Biosensors for marine applications. We all need the sea, but does the sea need biosensors?

The aim of the paper is to explain the rationale behind marine biosensor applications, give an overview of measurement strategies currently employed, summarise some of the relevant available biosensor technology as well as instrumentation requirements for marine sensors and attempt a forward look at what the future might hold in terms of needs and developments. Application areas considered are eutrophication, organism detection, food safety, pollutants, trace metals and ecotoxicology. The drivers for many of these studies are discussed and the policy environment for current and future measurements is outlined.     

Integrating models with data in ecology and palaeoecology: advances towards a model-data fusion approach

It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process-based ecological models and data in cohesive, systematic ways. Model-data fusion (MDF) is an emerging area of research in ecology and palaeoecology. It provides a new quantitative approach that offers a high level of empirical constraint over model predictions based on observations using inverse modelling and data assimilation (DA) techniques. Increasing demands to integrate model and data methods in the past decade has led to MDF utilization in palaeoecology, ecology and earth system sciences. This paper reviews key features and principles of MDF and highlights different approaches with regards to DA. After providing a critical evaluation of the numerous benefits of MDF and its current applications in palaeoecology (i.e., palaeoclimatic reconstruction, palaeovegetation and palaeocarbon storage) and ecology (i.e. parameter and uncertainty estimation, model error identification, remote sensing and ecological forecasting), the paper discusses method limitations, current challenges and future research direction. In the ongoing data-rich era of today's world, MDF could become an important diagnostic and prognostic tool in which to improve our understanding of ecological processes while testing ecological theory and hypotheses and forecasting changes in ecosystem structure, function and services.     

Integration of distributed multi-analyzer monitoring and control in bioprocessing based on a real-time expert system

A computer system solution for integration of a distributed bioreactor monitoring and control instrumentation on the laboratory scale is described. Bioreactors equipped with on-line analyzers for mass spectrometry, near-infrared spectroscopy, electrochemical probes and multi-array gas sensors and their respective software were networked through a real-time expert systems platform. The system allowed data transmission of more than 1800 different signals from the instrumentation, including signals from gas sensors, electrodes, spectrometer detectors, balances, flowmeters, etc., and were used for processing and carrying out a number of computational tasks such as partial least-square regression, principal component analysis, artificial neural network modelling, heuristic decision-making and adaptive control. The system was demonstrated on different cultivations/fermentations which illustrated sensor fusion control, multivariate statistical process monitoring, adaptive glucose control and adaptive multivariate control. The performance of these examples showed high operational stability and reliable function and meet typical requirements for production safety and quality.     

基于EMR数据挖掘的临床路径构建中EMR数据预处理

高质量决策取决于高质量数据。与其他领域的数据挖掘一样,在基于电子病历数据挖掘构建临床路径中,也需要对拟挖掘的电子病历数据进行预处理,为最终的数据挖掘提供干净、准确、更有针对性的数据,从而提高挖掘的效率和准确度,但对于基于EMR数据挖掘的临床数据构建中的电子病历预处理又有其自身的特点。文章对此进行了分析与探索。     

Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices

Biological and medical diagnoses depend on high-quality measurements. A wearable device based on Internet of Things (IoT) must be unobtrusive to the human body to encourage users to accept continuous monitoring. However, unobtrusive IoT devices are usually of low quality and unreliable because of the limitation of technology progress that has slowed down at high peak. Therefore, advanced inference techniques must be developed to address the limitations of IoT devices. This review proposes that IoT technology in biological and medical applications should be based on a new data assimilation process that fuses multiple data scales from several sources to provide diagnoses. Moreover, the required technologies are ready to support the desired disease diagnosis levels, such as hypothesis test, multiple evidence fusion, machine learning, data assimilation, and systems biology. Furthermore, cross-disciplinary integration has emerged with advancements in IoT. For example, the multiscale modeling of systems biology from proteins and cells to organs integrates current developments in biology, medicine, mathematics, engineering, artificial intelligence, and semiconductor technologies. Based on the monitoring objectives of IoT devices, researchers have gradually developed ambulant, wearable, noninvasive, unobtrusive, low-cost, and pervasive monitoring devices with data assimilation methods that can overcome the limitations of devices in terms of quality measurement. In the future, the novel features of data assimilation in systems biology and ubiquitous sensory development can describe patients’ physical conditions based on few but long-term measurements.     

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Advanced micrometeorological methods have become increasingly important in soil, crop, and environmental sciences. For many scientists without formal training in atmospheric science, these techniques are relatively inaccessible. Surface renewal and other flux measurement methods require an understanding of boundary layer meteorology and extensive training in instrumentation and multiple data management programs. To improve accessibility of these techniques, we describe the underlying theory of surface renewal measurements, demonstrate how to set up a field station for surface renewal with eddy covariance calibration, and utilize our open-source turnkey data logger program to perform flux data acquisition and processing. The new turnkey program returns to the user a simple data table with the corrected fluxes and quality control parameters, and eliminates the need for researchers to shuttle between multiple processing programs to obtain the final flux data. An example of data generated from these measurements demonstrates how crop water use is measured with this technique. The output information is useful to growers for making irrigation decisions in a variety of agricultural ecosystems. These stations are currently deployed in numerous field experiments by researchers in our group and the California Department of Water Resources in the following crops: rice, wine and raisin grape vineyards, alfalfa, almond, walnut, peach, lemon, avocado, and corn.     

我国心血管专科临床科研数据中心的建设与思考

中国是心血管疾病的数据大国,但是从数据质量和临床科研上讲,并非强国。为了提高心血管数据在临床质量评估和科研中的应用,我院心内科开始了心血管专科临床科研数据中心的尝试和建设。通过借鉴美国相关经验,以及对我国心血管临床质量评估以及科研需求的深入调研,我们建设了以心血管专科临床数据仓库以及心血管注册数据中心为核心的心血管专科临床科研数据中心。在此基础上,进一步探索并建设了自定义科研表单,多维度组合筛选,基于随访策略的心血管随访管理,基于个性化诊疗计划的心血管病人院后管理,相关性分析及不良心血管事件模式发现等应用和工具,有助于提升心血管专科的临床流程效率,临床质量评估和科研水平,同时为建立全国性的心血管疾病注册数据库打下了基础。     

The use of gas-sensor arrays to diagnose urinary tract infections

Sensorial analysis based on the utilisation of human senses, is one of the most important and straightforward investigation methods in food and chemical analysis. An electronic nose has been used to detect in vivo Urinary Tract Infections from 45 suspected cases that were sent for analysis in a UK Health Laboratory environment. These samples were analysed by incubation in a volatile generation test tube system for 4-5 h. The volatile production patterns were then analysed using an electronic nose system with 14 conducting polymer sensors. An intelligent model consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on learning techniques has been considered. The implementation of an Extended Normalised Radial Basis Function network with advanced features for determining its size and parameters and the concept of fusion of multiple classifiers dedicated to specific feature parameters has been also adopted in this study. The proposed scheme achieved a very high classification rate of the testing dataset, demonstrating in this way the efficiency of the proposed scheme compared with other approaches. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology.     

Parameterized specification, configuration and execution of data-intensive scientific workflows

Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multidimensional parameter space consisting of input performance parameters to the applications that are known to affect their execution times. While some performance parameters such as grouping of workflow components and their mapping to machines do not affect the accuracy of the analysis, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple such parameters. Using two real-world applications in the spatial, multidimensional data analysis domain, we present an experimental evaluation of the proposed framework.     

High Throughput Multispectral Image Processing with Applications in Food Science

Recently, machine vision is gaining attention in food science as well as in food industry concerning food quality assessment and monitoring. Into the framework of implementation of Process Analytical Technology (PAT) in the food industry, image processing can be used not only in estimation and even prediction of food quality but also in detection of adulteration. Towards these applications on food science, we present here a novel methodology for automated image analysis of several kinds of food products e.g. meat, vanilla crème and table olives, so as to increase objectivity, data reproducibility, low cost information extraction and faster quality assessment, without human intervention. Image processing’s outcome will be propagated to the downstream analysis. The developed multispectral image processing method is based on unsupervised machine learning approach (Gaussian Mixture Models) and a novel unsupervised scheme of spectral band selection for segmentation process optimization. Through the evaluation we prove its efficiency and robustness against the currently available semi-manual software, showing that the developed method is a high throughput approach appropriate for massive data extraction from food samples.     

A low-cost, portable generic biotoxicity assay for environmental monitoring applications

Fish chromatophores have been shown to be promising biosensors for the detection of hostile agents in the environment. However, state-of-art methods for such applications are still based on extensive use of data/signal processing, in conjunction with need for a skilled human observer to carry out the detection. As a result, conventional methods are complex, costly and cumbersome rendering them useless for field applications requiring low-cost portable solutions capable of fast detection. A new technique is proposed based on the popular scheme of observing the aggregation response in chromatophores for detection of toxicity, and a solution using optical detection and electronic processing is outlined. This scheme has the advantage of being low in cost while providing simple, fast and reliable detection.     

Device Attitude and Real-Time 3D Visualization: An Interface for Elderly Care

Objectivethis paper presents an innovative graphical user interface to visualize the attitude of a sensing device in a three-dimensional space, serving a wide-range of medical applications.Material and methodsbased on inertial measurement units (IMU) or on magnetic, angular rate and gravity (MARG) sensors, a processing unit provides Euler angles using a sensor fusion technique to display the orientation of the device relative to the Earth frame in real-time. The device is schematized by linking six polygonal regions, and is subject to sequential rotations by updating the graph each 350 ms. We conduct comparative studies between the two sensing devices, i.e. IMUs and MARGs, as well as two orientation filters, namely Madgwick's algorithm and Mahony's algorithm.Resultsthe accuracy of the system is reported as a function of (i) the sampling frequency, (ii) the sensing unit, and (iii) the orientation filter, following two elderly care applications, namely fall risk assessment and body posture monitoring. The experiments are conducted using public datasets. The corresponding results show that Madgwick's algorithm is best suited for low sampling rates, whereas MARG sensors are best suited for the detection of postural transitions.Conclusionthis paper addresses the different aspects and discusses the limitations of attitude estimation systems, which are important tools to help clinicians in their diagnosis.     

Data fusion-based assessment of raw materials in mammalian cell culture

In mammalian cell culture producing therapeutic proteins, one of the important challenges is the use of several complex raw materials whose compositional variability is relatively high and their influences on cell culture is poorly understood. Under these circumstances, application of spectroscopic techniques combined with chemometrics can provide fast, simple, and non‐destructive ways to evaluate raw material quality, leading to more consistent cell culture performance. In this study, a comprehensive data fusion strategy of combining multiple spectroscopic techniques is investigated for the prediction of raw material quality in mammalian cell culture. To achieve this purpose, four different spectroscopic techniques of near‐infrared, Raman, 2D fluorescence, and X‐ray fluorescence spectra were employed for comprehensive characterization of soy hydrolysates which are commonly used as supplements in culture media. First, the different spectra were compared separately in terms of their prediction capability. Then, ensemble partial least squares (EPLS) was further employed by combining all of these spectral datasets in order to produce a more accurate estimation of raw material properties, and compared with other data fusion techniques. The results showed that data fusion models based on EPLS always exhibit best prediction accuracy among all the models including individual spectroscopic methods, demonstrating the synergetic effects of data fusion in characterizing the raw material quality. Biotechnol. Bioeng. 2012; 109: 2819–2828. © 2012 Wiley Periodicals, Inc.     

基于多源遥感数据的生态保护修复项目区监测方法评述

回顾了山水林田湖草生态保护修复项目的实施背景,针对生态保护修复项目监测监管范围广、技术难等问题,强调了基于多源遥感数据开展项目遥感监测的重要性与必要性。从监测指标拟定、遥感地物信息提取、多源遥感数据融合、动态变化检测等方面评述了基于多源遥感数据的生态保护修复项目区监测方法,包括基于中高空间分辨率遥感数据的地物信息提取、融合机器学习的非线性混合像元分析、基于混合像元分析的时空融合等。在总结技术和工作推进方面的优势、局限基础上,提出要结合实际工作,持续优化国土空间生态保护修复监测指标;充分挖掘遥感数据解析的相关算法潜力,提升地物信息提取和混合像元分析的精度;加强时空融合算法与变化检测方法的研究探索,加强相关方法的实践应用;以“山水林田湖草生态保护修复工程试点”项目为平台,建立稳定的国土空间生态保护修复遥感监测运行机制,加强科技创新,形成技术标准,指导工作开展。     

Do's and Don'ts of Cryo-electron Microscopy: A Primer on Sample Preparation and High Quality Data Collection for Macromolecular 3D Reconstruction

Cryo-electron microscopy (cryoEM) entails flash-freezing a thin layer of sample on a support, and then visualizing the sample in its frozen hydrated state by transmission electron microscopy (TEM). This can be achieved with very low quantity of protein and in the buffer of choice, without the use of any stain, which is very useful to determine structure-function correlations of macromolecules. When combined with single-particle image processing, the technique has found widespread usefulness for 3D structural determination of purified macromolecules. The protocol presented here explains how to perform cryoEM and examines the causes of most commonly encountered problems for rational troubleshooting; following all these steps should lead to acquisition of high quality cryoEM images. The technique requires access to the electron microscope instrument and to a vitrification device. Knowledge of the 3D reconstruction concepts and software is also needed for computerized image processing. Importantly, high quality results depend on finding the right purification conditions leading to a uniform population of structurally intact macromolecules. The ability of cryoEM to visualize macromolecules combined with the versatility of single particle image processing has proven very successful for structural determination of large proteins and macromolecular machines in their near-native state, identification of their multiple components by 3D difference mapping, and creation of pseudo-atomic structures by docking of x-ray structures. The relentless development of cryoEM instrumentation and image processing techniques for the last 30 years has resulted in the possibility to generate de novo 3D reconstructions at atomic resolution level.     

An integrated and sensitive detection platform for magneto-resistive biosensors

A compact biosensor platform with giant magneto-resistive (GMR) sensors suited for the detection of superparamagnetic nanoparticle labels is presented. The platform consist of disposable biosensor cartridges and an electronic reader, which enables quantitative detection with high analytical performance, combined with robustness, ease of use and at low cost. In order to optimise the signal-to-noise ratio (SNR), magnetic labels are excited at high frequency. Wires, integrated in the silicon of the sensor chip are used to generate a well-defined magnetic field on the sensor surface, thus removing the need for mechanical alignment with external apparatus. A signal modulation scheme is applied to obtain optimal detection accuracy. The platform is scalable and can be adapted according to application-specific requirements. Experimental results indicate that three beads of 300 nm diameter can be detected on a sensor surface of 1500 microm2 for a measurement time of 1s.     

Sample preparation for mass spectrometry imaging: small mistakes can lead to big consequences

Mass spectrometry imaging (MSI) enables the direct analysis of molecules from the surface of a wide variety of samples, allowing the multiplex measurement of both abundance and distribution of small molecules, lipids, peptides and proteins. As the technology has been refined an increasing number of ionization methods and mass analyzers has been used that enable increased spatial and spectral resolution measurements to be made at an increased speed. Alongside the instrumentation improvements there has been optimization of sample preparation procedures that allow the highest quality data to be obtained, reproducibly, from an ever increasing diversity of samples. This review will consider the development and standardization of sample preparation methods applicable to MSI, describing the stages and procedures undertaken from the instance of sample collection, through storage, preparation and on through final processing prior to analysis. Recent technical advancements will be highlighted and areas where further experimentation and optimization may well be required will be described. All aspects of the sample preparation pipeline will be considered in detail, with examples from the literature used to emphasize why rigorous sample preparation for MSI is vital to achieve the most accurate, reproducible and validated MSI data possible.