Correction of a CADASIL point mutation using adenine base editors in hiPsCsandbloodvessel organoids

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a monogenic small vessel disease caused by mutations in the NOTCH3 gene. However, the pathogenesis of CADASIL remains unclear, and patients have limited treatment options. Here, we use human induced pluripotent stem cells (hiPSCs) generated from the peripheral blood mononuclear cells of a patient with CADASIL carrying a heterozygous NOTCH3 mutation (c.1261C>T, p.R421C) to develop a disease model. The correction efficiency of different adenine base editors (ABEs) is tested using the HEK293T-NOTCH3 reporter cell line. ABEmax is selected based on its higher efficiency and minimization of predicted off-target effects. Vascular smooth muscle cells (VSMCs) differentiated from CADASIL hiPSCs show NOTCH3 deposition and abnormal actin cytoskeleton structure, and the abnormalities are recovered in corrected hiPSC-derived VSMCs. Furthermore, CADASIL blood vessel organoids generated for in vivo modeling show altered expression of genes related to disease phenotypes, including the downregulation of cell adhesion, extracellular matrix organization, and vessel development. The dual adeno-associated virus (AAV) split-ABEmax system is applied to the genome editing of vascular organoids with an average editing efficiency of 8.82%. Collectively, we present potential genetic therapeutic strategies for patients with CADASIL using blood vessel organoids and the dual AAV split-ABEmax system.

Feedback and contribution of vegetation, air temperature and precipitation to land surface temperature in the Yangtze River Basin considering statistical analysis

Land surface temperature(LST),especially day-night LST difference(LSTd-LSTn),is a key variable for the stability of terrestrial ecosystems,affected by vegetation and climate change.Quantifying the contribution and feedback of vegetation and climate to LST changes is critical to developing mitigation strategies.Based on LST,Normalized vegetation index(NDVI),land use(LU),air temperature(AT)and precipitation(Pre)from 2003 to 2021,partial correlation was used to analyze the response of LST to vegetation and climate.The feedback and contribution of both to LST were further quantifed by using spatial linear relationships and partial derivatives analysis.The results showed that both interannual LST(LSTy)and LSTd-LSTn responded negatively to vegetation,and vegetation had a negative feedback effect in areas with significantly altered.Vegetation was also a major contributor to the decline of LSTd-LSTn.With the advantage of positive partial correlation area of 94.99%,AT became the main driving factor and contributor to LSTy change trend.Pre contributed negatively to both LSTy and LSTd-LSTn,with contributions of-0.004℃/y and-0.022℃/y,respectively.AT played a decisive role in LST warming of YRB,which was partially mitigated by vegetation and Pre.The present research contributed'to,the,detection,of LST changes and improved understanding of the driving mechanism.

MDvT: introducing mobile three-dimensional convolution to a vision transformer for hyperspectral image classification

Hyperspectral images carry numerous spectral bands,and their wealth of band data is a valuable source of information for the accurate classification of ground objects.Three-dimensional(3D)convolution,although an excellent spectral information extraction method,is limited by its huge number of parameters and long model training time.To allow better integration of 3D convolution with the most popular transformer models currently available,a new architecture called mobile 3D convolutional vision transformer(MDvT)is proposed.The MDvT introduces inverted residual structure to reduce the number of model parameters and balance the data mining efficiency of low-dimensional data input.Simultaneously,a square patch is used to cut the sequence of tokens to accelerate the model operation.Through extensive experiments,we evaluated the classification overall performance of the proposed MDvT on the WHU-Hi and Pavia University datasets,and demonstrated significant improvements in classification accuracy and model runtime compared with classical deep learning models.It is worth noting that compared with directly integrating 3D convolution into the transformer model,the MDvT architecture improves the accuracy while reducing the time to train an epoch by approximately 58.54%.To facilitate the reproduction of the work in this paper,the model code is available at https://github.com/gloryofroad/MDvT.