Dual-Topology Hamiltonian-Replica-Exchange Overlap Histogramming Method to Calculate Relative Free Energy Difference in Rough Energy Landscape

A novel overlap histogramming method based on Dual-Topology Hamiltonian-Replica-Exchange simulation technique is presented to efficiently calculate relative free energy difference in rough energy landscape, in which multiple conformers coexist and are separated by large energy barriers. The proposed method is based on the realization that both DT-HERM exchange efficiency and confidence of free energy determination in overlap histogramming method depend on the same criteria: neighboring states' energy derivative distribution overlap. In this paper, we demonstrate this new methodology by calculating free energy difference between amino acids: Leucine and Asparagine, which is an identified chanllenging system for free energy simulations.Comment: 14 pages with 4 figure

室内植物表型平台及性状鉴定研究进展和展望

Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perceptivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorised according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future

Alleviation of cadmium toxicity in cucumber (Cucumis sativus) seedlings by the application of selenium

In the present study, the role of selenium in cadmium toxicity was investigated in cucumber seedlings by hydroponic experiments. The application of Se for cucumber exposed to Cd significantly reduced Cd accumulation in all tissues, elevated Cd-depressed chlorophyll content, and improved photosynthetic performance. External Se significantly reduced ·OH, H2O2 and malondialdehyde content. Exogenous Se balanced Cd-depressed elements (e.g., Se enhanced Cd-induced decreases in root Zn, leaf/stem/root Mn concentrations) and carbohydrate contents. External Se also significantly decreased the Cd-induced increases in Na+K+-, Ca2+Mg2+- and total ATPase activities, which recovered almost to control level. Results indicate that application of Se can alleviate Cd toxicity in cucumber seedlings by reducing Cd uptake and reactive oxygen species (ROS) accumulation, moreover protecting photosynthetic machinery from damaging, balancing elements and carbohydrate contents, and improving ATPase activities in cucumber

Unexpected CRISPR off-target mutation pattern in vivo are not typically germline-like [preprint]

A computationally evolutionary investigation was performed to re-analyze the WGS data of the two studies published in Nature Methods (2015, 2017) with opposite conclusions on CRISPR off-target mutations. Our analysis concluded that the so-called unexpected SNVs pattern obtained by the study of Schaefer et al. are not typically germline-like. Some of unusual and unidentified mutations may arise, but the real reasons remain to be explored. Based on the available data and a direct comparison of the two studies, we presented two possible reasons and future re-analysis directions that may contribute to such different conclusions. To characterize the authentic CRISPR-mediated mutations, we are required to have appropriate controls to rule out other sources of mutations, which will be needed for benchmarking of targeting safety of CRISPR-based gene therapy

Learning to find topic experts in Twitter via different relations

Singapore National Research Foundatio

Non-destructive prediction and visualization of anthocyanin content in mulberry fruits using hyperspectral imaging

Being rich in anthocyanin is one of the most important physiological traits of mulberry fruits. Efficient and non-destructive detection of anthocyanin content and distribution in fruits is important for the breeding, cultivation, harvesting and selling of them. This study aims at building a fast, non-destructive, and high-precision method for detecting and visualizing anthocyanin content of mulberry fruit by using hyperspectral imaging. Visible near-infrared hyperspectral images of the fruits of two varieties at three maturity stages are collected. Successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and stacked auto-encoder (SAE) are used to reduce the dimension of high-dimensional hyperspectral data. The least squares-support vector machine and extreme learning machine (ELM) are used to build models for predicting the anthocyanin content of mulberry fruit. And genetic algorithm (GA) is used to optimize the major parameters of models. The results show that the higher the anthocyanin content is, the lower the spectral reflectance is. 15, 7 and 13 characteristic variables are extracted by applying CARS, SPA and SAE respectively. The model based on SAE-GA-ELM achieved the best performance with R2 of 0.97 and the RMSE of 0.22 mg/g in both the training set and testing set, and it is applied to retrieve the distribution of anthocyanin content in mulberry fruits. By applying SAE-GA-ELM model to each pixel of the mulberry fruit images, distribution maps are created to visualize the changes in anthocyanin content of mulberry fruits at three maturity stages. The overall results indicate that hyperspectral imaging, in combination with SAE-GA-ELM, can help achieve rapid, non-destructive and high-precision detection and visualization of anthocyanin content in mulberry fruits

PAN Nanofibers Reinforced with MMT/GO Hybrid Nanofillers

Single component nanofiller has shown some limitations in its performance, which can be overcome by hybrid nanofillers with two different components. In this work, montmorillonite (MMT)/graphene oxide (GO) hybrid nanofillers were formed by self-assembly and then incorporated into the polyacrylonitrile (PAN) nanofibers by electrospinning process. X-ray diffraction (XRD), atomic force microscopy (AFM), and transmission electron microscopy (TEM) were utilized to analyze the structures of MMT/GO hybrid nanofillers. And the effects of MMT/GO hybrid nanofillers on the morphology, thermal stability, and mechanical properties of PAN/MMT/GO composite nanofibrous membrane were examined by scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and tensile test machine, respectively. The incorporation of MMT/GO hybrid nanofillers into PAN nanofibers showed a noticeable increase up to 30°C for the onset decomposition temperature and 1.32 times larger tensile strength than the pure PAN, indicating that the hybrid nanofiller is a promising candidate in improving thermal and mechanical properties of polymers

Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde