Loss of the ceramide synthase HYL-2 from Caenorhabditis elegans impairs stress responses and alters sphingolipid composition.

Sphingolipids, essential membrane components and signaling molecules in cells, have ceramides at the core of their metabolic pathways. Initially termed as "longevity assurance genes", the encoding genes of ceramide synthases are closely associated with individual aging and stress responses, although the mechanisms remain unclear. This study aims to explore the alterations and underlying mechanisms of three ceramide synthases, HYL-1, HYL-2, and LAGR-1, in the aging and stress responses of Caenorhabditis elegans. Our results showed the knockdown of HYL-1 extends the lifespan and enhance stress resistance in worms, whereas the loss of HYL-2 function significantly impairs tolerances to heat, oxidation, and ultraviolet stress. Stress intolerance induced by HYL-2 deficiency may result from intracellular mitochondrial dysfunction, accumulation of reactive oxygen species, and abnormal nuclear translocation of DAF-16 under stress conditions. Loss of HYL-2 led to a significant reduction of predominant ceramides (d17:1/C20∼C23) as well as corresponding complex sphingolipids. Furthermore, the N-acyl chain length composition of sphingolipids underwent dramatic modifications, characterized by a decrease in C22 sphingolipids and an increase in C24 sphingolipids. Extra d18:1-ceramides resulted in diminished stress resilience in wild-type worms, while supplementation of d18:1/C16 ceramide to HYL-2-deficient worms marginally improved stress tolerance to heat and oxidation. These findings indicate the importance of appropriate ceramide content and composition in maintaining subcellular homeostasis and nuclear-cytoplasmic signal transduction during healthy aging and stress responses.

Rethinking the neighborhood information for deep learning-based optical coherence tomography angiography.

PURPOSE: Optical coherence tomography angiography (OCTA) is a premium imaging modality for noninvasive microvasculature studies. Deep learning networks have achieved promising results in the OCTA reconstruction task, benefiting from their powerful modeling capability. However, two limitations exist in the current deep learning-based OCTA reconstruction methods: (a) the angiogram information extraction is only limited to the locally consecutive B-scans; and (b) all reconstruction models are confined to the 2D convolutional network architectures, lacking effective temporal modeling. As a result, the valuable neighborhood information and inherent temporal characteristics of OCTA are not fully utilized. In this paper, we designed a neighborhood information-fused Pseudo-3D U-Net (NI-P3D-U) for OCTA reconstruction. METHODS: The proposed NI-P3D-U was investigated on an in vivo animal dataset by a cross-validation strategy under both fully supervised learning and weakly supervised learning pipelines. To demonstrate the OCTA reconstruction capability of the proposed NI-P3D-U, we compared it with several state-of-the-art methods. RESULTS: The results showed that the proposed network outperformed the state-of-the-art deep learning-based OCTA algorithms in terms of visual quality and quantitative metrics, and demonstrated an effective generalization for different training strategies (fully supervised and weakly supervised) and imaging protocols. Meanwhile, the idea of neighborhood information fusion was also expanded to other network architectures, resulting in significant improvements. CONCLUSIONS: These investigations indicate that the proposed network, which combines the neighborhood information strategy with temporal modeling architecture, is well capable of performing OCTA reconstruction, and has a certain potential for clinical applications.

The complete chloroplast genome sequence of Casuarina equisetifolia.

Casuarina equisetifolia, as windbreaks, soil erosion, and sand dune stabilization with high resistant to typhoon force winds, drought and salinization, belongs to the Casuarinaceae family. In this study, the complete chloroplast genome of C. equisetifolia was sequenced by Illumina sequencing platform and annotated by Geneious Prime. The complete chloroplast genome size is 156,128 bp in length, with a large single copy region (LSC: 86,192 bp) and a small single-copy region (SSC: 18,462 bp), which was separated by a pair of 25,737 bp inverted repeated regions (IRs). The chloroplast genome of C. equisetifolia encodes total 127 genes, including 82 protein-coding genes, 37 tRNA genes, and eight rRNA genes. The phylogenomic relationship analysis suggested that the Casuarinaceae family, which includes C. equisetifolia, was more closely related to the family of Betulaceae.

Comparative study of deep neural networks with unsupervised Noise2Noise strategy for noise reduction of optical coherence tomography images.

As a powerful diagnostic tool, optical coherence tomography (OCT) has been widely used in various clinical setting. However, OCT images are susceptible to inherent speckle noise that may contaminate subtle structure information, due to low-coherence interferometric imaging procedure. Many supervised learning-based models have achieved impressive performance in reducing speckle noise of OCT images trained with a large number of noisy-clean paired OCT images, which are not commonly feasible in clinical practice. In this article, we conducted a comparative study to investigate the denoising performance of OCT images over different deep neural networks through an unsupervised Noise2Noise (N2N) strategy, which only trained with noisy OCT samples. Four representative network architectures including U-shaped model, multi-information stream model, straight-information stream model and GAN-based model were investigated on an OCT image dataset acquired from healthy human eyes. The results demonstrated all four unsupervised N2N models offered denoised OCT images with a performance comparable with that of supervised learning models, illustrating the effectiveness of unsupervised N2N models in denoising OCT images. Furthermore, U-shaped models and GAN-based models using UNet network as generator are two preferred and suitable architectures for reducing speckle noise of OCT images and preserving fine structure information of retinal layers under unsupervised N2N circumstances.

The complete chloroplast genome of Clerodendrum japonicum (Thunb.) Sweet, a traditional Chinese medicinal plant.

Clerodendrum japonicum (Thunb.) sweet, a member of Verbenaceae, is a traditional Chinese medicinal plant mainly distributed in tropical and subtropical Asia. Herein, we reported the complete chloroplast genome sequence of C. japonicum. The size of the chloroplast genome is 152,171 bp in length, including a large single-copy region (LSC) of 83,415 bp, a small single-copy region (SSC) of 17,318 bp, which was separated by a pair of inverted repeated regions of 25,719 bp. The C. japonicum chloroplast genome encodes 133 genes, including 88 protein-coding genes, 37 tRNA genes, and eight rRNA genes. The phylogenetic tree showed that C. japonicum is closely related to C. mandarinorum and C. yunnanense.

The complete chloroplast genome sequence of Sloanea sinensis.

Sloanea sinensis (Hance) Hu is a tree species and member of the Elaeocarpaceae family. It's an excellent commercial tree species which has a relatively high net growth as forests. Here, we report the complete chloroplast genome sequence of a Sloanea genus for the first time. The complete chloroplast sequence of S. sinensis is 158,001 bp in length, including a large single copy region (LSC: 88,481 bp) and a small single copy region (SSC: 17,481 bp), the latter of which is separated by a pair of inverted repeat regions (IRs: 26,051 bp). Phylogenetic analysis indicates that the Elaeocarpaceae is a family within the Oxalidales may be more appropriate than belongs to Malvales as traditional plant taxonomy.

PMS-GAN: Parallel Multi-Stream Generative Adversarial Network for Multi-Material Decomposition in Spectral Computed Tomography.

Spectral computed tomography is able to provide quantitative information on the scanned object and enables material decomposition. Traditional projection-based material decomposition methods suffer from the nonlinearity of the imaging system, which limits the decomposition accuracy. Inspired by the generative adversarial network, we proposed a novel parallel multi-stream generative adversarial network (PMS-GAN) to perform projection-based multi-material decomposition in spectral computed tomography. By designing the differential map and incorporating the adversarial network into loss function, the decomposition accuracy was significantly improved with robust performance. The proposed network was quantitatively evaluated by both simulation and experimental study. The results show that PMS-GAN outperformed the reference methods with certain robustness. Compared with Pix2pix-GAN, PMS-GAN increased the structural similarity index by 172% on the contrast agent Ultravist370, 11% on bones, and 71% on bone marrow, respectively, in a simulated test scenario. In an experimental test scenario, 9% and 38% improvements of the structural similarity index on the biopsy needle and on a torso phantom were observed, respectively. The proposed network demonstrates its capability of multi-material decomposition and has certain potential toward clinical applications.

Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography.

Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Deep learning networks have been widely applied in the field of OCTA reconstruction, benefiting from its powerful mapping capability among images. However, these existing deep learning-based methods depend on high-quality labels, which are hard to acquire considering imaging hardware limitations and practical data acquisition conditions. In this article, we proposed an unprecedented weakly supervised deep learning-based pipeline for OCTA reconstruction task, in the absence of high-quality training labels. The proposed pipeline was investigated on an in vivo animal dataset and a human eye dataset by a cross-validation strategy. Compared with supervised learning approaches, the proposed approach demonstrated similar or even better performance in the OCTA reconstruction task. These investigations indicate that the proposed weakly supervised learning strategy is well capable of performing OCTA reconstruction, and has a certain potential towards clinical applications.

N2NSR-OCT: Simultaneous denoising and super-resolution in optical coherence tomography images using semisupervised deep learning.

Optical coherence tomography (OCT) imaging shows a significant potential in clinical routines due to its noninvasive property. However, the quality of OCT images is generally limited by inherent speckle noise of OCT imaging and low sampling rate. To obtain high signal-to-noise ratio (SNR) and high-resolution (HR) OCT images within a short scanning time, we presented a learning-based method to recover high-quality OCT images from noisy and low-resolution OCT images. We proposed a semisupervised learning approach named N2NSR-OCT, to generate denoised and super-resolved OCT images simultaneously using up- and down-sampling networks (U-Net (Semi) and DBPN (Semi)). Additionally, two different super-resolution and denoising models with different upscale factors (2× and 4×) were trained to recover the high-quality OCT image of the corresponding down-sampling rates. The new semisupervised learning approach is able to achieve results comparable with those of supervised learning using up- and down-sampling networks, and can produce better performance than other related state-of-the-art methods in the aspects of maintaining subtle fine retinal structures.

Noise reduction in optical coherence tomography images using a deep neural network with perceptually-sensitive loss function.

Optical coherence tomography (OCT) is susceptible to the coherent noise, which is the speckle noise that deteriorates contrast and the detail structural information of OCT images, thus imposing significant limitations on the diagnostic capability of OCT. In this paper, we propose a novel OCT image denoising method by using an end-to-end deep learning network with a perceptually-sensitive loss function. The method has been validated on OCT images acquired from healthy volunteers' eyes. The label images for training and evaluating OCT denoising deep learning models are images generated by averaging 50 frames of respective registered B-scans acquired from a region with scans occurring in one direction. The results showed that the new approach can outperform other related denoising methods on the aspects of preserving detail structure information of retinal layers and improving the perceptual metrics in the human visual perception.

Comparative study of deep learning models for optical coherence tomography angiography.

Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Meanwhile, deep learning has achieved rapid development in image-to-image translation tasks. Some studies have proposed applying deep learning models to OCTA reconstruction and have obtained preliminary results. However, current studies are mostly limited to a few specific deep neural networks. In this paper, we conducted a comparative study to investigate OCTA reconstruction using deep learning models. Four representative network architectures including single-path models, U-shaped models, generative adversarial network (GAN)-based models and multi-path models were investigated on a dataset of OCTA images acquired from rat brains. Three potential solutions were also investigated to study the feasibility of improving performance. The results showed that U-shaped models and multi-path models are two suitable architectures for OCTA reconstruction. Furthermore, merging phase information should be the potential improving direction in further research.

Autophagy and Hsp70 activation alleviate oral epithelial cell death induced by food-derived hypertonicity.

Stable intracellular and intercellular osmolarity is vital for all physiological processes. Although it is the first organ that receives food, the osmolarity around the mouth epithelium has never been systematically investigated. We found that oral epithelial cells are a population of ignored cells routinely exposed to hypertonic environments mainly composed of saline, glucose, etc. in vivo after chewing food. By using cultured oral epithelial cells as an in vitro model, we found that the hypotonic environments caused by both high NaCl and high glucose induced cell death in a dose- and time-dependent manner. Transcriptomics revealed similar expression profiles after high NaCl and high glucose stimulation. Most of the common differentially expressed genes were enriched in "mitophagy" and "autophagy" according to KEGG pathway enrichment analysis. Hypertonic stimulation for 1 to 6 h resulted in autophagosome formation. The activation of autophagy protected cells from high osmolarity-induced cell death. The activation of Hsp70 by the pharmacological activator handelin significantly improved the cell survival rate after hypertonic stimulation. The protective role of Hsp70 activation was partially dependent on autophagy activation, indicating a crosstalk between Hsp70 and autophagy in hypertonic stress response. The extract of the handelin-containing herb Chrysanthemum indicum significantly protected oral epithelial cells from hypertonic-induced death, providing an inexpensive way to protect against hypertonic-induced oral epithelial damage. In conclusion, the present study emphasized the importance of changes in osmolarity in oral health for the first time. The identification of novel compounds or herbal plant extracts that can activate autophagy or HSPs may contribute to oral health and the food industry.