinearity analysis suggest whole-genome duplication/segmental duplication may be a key factor in the amplification of the cotton HACD gene family. The qRT-PCR analysis of high-oil and low-oil genotype found significant differences in the expression levels of GhHACD1-4, which indicates GhHACD1-4 is expected to participate in the lipid oil biosynthesis process. Subcellular localization experiments confirmed the presence of the GhHACD2 inendoplasmic reticulum. The KEGG pathway enrichment analysis of co-expressed genes of GhHACD1 and GhHACD2 genes were conducted to confirm their potential involvement in fatty acid elongation and oil biosynthesis. Furthermore, transgenic overexpression analysis of GhHACD2 caused a 5.02% decrease in oil content compared with the control in yeast, while the levels of C28:0, C30:0, and VLCFAs were significantly improved. This study characterizes HACD gene family members in cotton and provides rich genetic resources for increasing cottonseed oil content and improving the nutritional value of cottonseed oil

2D Beam Profile Monitors at CPHS of Tsinghua University

International audienceBeam profile is a key parameter for high current proton linac. Compact Pulsed Hadron Source(CPHS) has two type of detectors to monitor beam 2D beam profile: scintillator screen and rotatable multi-wire scanner. A retractable chromium-doped alumina (Chromox) screen is used as scintillator, emitted lights when impacted by proton are captured by a 12 bit CCD camera. Nineteen carbon fibre wires with a diameter of 30 'm, 3 mm separated from each other, are used to measure beam 1D distribution. Projection can be measured at different direction by rotating the multi-wire scanner about beam direction. 2D beam distribution is reconstructed from multiple projections with the help of CT. Different CT algorithms, Algebra Reconstruct Technique (ART) and Maximum Entropy algorithm (MENT), are applied to achieve accurate or quick reconstruction. The preliminary experimental results show the two profile monitors working consistently with each other

Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from Chinese Association of Radiologists

In recent years, with the development of artificial intelligence, especially deep learning technology, researches on automatic detection of cerebrovascular diseases on medical images have made tremendous progress and these models are gradually entering into clinical practice. However, because of the complexity and flexibility of the deep learning algorithms, these researches have great variability on model building, validation process, performance description and results interpretation. The lack of a reliable, consistent, standardized design protocol has, to a certain extent, affected the progress of clinical translation and technology development of computer aided detection systems. After reviewing a large number of literatures and extensive discussion with domestic experts, this position paper put forward recommendations of standardized design on the key steps of deep learning-based automatic image detection models for cerebrovascular diseases. With further research and application expansion, this position paper would continue to be updated and gradually extended to evaluate the generalizability and clinical application efficacy of such tools