陆地生态系统环境控制实验的研究方法及技术体系

生物及生态系统与环境变化间的反馈关系及其过程机制是生态学研究的重要内容。不同类型的生物环境因素控制实验以及大尺度的联网野外控制实验被认为是认识生态系统响应和适应环境变化过程机制、精细定量表达的有效手段及认知过程的加速器。近年来发展了大型野外物理模拟实验装置网络(如ECOTRON)、生态系统分析与实验平台(AnaEE)、国际干旱实验研究网络(Drought Network)、氮沉降联合实验网络(Nutrient Network),以及基于各区域性生态观测实验站的联网控制实验(如USA-ILTER)。发展大陆尺度联网实验研究平台事业正日益受到学术界的重视,将会在认知生态系统环境响应过程机制方面发挥更重要的作用。基于以上背景,本文综述了生态系统环境控制实验的研究方法和实验体系的发展,明确指出各种类型的生物环境控制实验需要形成联合协作体系,共同解决生态系统对环境变化的响应及适应的基本科学问题。目前的控制实验包括: 1) 实验室封闭装置内的生物生理生态学控制实验;2) 野外实验场的半开放部分环境要素控制实验;3) 近自然状态的野外环境控制实验;以及4) 基于野外生态站的联网控制实验。进而,本文还深入讨论了陆地生态系统的环境响应及适应过程机制实验系统设计的发展趋势,分析了基于大尺度自然环境梯度实验及生态站尺度的要素控制实验的优势,提出了整合两种实验技术、发展新一代的野外联网实验体系的科学设想,讨论了基于野外联网控制实验的研究体系,论证了研究生态系统对环境变化短期响应和长期适应的规律和机制、生态系统环境响应定量表达的技术途径。若本文提出的控制实验体系设计方案能够得以实施,必将大大促进我国乃至全球生态系统和环境变化科学的研究水平,对我国应对气候变化和生态环境建设具有重要的科学意义。     

既主内政, 又辖外交——以PHR为中心的基因网络调控植物-菌根真菌的共生

磷是植物生长发育必需的大量矿质营养元素, 但自然界大部分土壤都存在严重缺磷的问题。为了适应这一营养逆境, 植物演化出一系列低磷胁迫应答反应。通过改变基因的转录水平调控低磷胁迫应答反应, 而转录因子PHR1在调控植物对低磷胁迫的转录响应中起关键作用。此外, 大部分陆生植物还能与丛枝菌根真菌建立共生关系, 通过丛枝菌根真菌更有效地从土壤中获取磷元素。最近, 中国科学院分子植物科学卓越创新中心王二涛研究组发现, 以PHR为中心的转录调控网络控制植物-丛枝菌根真菌共生的建立。因此, PHR不但在维持植物细胞自身的磷稳态中发挥作用, 而且参与植物与外界微生物的相互作用, 为植物有效地从环境中获得磷元素提供了另外一条途径。     

蛹虫草组学研究进展

作为虫草属的模式种,蛹虫草是目前虫草类真菌中研究和应用最为广泛的物种之一。随着基因组序列的公布,蛹虫草组学水平的研究近年来取得了明显的进展。本文从基因组、线粒体基因组、甲基化组、转录组、蛋白质组、代谢网络等角度对蛹虫草组学水平的研究现状进行综述,期望对进一步推动蛹虫草的深入研究提供帮助。     

Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective

Genetic, epigenetic, and metabolic alterations are all hallmarks of cancer. However, the epigenome and metabolome are both highly complex and dynamic biological networks in vivo. The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy. From this perspective, we first review the state of high-throughput biological data acquisition(i.e. multiomics data)and analysis(i.e. computational tools) and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net) that is based on these current high-throughput methods. The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes, omics data acquisition, analysis of network information, and integration with validated database knowledge. Thus, MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks. We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.     

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN–2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.     

A guide to metabolic flux analysis in metabolic engineering: Methods,tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, “Metabolic fluxes and metabolic engineering” (Metabolic Engineering, 1: 1–11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.     

Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer's Disease

ObjectivesAlzheimer's Disease (AD) is the most general type of dementia. In all leading countries, it is one of the primary reasons of death in senior citizens. Currently, it is diagnosed by calculating the MSME score and by the manual study of MRI Scan. Also, different machine learning methods are utilized for automatic diagnosis but existing has some limitations in terms of accuracy. So, main objective of this paper to include a preprocessing method before CNN model to increase the accuracy of classification.Materials and methodIn this paper, we present a deep learning-based approach for detection of Alzheimer's Disease from ADNI database of Alzheimer's disease patients, the dataset contains fMRI and PET images of Alzheimer's patients along with normal person's image. We have applied 3D to 2D conversion and resizing of images before applying VGG-16 architecture of Convolution neural network for feature extraction. Finally, for classification SVM, Linear Discriminate, K means clustering, and Decision tree classifiers are used.ResultsThe experimental result shows that the average accuracy of 99.95% is achieved for the classification of the fMRI dataset, while the average accuracy of 73.46% is achieved with the PET dataset. On comparing results on the basis of accuracy, specificity, sensitivity and on some other parameters we found that these results are better than existing methods.Conclusionsthis paper, suggested a unique way to increase the performance of CNN models by applying some preprocessing on image dataset before sending to CNN architecture for feature extraction. We applied this method on ADNI database and on comparing the accuracies with other similar approaches it shows better results.     

An Approach Based on Mutually Informed Neural Networks to Optimize the Generalization Capabilities of Decision Support Systems Developed for Heart Failure Prediction

Available clinical methods for heart failure (HF) diagnosis are expensive and require a high-level of experts intervention. Recently, various machine learning models have been developed for the prediction of HF where most of them have an issue of over-fitting. Over-fitting occurs when machine learning based predictive models show better performance on the training data yet demonstrate a poor performance on the testing data and the other way around. Developing a machine learning model which is able to produce generalization capabilities (such that the model exhibits better performance on both the training and the testing data sets) could overall minimize the prediction errors. Hence, such prediction models could potentially be helpful to cardiologists for the effective diagnose of HF. This paper proposes a two-stage decision support system to overcome the over-fitting issue and to optimize the generalization factor. The first stage uses a mutual information based statistical model while the second stage uses a neural network. We applied our approach to the HF subset of publicly available Cleveland heart disease database. Our experimental results show that the proposed decision support system has optimized the generalization capabilities and has reduced the mean percent error (MPE) to 8.8% which is significantly less than the recently published studies. In addition, our model exhibits a 93.33% accuracy rate which is higher than twenty eight recently developed HF risk prediction models that achieved accuracy in the range of 57.85% to 92.31%. We can hope that our decision support system will be helpful to cardiologists if deployed in clinical setup.