A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling

Chinese Academy of Sciences ; National Science Foundation of China [41071059]; National Key Technology R&D Program of China [2008BAK50B06-02]; National Basic Research Program of China [2010CB950900, 2010CB950704]; Natural Sciences and Engineering Research Council of Canada

Studies on the fusion heat of poly(ether ether ketone ketone)-poly(ether biphenyl ether ketone ketone) copolymers

On the basis of DSC measurements, the Delta H-f(0) values of the fusion heat for PEEKK-PEBEKK copolymers with various biphenyl contents were obtained by using thermodynamics statistical theory proposed by Flory and graphical method of the specific volume-fusion heat. The results reveal that Delta H-f(0) values determined by these two methods for PEEKK-PEBEKK copolymers with various biphenyl content are nearly the same, and that Delta H-f(0) values are closely dependent on biphenyl content. Delta H-f(0) value is minimum at n(B)=0.35.

Lineage-specific domain fusion in the evolution of purine nucleotide cyclases in cyanobacteria

Cyclic nucleotides (both cAMP and cGMP) play extremely important roles in cyanobacteria, such as regulating heterocyst formation, respiration, or gliding. Catalyzing the formation of cAMP and cGMP from ATP and GTP is a group of functionally important enzymes named adenylate cyclases and guanylate cyclases, respectively. To understand their evolutionary patterns, in this study, we presented a systematic analysis of all the cyclases in cyanobacterial genomes. We found that different cyanobacteria had various numbers of cyclases in view of their remarkable diversities in genome size and physiology. Most of these cyclases exhibited distinct domain architectures, which implies the versatile functions of cyanobacterial cyclases. Mapping the whole set of cyclase domain architectures from diverse prokaryotic organisms to their phylogenetic tree and detailed phylogenetic analysis of cyclase catalytic domains revealed that lineage-specific domain recruitment appeared to be the most prevailing pattern contributing to the great variability of cyanobacterial cyclase domain architectures. However, other scenarios, such as gene duplication, also occurred during the evolution of cyanobacterial cyclases. Sequence divergence seemed to contribute to the origin of putative guanylate cyclases which were found only in cyanobacteria. In conclusion, the comprehensive survey of cyclases in cyanobacteria provides novel insight into their potential evolutionary mechanisms and further functional implications.

An Obstacle Cluster Based Data Fusion Method for Mobile Robot

首先给出了一种通过融合多个超声波传感器和一台激光全局定位系统的数据建立机器人环境地图的方法 ,并在此基础上 ,首次提出了机器人在非结构环境下识别障碍物的一种新方法 ,即基于障碍物群的方法 .该方法的最大特点在于它可以更加简洁、有效地提取和描述机器人的环境特征 ,这对于较好地实现机器人的导航、避障 ,提高系统的自主性和实时性是至关重要的 .大量的实验结果表明了该方法的有效性 .

Multi-sensors data acquisition and information fusion for manned submersible vehicle

本文介绍了一种用于载人潜水器的导航传感器的数据采集及信息融合技术。航行控制计算机通过基于工业以太网的数据通信系统对各传感器进行数据采集,采用卡尔曼滤波器完成对各传感器数据信息的融合,以便提高数据的精度和控制系统的性能,并将结果送给监控计算机,用于载人潜水器的姿态显示。

Purification and characterization of recombinant truncated human interleukin-11 expressed as fusion protein in Escherichia coli

Mature human interleukin-11 (HuIL-11) is a cytokine consisting of 178 amino acid residues that results from scission of the N-terminal signal peptide, consisting of 21 amino acid residaues, from the corresponding nascent polypeptide. A DNA fragment encoding a truncated HuIL-11 (trHuIL-11), with an additional 5 amino acid residues removed from the N-terminus, was cloned into vector pGEX-2T between the BamHI site and the EcoRI site. Upon transformation with Escherichia coli BL21, the construct over-produced a glutathione S-transferase (GST)-fused protein in a soluble form after IPTG induction. The fusion protein was initially fractionated with butyl-Sepharose 4 fast flow column and by affinity chromatography using a GSH-Sepharose 4B column. On-site enzymatic release with thrombin gave the target protein at 96% purity as judged by SDS-PAGE and HPLC. Expression of the interleukin as a GST-fused protein thus greatly improved downstream processing. Subsequent biological activity assay suggested that trHuIL-11 had similar activity profile to the naturally produced sample and may be a promising candidate for further development as biopharmaceutical.

Heat capacity and enthalpy of fusion of fenoxycarb

Fenoxycarb was synthesized and its heat capacities were precisely measured with an automated adiabatic calorimeter over the temperature range from 79 to 360 K. The sample was observed to melt at (326.31 +/- 0.14) K. The molar enthalpy and entropy of fusion as well as the chemical purity of the compound were determined to be (26.98 +/- 0.04) kJ-mol(-1), (82.69 +/- 0.09) J-K-1-mol(-1) and 99.53% +/- 0.01%, respectively. The thermodynamic functions relative to the reference temperature (298.15 K) were calculated based on the heat capacity measurements in the temperature range between 80 and 360 K. The extrapolated melting temperature for the absolutely pure compound obtained from fractional melting experiments was (326.62 +/- 0.06) K. Further research on the melting process of this compound was carried out by means of differential scanning calorimetry technique. The result was in agreement with that obtained from the measurements of heat capacities.

Information Fusion and Hierarchical Knowledge Discovery by ARTMAP Neural Networks

Mapping novel terrain from sparse, complex data often requires the resolution of conflicting information from sensors working at different times, locations, and scales, and from experts with different goals and situations. Information fusion methods help resolve inconsistencies in order to distinguish correct from incorrect answers, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods developed here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an objects class is car, vehicle, or man-made. Underlying relationships among objects are assumed to be unknown to the automated system of the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchial knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples.

Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.

Fusion Artmap: A Neural Network Architecture for Multi-Channel Data Fusion and Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ARTMAP during supervised learning. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, become inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called paraellel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of them. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network. Fusion ARTMAP's multi-channel coding is illustrated by simulations of the Quadruped Mammal database.

Fusion ARTMAP: An Adaptive Fuzzy Network for Multi-Channel Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Fusion ARTMAP generalizes the fuzzy ARTMAP architecture in order to adaptively classify multi-channel data. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, beco1ne inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called parallel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of thmn. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network.

Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.