塑料生物降解检测方法的研究进展

塑料处理不当造成的污染问题已成为全球性难题。目前的解决办法除回收利用与使用可生物降解塑料替代之外,最主要途径仍是寻求高效的塑料降解方法。其中,采用微生物或酶处理塑料的方法因其具有条件温和、不产生次生环境污染的优势而受到越来越多的关注。塑料生物降解技术的核心是高效解聚微生物/酶,然而当前的分析检测方法无法满足塑料生物降解资源的高效筛选,因此开发准确、快速的塑料降解过程分析方法,对于生物降解资源筛选和降解效能评价具有重要意义。本文介绍了近年来在塑料生物降解领域的常用分析检测技术,包括高效液相色谱、红外光谱、凝胶渗透色谱以及透明圈测定等,重点讨论了荧光分析策略在快速表征塑料生物降解过程中的应用,为进一步规范塑料生物降解过程的表征与分析研究,以及开发更高效的塑料生物降解资源筛选方法提供借鉴。     

Analysis of stereoisomers of chiral drug by mass spectrometry

Chiral molecules are of great importance in the life science since individual enantiomers may differ in biological activity, mechanism, and toxicity, making it necessary to explore efficient chiral analysis methods. Chromatography approaches are often used to differentiate enantiomers while mass spectrometry (MS) was thought to be blind in chiral analysis. With the development of MS technique, it began to play a more and more crucial part in chiral observation. In this review, we will give a detailed introduction of the analysis methods related to MS for chiral drugs, including its mechanism, applications, and future development.     

A review of big data analysis methods for baleen whale passive acoustic monitoring

Many organizations collect large passive acoustic monitoring (PAM) data sets that need to be efficiently and reliably analyzed. To determine appropriate methods for effective analysis of big PAM data sets, we undertook a literature review of baleen whale PAM analysis methods. Methodologies from 166 studies (published between 2000–2019) were summarized, and a detailed review was performed on the 94 studies that recorded more than 1,000 hr of acoustic data (“big data”). Analysis techniques for extracting baleen whale information from PAM data sets varied depending on the research observed. A spectrum of methodologies was used and ranged from manual analysis of all acoustic data by human experts to completely automated techniques with no manual validation. Based on this assessment, recommendations are provided to encourage robust research methods that are comparable across studies and sectors, achievable across research groups, and consistent with previous work. These include using automated techniques when possible to increase efficiency and repeatability, supplementing automation with manual review to calculate automated detector performance, and increasing consistency in terminology and presentation of results. This work can be used to facilitate discussion for minimum standards and best practices to be implemented in the field of marine mammal PAM.     

土壤微生物多样性研究方法

概述了研究土壤微生物多样性的主要方法．传统上,土壤微生物群落的分析依赖于培养技术,使用各种培养基最大限度地培养各种微生物群体,但仍只能培养和分离出一小部分土壤微生物群落．使用Biolog分析、磷脂脂肪酸分析和核酸分析等方法,可研究和表征那些现在还不能够被培养的土壤微生物。从而获取关于土壤微生物群落多样性的更多和更完整的信息．     

Methods for estimating and mitigating errors in spotted, dual-color DNA microarrays

The conceptual simplicity of DNA microarray technology often belies the complex nature of the measurement errors inherent in the methodology. As the technology has developed, the importance of understanding the sources of uncertainty in the measurements and developing ways to control their influence on the conclusions drawn has become apparent. In this review, strategies for modeling measurement errors and minimizing their effect on the outcome of experiments using a variety of techniques are discussed in the context of spotted, dual-color microarrays. First, methods designed to reduce the influence of random variability through data filtering, replication, and experimental design are introduced. This is followed by a review of data analysis methods that partition the variance into random effects and one or more systematic effects, specifically two-sample significance testing and analysis of variance (ANOVA) methods. Finally, the current state of measurement error models for spotted microarrays and their role in variance stabilizing transformations are discussed.     

Cytochemical methods for the detection of apoptosis.

Detection of apoptotic cell death in cells and tissues has become of paramount importance in many fields of modern biology, including studies of embryonic development, degenerative disease, and cancer biology. In addition to methods that employ biochemical analysis of large populations of cells, cytochemical methods have recently been extensively used both in individual cells and in tissues. Most of these methods exploit properties of dying cells that are more or less specific for the apoptotic process. However, considerable confusion exists over the interpretation of some of these methods and their usefulness in all settings. This review attempts to summarize the more recent advances in cytochemical detection of apoptosis and emphasizes some of the pitfalls that confuse the interpretation of results of these methods.     

Analysis and Interpretation of Superresolution Single-Particle Trajectories

A large number (tens of thousands) of single molecular trajectories on a cell membrane can now be collected by superresolution methods. The data contains information about the diffusive motion of molecule, proteins, or receptors and here we review methods for its recovery by statistical analysis of the data. The information includes the forces, organization of the membrane, the diffusion tensor, the long-time behavior of the trajectories, and more. To recover the long-time behavior and statistics of long trajectories, a stochastic model of their nonequilibrium motion is required. Modeling and data analysis serve extracting novel biophysical features at an unprecedented spatiotemporal resolution. The review presents data analysis, modeling, and stochastic simulations applied in particular on surface receptors evolving in neuronal cells.     

Advances in the analysis of protein phosphorylation

Phosphorylation is one of the most relevant and ubiquitous post-translational modifications. Despite its relevance, the analysis of protein phosphorylation has been revealed as one of the most challenging tasks due to its highly dynamic nature and low stoichiometry. However, the development and introduction of new analytical methods are modifying rapidly and substantially this field. Especially important has been the introduction of more sensitive and specific methods for phosphoprotein and phosphopeptide purification as well as the use of more sensitive and accurate MS-based analytical methods. The integration of both approaches has enabled large-scale phosphoproteome studies to be performed, an unimaginable task few years ago. Additionally, methods originally developed for differential proteomics have been adapted making the study of the highly dynamic nature of protein phosphorylation feasible. This review aims at offering an overview on the most frequently used methods in phosphoprotein and phosphopeptide enrichment as well as on the most recent MS-based analysis strategies. Current strategies for quantitative phosphoproteomics and the study of the dynamics of protein phosphorylation are highlighted.     

Structural mass spectrometry of membrane proteins

The analysis of proteins and protein complexes by mass spectrometry (MS) has come a long way since the invention of electrospray ionization (ESI) in the mid 80s. Originally used to characterize small soluble polypeptide chains, MS has progressively evolved over the past 3 decades towards the analysis of samples of ever increasing heterogeneity and complexity, while the instruments have become more and more sensitive and resolutive. The proofs of concepts and first examples of most structural MS methods appeared in the early 90s. However, their application to membrane proteins, key targets in the biopharma industry, is more recent. Nowadays, a wealth of information can be gathered from such MS-based methods, on all aspects of membrane protein structure: sequencing (and more precisely proteoform characterization), but also stoichiometry, non-covalent ligand binding (metals, drug, lipids, carbohydrates), conformations, dynamics and distance restraints for modelling. In this review, we present the concept and some historical and more recent applications on membrane proteins, for the major structural MS methods.     

功能性脑成像的多维模式分析方法

利用非侵入式的功能性脑成像记录大脑活动极大地提升了我们对人类认知功能的理解.与此同时,分析成像数据的手段也逐渐从传统的一元方式向更加有效的多元分析转变.在本综述中,特别针对在认知神经科学领域占主导地位的功能性磁共振成像技术,介绍其多元数据分析方法的发展以及这种分析方法的生理学基础和未来发展方向.     

Pathway-based Analysis Tools for Complex Diseases:A Review

Genetic studies are traditionally based on single-gene analysis. The use of these analyses can pose tremendous challenges for elucidating complicated genetic interplays involved in complex human diseases. Modern pathway-based analysis provides a technique, which allows a comprehen- sive understanding of the molecular mechanisms underlying complex diseases. Extensive studies uti- lizing the methods and applications for pathway-based analysis have significantly advanced our capacity to explore large-scale omics data, which has rapidly accumulated in biomedical fields. This article is a comprehensive review of the pathway-based analysis methods the powerful methods with the potential to uncover the biological depths of the complex diseases. The general concepts and procedures for the pathway-based analysis methods are introduced and then, a comprehensive review of the major approaches for this analysis is presented. In addition, a list of available path- way-based analysis software and databases is provided. Finally, future directions and challenges for the methodological development and applications of pathway-based analysis techniques are dis- cussed. This review will provide a useful guide to dissect complex diseases.     

Methods for kinetic and thermodynamic analysis of aminoacyl-tRNA synthetases

The accuracy of protein synthesis relies on the ability of aminoacyl-tRNA synthetases (aaRSs) to discriminate among true and near cognate substrates. To date, analysis of aaRSs function, including identification of residues of aaRS participating in amino acid and tRNA discrimination, has largely relied on the steady state kinetic pyrophosphate exchange and aminoacylation assays. Pre-steady state kinetic studies investigating a more limited set of aaRS systems have also been undertaken to assess the energetic contributions of individual enzyme-substrate interactions, particularly in the adenylation half reaction. More recently, a renewed interest in the use of rapid kinetics approaches for aaRSs has led to their application to several new aaRS systems, resulting in the identification of mechanistic differences that distinguish the two structurally distinct aaRS classes. Here, we review the techniques for thermodynamic and kinetic analysis of aaRS function. Following a brief survey of methods for the preparation of materials and for steady state kinetic analysis, this review will describe pre-steady state kinetic methods employing rapid quench and stopped-flow fluorescence for analysis of the activation and aminoacyl transfer reactions. Application of these methods to any aaRS system allows the investigator to derive detailed kinetic mechanisms for the activation and aminoacyl transfer reactions, permitting issues of substrate specificity, stereochemical mechanism, and inhibitor interaction to be addressed in a rigorous and quantitative fashion.     

New developments in profiling and imaging of proteins from tissue sections by MALDI mass spectrometry

Molecular imaging of tissue by MALDI mass spectrometry is a powerful tool for visualizing the spatial distribution of constituent analytes with high molecular specificity. Although the technique is relatively young, it has already contributed to the understanding of many diverse areas of human health. In recent years, a great many advances in the practice of imaging mass spectrometry have taken place, making the technique more sensitive, robust, and ultimately useful. The purpose of this review is to highlight some of the more recent technological advances that have improved the efficiency of imaging mass spectrometry for clinical applications. Advances in the way MALDI mass spectrometry is integrated with histology, improved methods for automation, and better tools for data analysis are outlined in this review. Refined top-down strategies for the identification and validation of candidate biomarkers found in tissue sections are discussed. A clinical example highlighting the application of these methods to a cohort of clinical samples is described.     

水稻抗旱耐盐蛋白质组学研究进展

抗旱耐盐是水稻抗逆研究的重要方向。水稻抗旱耐盐蛋白质组学研究通过观察水稻在干旱和盐胁迫条件下的蛋白质组表达情况,动态分析水稻的蛋白质组变化,使研究的结果和性状联系更紧密,相对于从基因组水平上研究更具实际意义。简要介绍了水稻蛋白质组研究的基本方法和技术并重点叙述了水稻抗旱耐盐蛋白质组研究取得的成果。     

Assessment of Lignocellulosic Biomass Using Analytical Spectroscopy: an Evolution to High-Throughput Techniques

Lignocellulosic biomass has been proposed as an option for reducing global dependence on nonrenewable energy sources, such as oil. Selection and development of biomass feedstocks that efficiently yield the maximum fuel or biomaterial requires the availability of reliable methods for compositional and structural characterization of plant material. Many standard methods for biomass analysis are laborious and slow, and employ a variety of harsh reagents requiring some degree of remediation. The use of simpler and more rapid spectroscopic methods has proved invaluable in analyzing biomass. In the twenty-first century, researchers have employed techniques such as Raman, mid-infrared, and near-infrared spectroscopy for a wide range of applications in endeavors to further understand biofuel feedstocks. While many methods remain time consuming and expensive, a growing interest in high-throughput spectroscopic techniques has provided faster and larger scale feedstock screening for desirable traits. This review seeks to provide an overview of both high-throughput techniques and those requiring longer analysis times but still providing abundant qualitative and quantitative data. While applications of these instrumental methods have been researched for decades, more recent developments will be discussed here.     

全基因组关联研究通路分析方法现状

全基因组关联研究(genome-wide association study, GWAS)自2005年首次发表以来已不断增进人们对疾病遗传机制的认识,结合系统生物学并改进统计分析方法是对GWAS数据进行深度挖掘的重要途径。通路分析(pathway analysis)将GWAS所检测的遗传变异根据一定的生物学含义组合为集合进行分析,有利于发现对疾病单独效应小却在通路中相互关联的遗传变异,更有利于进行生物学解释。当前通路分析在GWAS数据上已有较为广泛的应用并取得初步成果。与此同时,通路分析的统计方法仍在不断发展。本文旨在介绍现有直接以SNP为对象的GWAS通路分析算法,根据方法中是否采用核函数分为非核算法和核算法两大类,其中非核算法主要包括基因功能富集分析(gene set enrichment analysis, GSEA)和分层贝叶斯优取(hierarchical Bayes prioritization, HBP),核算法包括线性核(linear kernel, LIN)、状态认证核(identity-by-status kernel, IBS)和尺度不变核(powered exponential kernel)。通过介绍这些方法的计算原理和优缺点,以期为新算法的构建提供更好的思路,为GWAS领域研究方法的选择提供参考。     

Challenges of single-cell diagnostics: analysis of gene expression

Analysis of single-cell gene expression promises a more precise understanding of human disease pathogenesis and important diagnostic applications. Here, we review the rationale for the study of gene expression at the single-cell level, practical methods to isolate homogeneous or single-cell samples, and current approaches to the analysis of single-cell gene expression. Finally, we highlight applications of laser microdissection-based gene expression analysis to the study of human disease and clinical diagnosis.     

Novel approaches for COVID-19 diagnosis and treatment: a nonsystematic review

Since COVID-19 pandemic has been continuously rising and spreading, several original contributions and review articles on COVID-19 started to appear in the literature. The review articles are mainly focus on the current status of the pandemic along with current status of the corona diagnosis and treatment process. Due to some disadvantages of the currently used methods, the improvement on the novel promising diagnosis and treatment methods of corona virus is very important issue. In this review, after briefly discussing the status of current diagnosis and treatment methods, we present to the scientific community, novel promising methods in the diagnosis and treatment of COVID-19. As with other novel approaches, first, the diagnosis potential of mass spectroscopy and optical spectroscopic methods such as UV/visible, infrared, and Raman spectroscopy coupled with chemometrics will be discussed for the corona virus infected samples based on the relevant literature. In vibrational spectroscopy studies, due to complexity of the data, multivariate analysis methods are also applied to data. The application of multivariate analysis tools that can be used to extract useful information from the data for diagnostic and characterisation purposes is also included in this review. The reviewed methods include hierarchical cluster analysis, principal component analysis, linear and quadratic discriminant analysis, support vector machine algorithm, and one form of neural networks namely deep learning method. Second, novel treatment methods such as photodynamic therapy and the use of nanoparticles in the in-corona virus therapy will be discussed. Finally, the advantages of novel promising diagnosis and treatment methods in COVID-19, over standard methods will be discussed. One of the main aims of this paper is to encourage the scientific community to explore the potential of this novel tools for their use in corona virus characterization, diagnosis, and treatment.