Amino Acid Changes during Energy Storage Compounds Accumulation of Microalgae under the Nitrogen Depletion

The nitrogen depletion stress is widely used to promote energy storage compound (ESC) production of microalgae, such as starch and lipids. Our cultivation results and most reports show that during the nitrogen depletion, the fast ESC’s accumulation happens around the overall nitrogen content lowered to the half of normal cells. It indicates that the cells may take an active nitrogen reassembly to rebalance the requirement of nitrogen, in which the amino acid conversion should play an important role. So here, using a marine strain, Isochrysis zhanjiangensis, as the model to give a detail view on metabolic, transcriptomic and proteomic levels within the “golden period” of ESC’s accumulation. To monitor the metabolic transition in response to nitrogen starvation, the intracellular metabolite fluctuation within 32 h was profiled by GC-MS and LC-MS scanned in selected ion monitoring mode for the first time. These techniques identified and quantified the levels of 14 SMAs, 2 carbohydrates involved in the TCA cycle and glycolysis, and 28 free amino acids (AAs). The pulsed increase of pyruvate, which is the precursor of acetyl-CoA and fatty acids (FAs), indicated a potential to produce more FAs. Although overall AAs showed a decreasing trend under the experimental conditions, Ala and Phe showed increased levels initially. Meanwhile, the transcriptomic and proteomic studies were utilized, and the nitrogen metabolic pathways were studied in this ESC’s fast accumulation period. It is found that gamma-aminobutyric acid (GABA) and other non-protein AAs also play important roles in the regulation of energy metabolism

Building extraction from high-resolution aerial imagery using a generative adversarial network with spatial and channel attention mechanisms.

Segmentation of high-resolution remote sensing images is an important challenge with wide practical applications. The increasing spatial resolution provides fine details for image segmentation but also incurs segmentation ambiguities. In this paper, we propose a generative adversarial network with spatial and channel attention mechanisms (GAN-SCA) for the robust segmentation of buildings in remote sensing images. The segmentation network (generator) of the proposed framework is composed of the well-known semantic segmentation architecture (U-Net) and the spatial and channel attention mechanisms (SCA). The adoption of SCA enables the segmentation network to selectively enhance more useful features in specific positions and channels and enables improved results closer to the ground truth. The discriminator is an adversarial network with channel attention mechanisms that can properly discriminate the outputs of the generator and the ground truth maps. The segmentation network and adversarial network are trained in an alternating fashion on the Inria aerial image labeling dataset and Massachusetts buildings dataset. Experimental results show that the proposed GAN-SCA achieves a higher score (the overall accuracy and intersection over the union of Inria aerial image labeling dataset are 96.61% and 77.75%, respectively, and the F1-measure of the Massachusetts buildings dataset is 96.36%) and outperforms several state-of-the-art approaches

Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data.

Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provides a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases

High-Resolution Aerial Imagery Semantic Labeling with Dense Pyramid Network

Semantic segmentation of high-resolution aerial images is of great importance in certain fields, but the increasing spatial resolution brings large intra-class variance and small inter-class differences that can lead to classification ambiguities. Based on high-level contextual features, the deep convolutional neural network (DCNN) is an effective method to deal with semantic segmentation of high-resolution aerial imagery. In this work, a novel dense pyramid network (DPN) is proposed for semantic segmentation. The network starts with group convolutions to deal with multi-sensor data in channel wise to extract feature maps of each channel separately; by doing so, more information from each channel can be preserved. This process is followed by the channel shuffle operation to enhance the representation ability of the network. Then, four densely connected convolutional blocks are utilized to both extract and take full advantage of features. The pyramid pooling module combined with two convolutional layers are set to fuse multi-resolution and multi-sensor features through an effective global scenery prior manner, producing the probability graph for each class. Moreover, the median frequency balanced focal loss is proposed to replace the standard cross entropy loss in the training phase to deal with the class imbalance problem. We evaluate the dense pyramid network on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam 2D semantic labeling dataset, and the results demonstrate that the proposed framework exhibits better performances, compared to the state of the art baseline

The Light Regime Effect on Triacylglycerol Accumulation of Isochrysis zhangjiangensis

Stress state of microalgal cells is caused under unfavorable conditions such as disordered light regime and depleted nitrogen. The stress state can impair photosynthetic efficiency, inhibit cell growth and result in the accumulation of triacylglycerol (TAG) from protective mechanisms. Continuous light or nitrogen starvation was applied on microalgae and performed effectively on inducing TAG production. To evaluate the light regime effect on inducing TAG production, the effect of different light regimes on nitrogen-starved Isochrysis zhangjiangensis was investigated in this work. The continuous light and nitrogen starvation elevated TAG content of biomass by 73% and 193%, respectively. Furthermore, the TAG accumulation of I. zhangjiangensis cell under nitrogen starvation decreased under aggravated stress from continuous illumination. Our results demonstrated that culturing the cells with 14L: 10D light regime under nitrogen starvation is the optimal mode to achieve maximal accumulation of TAG. A recovery in light regime was necessary for I. zhangjiangensis cultivation

thelightregimeeffectontriacylglycerolaccumulationofisochrysiszhangjiangensis

Stress state of microalgal cells is caused under unfavorable conditions such as disordered light regime and depleted nitrogen.The stress state can impair photosynthetic efficiency,inhibit cell growth and result in the accumulation of triacylglycerol(TAG)from protective mechanisms.Continuous light or nitrogen starvation was applied on microalgae and performed effectively on inducing TAG production.To evaluate the light regime effect on inducing TAG production,the effect of different light regimes on nitrogen-starved Isochrysis zhangjiangensis was investigated in this work.The continuous light and nitrogen starvation elevated TAG content of biomass by 73%and 193%,respectively.Furthermore,the TAG accumulation of I.zhangjiangensis cell under nitrogen starvation decreased under aggravated stress from continuous illumination.Our results demonstrated that culturing the cells with 14L:10D light regime under nitrogen starvation is the optimal mode to achieve maximal accumulation of TAG.A recovery in light regime was necessary for I.zhangjiangensis cultivation

Semantic labeling of high resolution aerial imagery and LiDAR data with fine segmentation network

In this paper, a novel convolutional neural network (CNN)-based architecture, named fine segmentation network (FSN), is proposed for semantic segmentation of high resolution aerial images and light detection and ranging (LiDAR) data. The proposed architecture follows the encoder–decoder paradigm and the multi-sensor fusion is accomplished in the feature-level using multi-layer perceptron (MLP). The encoder consists of two parts: the main encoder based on the convolutional layers of Vgg-16 network for color-infrared images and a lightweight branch for LiDAR data. In the decoder stage, to adaptively upscale the coarse outputs from encoder, the Sub-Pixel convolution layers replace the transposed convolutional layers or other common up-sampling layers. Based on this design, the features from different stages and sensors are integrated for a MLP-based high-level learning. In the training phase, transfer learning is employed to infer the features learned from generic dataset to remote sensing data. The proposed FSN is evaluated by using the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam and Vaihingen 2D Semantic Labeling datasets. Experimental results demonstrate that the proposed framework can bring considerable improvement to other related networks

thelightregimeeffectontriacylglycerolaccumulationofisochrysiszhangjiangensis

Stress state of microalgal cells is caused under unfavorable conditions such as disordered light regime and depleted nitrogen.The stress state can impair photosynthetic efficiency,inhibit cell growth and result in the accumulation of triacylglycerol(TAG)from protective mechanisms.Continuous light or nitrogen starvation was applied on microalgae and performed effectively on inducing TAG production.To evaluate the light regime effect on inducing TAG production,the effect of different light regimes on nitrogen-starved Isochrysis zhangjiangensis was investigated in this work.The continuous light and nitrogen starvation elevated TAG content of biomass by 73%and 193%,respectively.Furthermore,the TAG accumulation of I.zhangjiangensis cell under nitrogen starvation decreased under aggravated stress from continuous illumination.Our results demonstrated that culturing the cells with 14L:10D light regime under nitrogen starvation is the optimal mode to achieve maximal accumulation of TAG.A recovery in light regime was necessary for I.zhangjiangensis cultivation

The synchronous TAG production with the growth by the expression of chloroplast transit peptide-fused ScPDAT in Chlamydomonas reinhardtii

Abstract Background The synchronous triacylglycerol (TAG) production with the growth is a key step to lower the cost of the microalgae-based biofuel production. Phospholipid: diacylglycerol acyltransferase (PDAT) has been identified recently and catalyzes the phospholipid contributing acyl group to diacylglycerol to synthesize TAG, and is considered as the important source of TAG in Chlamydomonas reinhardtii. Results Using a chimeric Hsp70A–RbcS2 promoter, exogenous PDAT form Saccharomyces cerevisiae fused with a chloroplast transit peptide was expressed in C. reinhardtii CC-137. Proved by western blot, the expression of ScPDAT showed a synchronous trend to the growth in the exponential phase. Compared to the wild type, the strain of Scpdat achieved 22% increase in the content of total fatty acids and 32% increase in TAG content. In addition, the fluctuation of C16 series fatty acid in monogalactosyldiacylglycerol, diacylglyceryltrimethylhomoserine and TAG indicated an enhancement in the TAG accumulation pathway. Conclusion The TAG production was enhanced in the regular cultivation without the nutrient stress by strengthening the conversion of polar lipid to TAG in C. reinhardtii and the findings provide a candidate strategy for rational engineered strain to overcome the decline in the growth during the TAG accumulation triggered by nitrogen starvation