The potential of cloud point system as a novel two-phase partitioning system for biotransformation

Although the extractive biotransformation in two-phase partitioning systems have been studied extensively, such as the water–organic solvent two-phase system, the aqueous two-phase system, the reverse micelle system, and the room temperature ionic liquid, etc., this has not yet resulted in a widespread industrial application. Based on the discussion of the main obstacles, an exploitation of a cloud point system, which has already been applied in a separation field known as a cloud point extraction, as a novel two-phase partitioning system for biotransformation, is reviewed by analysis of some topical examples. At the end of the review, the process control and downstream processing in the application of the novel two-phase partitioning system for biotransformation are also briefly discussed.     

Light traffic: photo-crosslinking a novel transport system

The Tat protein transporter is found in the membranes of many bacteria and in plant chloroplasts. This highly unusual transport machine moves folded and often oligomeric substrate proteins across energy-conserving membranes. A recent paper reports the first use of a photo-crosslinking approach to dissect the early recognition events between the transporter and its substrate.     

The big data analytics and applications of the surveillance system using video structured description technology

Recently, the video data has very huge volume, taking one city for example, thousands of cameras are built of which each collects high-definition video over 24–48 GB every day with the rapidly growth; secondly, data collected includes variety of formats involving multimedia, images and other unstructured data; furthermore the valuable information contains in only a few frames called key frames of massive video data; and the last problem caused is how to improve the processing velocity of a large amount of original video with computers, so as to enhance the crime prediction and detection effectiveness of police and users. In this paper, we conclude a novel architecture for next generation public security system, and the “front + back” pattern is adopted to address the problems brought by the redundant construction of current public security information systems which realizes the resource consolidation of multiple IT resources, and provides unified computing and storage environment for more complex data analysis and applications such as data mining and semantic reasoning. Under the architecture, we introduce cloud computing technologies such as distributed storage and computing, data retrieval of huge and heterogeneous data, provide multiple optimized strategies to enhance the utilization of resources and efficiency of tasks. This paper also presents a novel strategy to generate a super-resolution image via multi-stage dictionaries which are trained by a cascade training process. Extensive experiments on image super-resolution validate that the proposed solution can get much better results than some state-of-the-arts ones.     

Applications of a novel biodetection system to saliva using protein fingerprints with data processing

A fundamental method has been developed focusing on a facile and rapid examination of periodontal disease. Periodontal disease is an oral disease thought to affect 80% of adults, and early detection with treatment is desirable for the improvement of the quality of life. Unfortunately conventional methods are not consistent as the disease is caused by a number of undefined bacteria and detection relies on the skills of the dentist. Thus an objective detection system is required. We have performed an experiment on saliva using a novel biodetection system, designated PepTenChip®. A disease model for saliva was prepared using a specimen from a healthy subject and a mixture of hemoglobin (f-Hb) and lactate dehydrogenase (LDH), which is used as a periodontal disease marker protein with healthy saliva. PepTenChip® is a peptide microarray in which fluorescent labelled structured peptides are immobilized on a novel amorphous carbon substrate. Since the peptides used as capture molecules are fluorescently labelled, labeling of analytes is not necessary. The fluorescence intensity change before and after application of analytes are detected rather than the ON/OFF detection common to conventional microarrays using a set of antigen–antibody. The fluorescence intensity value changes according to the concentration of captured protein allowing the generation of protein fingerprint (PFP) and dendrograms. The present method does not rely on a “one to one” interaction, unlike conventional biodetection, and advantages can be envisaged in the case of an undefined or unknown cause of disease. The statistical analyses, such as multivariate analyses, allow classification of the type of proteins added in saliva as mimetics of disease. PepTenChip® system is useful and convenient for examination of periodontal disease in health care.     

Automatic fetal biometry prediction using a novel deep convolutional network architecture

PurposeFetal biometric measurements face a number of challenges, including the presence of speckle, limited soft-tissue contrast and difficulties in the presence of low amniotic fluid. This work proposes a convolutional neural network for automatic segmentation and measurement of fetal biometric parameters, including biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL) from ultrasound images that relies on the attention gates incorporated into the multi-feature pyramid Unet (MFP-Unet) network.MethodsThe proposed approach, referred to as Attention MFP-Unet, learns to extract/detect salient regions automatically to be treated as the object of interest via the attention gates. After determining the type of anatomical structure in the image using a convolutional neural network, Niblack's thresholding technique was applied as pre-processing algorithm for head and abdomen identification, whereas a novel algorithm was used for femur extraction. A publicly-available dataset (HC18 grand-challenge) and clinical data of 1334 subjects were utilized for training and evaluation of the Attention MFP-Unet algorithm.ResultsDice similarity coefficient (DSC), hausdorff distance (HD), percentage of good contours, the conformity coefficient, and average perpendicular distance (APD) were employed for quantitative evaluation of fetal anatomy segmentation. In addition, correlation analysis, good contours, and conformity were employed to evaluate the accuracy of the biometry predictions. Attention MFP-Unet achieved 0.98, 1.14 mm, 100%, 0.95, and 0.2 mm for DSC, HD, good contours, conformity, and APD, respectively.ConclusionsQuantitative evaluation demonstrated the superior performance of the Attention MFP-Unet compared to state-of-the-art approaches commonly employed for automatic measurement of fetal biometric parameters.     

Single-cell viability assessment with a novel spectro-imaging system

Single-cell viability assessment by means of plural dye probes require the spectral and temporal analysis of microscopic images of the test cells. To meet this requirement, we have developed a simple and compact spectro-imaging system using an image slicer and a grism. The image slicer was made of a bundle of 100 optical fibers. The field of view is divided into 10 x 10 sections. The spectral data of each section could be recorded every 5 s in the range from 400 to 800 nm at 5 nm resolution. The viability changes of yeast or tobacco single-cells were measured with this system. Using BY-2 cells, for example, the response to a chemical stress of saponin was measured by means of two fluorescent probes. The spectral-spatial-temporal data of fluorescein and DNA bound ethidium bromide provided us with useful information about the dynamic change of cell membrane permeability from which the cell viability was assessed.     

基于多传感器的声景大数据在线采集与分析系统研究

声景包含重要的生态信息,具有实时性强、信息密度高的特点,有重要研究价值。现有的声景研究中,音频及相关环境参数采集和分析仍需要大量的人工作业,耗时耗力。基于多传感集成、边缘计算和深度学习技术,建立了一套声景大数据在线采集与分析系统,包括边缘计算节点和中心计算服务器。并通过3个实验站点,进行了近1年的技术验证,实现了声景大数据的自动化在线采集、传输和分析。该系统能适应户外恶劣的自然环境,能根据任务需求持续不断地进行声景大数据在线采集和分析,稳定性好。声学指数可以反映声景变化,但因指数侧重点不同,不同的声学指数之间变化特征差异较大,需要组合使用。通过声纹特征图能直观地识别出不同发声源,对物种的快速识别、声源的分类等具有较强的借鉴意义。系统借助VGGish网络提取的高维声景特征图能很好地识别不同站点和不同时间的声景变化,在不同站点和昼夜上具有较高的区分精度,有快速和直观地反映不同生态系统的类型特征、生态系统动态变化的潜力。丰富声纹特征库、优化声景特征分析神经网络、建设声景长期监测共享网络,有助于扩展系统在物种识别、生物多样性快速分析、生物与环境相互作用机制方面的应用。研究为声景大数据的在线采集...     

Geographical information system parallelization for spatial big data processing: a review

With the increasing interest in large-scale, high-resolution and real-time geographic information system (GIS) applications and spatial big data processing, traditional GIS is not efficient enough to handle the required loads due to limited computational capabilities.Various attempts have been made to adopt high performance computation techniques from different applications, such as designs of advanced architectures, strategies of data partition and direct parallelization method of spatial analysis algorithm, to address such challenges. This paper surveys the current state of parallel GIS with respect to parallel GIS architectures, parallel processing strategies, and relevant topics. We present the general evolution of the GIS architecture which includes main two parallel GIS architectures based on high performance computing cluster and Hadoop cluster. Then we summarize the current spatial data partition strategies, key methods to realize parallel GIS in the view of data decomposition and progress of the special parallel GIS algorithms. We use the parallel processing of GRASS as a case study. We also identify key problems and future potential research directions of parallel GIS.     

The system architecture of the BioPath system

BioPath is a prototype system for the interactive exploration of biochemical pathways. It has been developed as an electronic version of the famous Boehringer Biochemical Pathways map and offers various ways to access information on substances and pathways and to navigate through pathways. This paper describes the main features and the software architecture of BioPath. The companion paper [11] focuses on the advanced visualization incorporated into BioPath.     

Trend analysis of variations in carbon stock using stock big data

Changes in land use affect the terrestrial carbon stock through changes in the land cover. Research on land use and analysis of variations in carbon stock have practical applications in the optimization of land use and the mitigation of climate change effects. This study was conducted in Baixiang and Julu counties in the Taihang Piedmont by employing the trend analysis method to characterize the variation in county land use and carbon stock. The findings show that in both counties, agricultural and unused land areas decreased while built-up land area increased, and the reduction in cropland was the main reason behind the agricultural land reduction. An inflection point appeared on the cropland curves of Julu, because the cropland area decreased by 1576.97 hm\(^{2}\) from 2004 to 2006. Cropland area in Baixiang decreased from 1996 to 1998 by a total of 129.89 hm\(^{2}\) and then remained relatively stable after 1998. The total carbon storage and variation in land use in the two counties displayed similar trends. Total carbon reserves in Julu increased by 2.76 \(\times \) 10\(^{4}\) tC (carbon equivalent), while those in Baixiang decreased by 0.63 \(\times \) 10\(^{4}\) tC. Carbon stock of built-up land in Julu and Baixiang increased by 2.44 \(\times \) 10\(^{4}\) and 1.22 \(\times \) 10\(^{4}\) tC, respectively.     

Unsupervised assessment of microarray data quality using a Gaussian mixture model

Background  

Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny.     

Outline of a novel architecture for cortical computation

In this paper a novel architecture for cortical computation has been proposed. This architecture is composed of computing paths consisting of neurons and synapses. These paths have been decomposed into lateral, longitudinal and vertical components. Cortical computation has then been decomposed into lateral computation (LaC), longitudinal computation (LoC) and vertical computation (VeC). It has been shown that various loop structures in the cortical circuit play important roles in cortical computation as well as in memory storage and retrieval, keeping in conformity with the molecular basis of short and long term memory. A new learning scheme for the brain has also been proposed and how it is implemented within the proposed architecture has been explained. A few mathematical results about the architecture have been proposed, some of which are without proof.     

Automating data manipulation for genetic analysis using a data base management system

Inefficient coding and manipulation of pedigree data have often hindered the progress of genetic studies. In this paper we present the methodology for interfacing a data base management system (DBMS) called MEGADATS with a linkage analysis program called LIPED. Two families that segregate a dominant trait and one test marker were used in a simulated exercise to demonstrate how a DBMS can be used to automate tedious clerical steps and improve the efficiency of a genetic analysis. The merits of this approach to data management are discussed. We conclude that a standardized format for genetic analysis programs would greatly facilitate data analysis.     

A novel image-analysis toolbox enabling quantitative analysis of root system architecture

We present in this paper a novel, semiautomated image-analysis software to streamline the quantitative analysis of root growth and architecture of complex root systems. The software combines a vectorial representation of root objects with a powerful tracing algorithm that accommodates a wide range of image sources and quality. The root system is treated as a collection of roots (possibly connected) that are individually represented as parsimonious sets of connected segments. Pixel coordinates and gray level are therefore turned into intuitive biological attributes such as segment diameter and orientation as well as distance to any other segment or topological position. As a consequence, user interaction and data analysis directly operate on biological entities (roots) and are not hampered by the spatially discrete, pixel-based nature of the original image. The software supports a sampling-based analysis of root system images, in which detailed information is collected on a limited number of roots selected by the user according to specific research requirements. The use of the software is illustrated with a time-lapse analysis of cluster root formation in lupin (Lupinus albus) and an architectural analysis of the maize (Zea mays) root system. The software, SmartRoot, is an operating system-independent freeware based on ImageJ and relies on cross-platform standards for communication with data-analysis software.     

DIG-1, a novel giant protein, non-autonomously mediates maintenance of nervous system architecture

Dedicated mechanisms exist to maintain the architecture of an animal's nervous system after development is completed. To date, three immunoglobulin superfamily members have been implicated in this process in the nematode Caenorhabditis elegans: the secreted two-Ig domain protein ZIG-4, the FGF receptor EGL-15 and the L1-like SAX-7 protein. These proteins provide crucial information for neuronal structures, such as axons, that allows them to maintain the precise position they acquired during development. Yet, how widespread this mechanism is throughout the nervous system, and what other types of factors underlie such a maintenance mechanism, remains poorly understood. Here, we describe a new maintenance gene, dig-1, that encodes a predicted giant secreted protein containing a large number of protein interaction domains. With 13,100 amino acids, the DIG-1 protein is the largest secreted protein identifiable in any genome database. dig-1 functions post-developmentally to maintain axons and cell bodies in place within axonal fascicles and ganglia. The failure to maintain axon and cell body position is accompanied by defects in basement membrane structure, as evidenced by electron microscopy analysis of dig-1 mutants. Expression pattern and mosaic analysis reveals that dig-1 is produced by muscles to maintain nervous system architecture, demonstrating that dig-1 functions non-autonomously to preserve the proper layout of neural structures. We propose that DIG-1 is a component of the basement membrane that mediates specific contacts between cellular surfaces and their environment through the interaction with a cell-type specific set of other maintenance factors.     

Molecular risk assessment and epidemiological typing of Shiga toxin-producing Escherichia coli by using a novel PCR binary typing system

Shiga toxin-producing Escherichia coli (STEC) is a zoonotic pathogen that causes diarrheal disease in humans and is of public health concern because of its ability to cause outbreaks and severe disease such as hemorrhagic colitis or hemolytic-uremic syndrome. More than 400 serotypes of STEC have been implicated in outbreaks and sporadic human disease. The aim of this study was to develop a PCR binary typing (P-BIT) system that could be used to aid in risk assessment and epidemiological studies of STEC by using gene targets that would represent a broad range of STEC virulence genes. We investigated the distribution of 41 gene targets in 75 O157 and non-O157 STEC isolates and found that P-BIT provided 100% typeability for isolates, gave a diversity index of 97.33% (compared with 99.28% for XbaI pulsed-field gel electrophoresis [PFGE] typing), and produced 100% discrimination for non-O157 STEC isolates. We identified 24 gene targets that conferred the same level of discrimination and produced the same cluster dendrogram as the 41 gene targets initially examined. P-BIT clustering identified O157 from non-O157 isolates and identified seropathotypes associated with outbreaks and severe disease. Numerical analysis of the P-BIT data identified several genes associated with human or nonhuman sources as well as high-risk seropathotypes. We conclude that P-BIT is a useful approach for subtyping, offering the advantage of speed, low cost, and potential for strain risk assessment that can be used in tandem with current molecular typing schema for STEC.