Search

Oxidative stress causes chronic inflammation, and mediates various diseases.

Ganoderic acids (GAs) are well recognized as important pharmacological components of the medicinal species belonging to the basidiomycete genus Ganoderma. However, transcription factors directly regulating the expression of GA biosynthesis genes remain poorly understood. Here, the genome of Ganoderma lingzhi was de novo sequenced. Using DAP-seq, we identified putative targets of the transcription factor SREBP, including the genes of triterpenoid synthesis and lipid metabolism. Interactions between SREBP and the targets were verified by EMSA. RNA-seq showed that SREBP targets, mevalonate kinase and 3-hydroxy-3-methylglutaryl CoA synthetase in mevalonate pathway, sterol isomerase and lanosterol 14-demethylase in ergosterol biosynthesis, were significantly upregulated in the SREBP overexpression strain. In addition, 3 targets involved in glycerophospholipid/glycerolipid metabolism were upregulated. Then, the contents of mevalonic acid, lanosterol, ergosterol and 13 different GAs as well as a variety of lipids were significantly increased in this strain. Furthermore, the effects of SREBP overexpression on triterpenoid and lipid metabolisms were recovered when OE::SREBP strain were treated with exogenous fatostatin, a specific inhibitor of SREBP. Taken together, our genome-wide study clarified the role of SREBP in triterpenoid and lipid metabolisms of G. lingzhi.

Ganoderic acids (GAs) are well recognized as important pharmacological components of the medicinal species belonging to the basidiomycete genus Ganoderma. However, transcription factors directly regulating the expression of GA biosynthesis genes remain poorly understood. Here, the genome of Ganoderma lingzhi is de novo sequenced. Using DNA affinity purification sequencing, we identify putative targets of the transcription factor sterol regulatory element-binding protein (SREBP), including the genes of triterpenoid synthesis and lipid metabolism. Interactions between SREBP and the targets are verified by electrophoretic mobility gel shift assay. RNA-seq shows that SREBP targets, mevalonate kinase and 3-hydroxy-3-methylglutaryl coenzyme A synthetase in mevalonate pathway, sterol isomerase and lanosterol 14-demethylase in ergosterol biosynthesis, are significantly upregulated in the SREBP overexpression (OE::SREBP) strain. In addition, 3 targets involved in glycerophospholipid/glycerolipid metabolism are upregulated. Then, the contents of mevalonic acid, lanosterol, ergosterol and 13 different GAs as well as a variety of lipids are significantly increased in this strain. Furthermore, the effects of SREBP overexpression on triterpenoid and lipid metabolisms are recovered when OE::SREBP strain are treated with exogenous fatostatin, a specific inhibitor of SREBP. Taken together, our genome-wide study clarify the role of SREBP in triterpenoid and lipid metabolisms of G. lingzhi.

Radiomic representations can quantify properties of regions of interest in medical image data. Classically, they account for pre-defined statistics of shape, texture, and other low-level image features. Alternatively, deep learning-based representations are derived from supervised learning but require expensive annotations from experts and often suffer from overfitting and data imbalance issues. In this work, we address the challenge of learning representations of 3D medical images for an effective quantification under data imbalance. We propose a \emph{self-supervised} representation learning framework to learn high-level features of 3D volumes as a complement to existing radiomics features. Specifically, we demonstrate how to learn image representations in a self-supervised fashion using a 3D Siamese network. More importantly, we deal with data imbalance by exploiting two unsupervised strategies: a) sample re-weighting, and b) balancing the composition of training batches. When combining our learned self-supervised feature with traditional radiomics, we show significant improvement in brain tumor classification and lung cancer staging tasks covering MRI and CT imaging modalities., camera-ready version in MICCAI 2021

Chest radiography is widely used in annual medical screening to check whether lungs are healthy or not. Therefore it would be desirable to develop an intelligent system to help clinicians automatically detect potential abnormalities in chest X-ray images. Here with only healthy X-ray images, we propose a new abnormality detection approach based on an autoencoder which outputs not only the reconstructed normal version of the input image but also a pixel-wise uncertainty prediction. Higher uncertainty often appears at normal region boundaries with relatively larger reconstruction errors, but not at potential abnormal regions in the lung area. Therefore the normalized reconstruction error by the uncertainty provides a natural measurement for abnormality detection in images. Experiments on two chest X-ray datasets show the state-of-the-art performance by the proposed approach.

A facile one-pot method is proposed for the fabrication of yolk-shell mesoporous silica nanoparticles with high special surface area. The particle size can be well controlled by moderately tuning some experimental parameters.

Convolutional neural networks (CNNs) are vulnerable to adversarial noises, which may result in potentially disastrous consequences in safety or security sensitive systems. This paper proposes a novel mechanism to improve the robustness of medical image classification systems by bringing denoising ability to CNN classifiers with a naturally embedded auto-encoder and high-level feature invariance to general noises. This novel denoising mechanism can be adapted to many model architectures, and therefore can be easily combined with existing models and denoising mechanisms to further improve robustness of CNN classifiers. This proposed method has been confirmed by comprehensive evaluations with two medical image classification tasks.

Monin&ndash, Obukhov similarity theory (MOST) overestimates the mean vertical velocity gradient in some atmospheric stable conditions, i.e., Richardson number R f &lt, 0 . 25 . To obtain a given hub-height inflow velocity for a certain roughness length, this overestimated velocity gradient underpredicts the friction wind speed and the turbulence intensity, potentially influencing wake modeling of a wind turbine. This work investigates the side effects of the breakdown of MOST on wake modeling under stable conditions and makes some modifications to the flow similarity functions to eliminate these side effects. Based on a field measurement in a wind farm, we first show that MOST predicts a larger velocity gradient for the atmospheric stability parameter &zeta, &gt, 0 . 1 and proposes new flow similarity functions without constraining R f to limit the overestimated velocity gradient. Next, different turbulence models based on MOST and a modified one based on the new similarity functions are investigated through numerical simulations. These turbulence models are combined with the actuator disk model (AD) and Reynolds-averaged Navier&ndash, Stokes equations (RANS) to model wind turbine wakes under stable conditions. As compared to measurements, numerical results show that turbulence models based on MOST result in a larger wake deficit and a slower wake recovery rate with a root-mean-squared error (RSME) of wake deficit in the range of 0.07 to 0.20. This overestimated wake effect is improved by applying the new similarity functions, and the RSME of wake deficit is reduced by 0.05 on average.

The real time and in situ investigation of the crystallization process and structure transitions of asymmetric crystalline-crystalline diblock copolymers from the melt was performed with synchrotron simultaneous SAXS/WAXS. The asymmetric poly(ethylene oxide)-b-poly(ɛ-caprolactone) diblock copolymers were chosen for the present study. It was shown that the short blocks crystallized later than the long blocks and final lamellar structure was formed in all of the asymmetric diblock copolymers. The final lamellar structure was confirmed by AFM observation. The SAXS data were analyzed with different methods for the early stage of the crystallization. The Guinier plots indicated that there were no isolated domains formed before the formation of lamellae in the asymmetric diblock copolymers during the crystallization process. Debye-Bueche plots implied the formation of correlated domains during crystallization.

The time- and temperature-related crystallization process for the structure transitions of asymmetric crystalline-crystalline diblock copolymers from the melt to crystallites was investigated with synchrotron simultaneous small-angle/wide-angle X-ray scattering. Two asymmetric poly(ethylene oxide)-poly(e-caprolactone) diblock copolymers were chosen. It is found in the course of the copolymer crystallization that the shorter blocks are uncrystallizable in both of the asymmetric diblock copolymers and final lamellar structures are formed in both of them. The final lamellar structure was confirmed from atomic force microscopy observations. The small-angle X-ray scattering data collected were analyzed with different methods for the early stage of crystallization. Guinier and Debye-Bueche plots indicate that there are neither isolated domains nor correlated domains formed before the formation of lamellae in the asymmetric diblock copolymers during the crystallization process. The structure evolution was calculated according to the correlation function, and the soft nanoconfined crystallization behavior is discussed. Copyright (C) 2012 Society of Chemical Industry

A simple and facile one-pot sol-gel method is proposed for the fabrication of hollow mesoporous silica particles. Both the particle size and the shell thickness can be well controlled by moderately tuning some experimental parameters.

The suggestion of microcosmic syndrome differentiation is to meet the needs of clinical diagnosis and treatment of traditional Chinese medicine (TCM), the needs of evidence based medicine and the needs of the development of syndrome differentiation in TCM. The possibility of microcosmic syndrome differentiation might be the development of integration of traditional Chinese and Western medicine as well as modern science and technology. Microcosmic syndrome differentiation has its superiority and limitation. There are some problems in developing microcosmic syndrome differentiation, such as lack of standard for syndrome differentiation, contradiction between single microcosmic index and the entirety, non-specificity of the relationship between microcosmic indexes and syndromes, and the influence of disease on microcosmic syndrome differentiation, discrepancy between the essence study of TCM syndromes and the clinical practice. It is suggested that the concept of TCM syndrome is a certain pathological process during the onset and development of disease, composed of some subjective and objective indexes. The objective indexes include the macrocosmic and the microcosmic indexes, the entirety and the part.

Based on analysis the basic trend of Yunnan's energy economy, through the energy intensity structure decomposition analytic method, this paper analyses and researches the effect of economic structural change on energy intensity in Yunnan Province. The research shows that structural changes played a certain promotion effect on the energy intensity change in the industrial level; the power of energy intensity decline in Yunnan Province, mainly from the increased efficiency of energy use; after some further analysis we found the reason of rapid decline of energy intensity in Yunnan Province is basically due to the increased efficiency of energy use in the secondary industry, which offset the fluctuations of other industry in energy intensity as well as energy intensity increased by industrial structural changes. After the analysis we draw the following conclusion: there already have certain progresses of reduce the energy intensity through adjust the economic structure of Yunnan, but there still have potential; the economic structure promoted still should take the industrialization as the main direction, and developed the tertiary industry vigorously. In order to reduce energy intensity, promoting the energy efficiency through system innovation and technology introduction is still needed. For the above situations, this paper puts forward some corresponding proposals.