Microprojectile bombardment of Laminaria japonica gametophytes and rapid propagation of transgenic lines within a bubble-column bioreactor

A human acidic fibroblast growth factor gene, hafgf, was successfully transferred into Laminaria japonica (kelp) gametophytes via microprojectile bombardment using the biolistic PDS-1000/He gene gun. Following phosphinothricin screening, PCR detection and Southern blot analysis, transgenic L. japonica gametophytes were cultivated in an illuminated bubble-column bioreactor to optimize growth conditions. A maximal final dry cell density of 1,695 mg l(-1) was obtained in a batch culture having an initial dry cell density of 129.75 mg l(-1). This was achieved using an aeration rate of 1.08 l air min(-1) l(-1) culture in a medium containing 1.5 mM inorganic nitrate and 0.15 mM phosphate. In addition, the relationship between different nitrogen sources and growth of transgenic gametophytes indicated that both urea and sodium nitrate were effective nitrogen sources for cell growth, while ammonium ions inhibited growth of these gametophytes.

Development of rRNA and rDNA-targeted probes for fluorescence in situ hybridization to detect Heterosigma akashiwo (Raphidophyceae)

Heterosigma akashiwo (Hada) is a fragile, fish-killing alga. Efforts to understand and prevent blooms due to this harmful species to mitigate the impact on aquaculture require the development of methods for rapid and precise identification and quantification, so that adequate warning of a harmful algal bloom may be given. Here, we report the development and application of rRNA and rDNA-targeted oligonucleotide probes for fluorescence in situ hybridization (FISH) to aid in the detection and enumeration of H. akashiwo. The designed probes were species specific, showing no cross-reactivity with four common HAB causative species: Prorocentrum micans Ehrenberg, P. minimum (Pavillard) Schiller, Alexandrium tarmarense (Lebour) Balech, and Skeletonema costatum (Greville) Cleve, or with four other microalgae, including Gymnodinium sp. Stein, Platy-monas cordiformis (Karter) Korsch, Skeletonema sp.1 Greville and Skeletonema sp.2. The rRNA-targeted probe hybridized to cytoplasmic rRNA, showing strong green fluorescence throughout the whole cell, while cells labeled by rDNA-targeted probe exhibited exclusively fluorescent nucleus. The detection protocols were optimized and could be completed within an hour. For rRNA and rDNA probes, about a corresponding 80% and 70% of targeted cells could be identified and quantified during the whole growth circle, despite the inapparent variability in the average probe reactivity. The established FISH was proved promising for specific, rapid, precise, and quantitative detection of H. akashiwo. (C) 2008 Elsevier B.V. All rights reserved.

Determination of aliphatic amines from soil and wastewater of a paper mill by pre-column derivatization using HPLC and tandem mass spectrometry (HPLC-MS/MS)

A pre-column derivatization method for the sensitive determination of aliphatic amines using the labeling reagent 1,2-benzo-3,4-dihydrocarbazole-9-ethyl chloroformate (BCEOC) followed by HPLC with fluorescence detection and APCI/NIS identification in positive-ion mode has been developed. The chromophore of 2-(9-carbazole)-ethyl chloroformate (CEOC) reagent was replaced by the 1,2-benzo-3,4-dihydrocarbazole functional group, which resulted in a sensitive fluorescence derivatizing reagent, BCEOC, that could easily and quickly label amines. Derivatives were stable enough to be efficiently analyzed by HPLC and showed an intense protonated molecular ion corresponding m/z [M + H](+) with APCI/MS in positive-ion mode. The collision induced dissociation of the protonated molecular ion formed characteristic fragment ions at m/z 264.1, m/z 246.0 and m/z 218.1, corresponding to the cleavages of CH2CH2O-CO, CH2CH2-OCO, and N-CH2CH2O bonds. Studies on derivatization conditions demonstrated that excellent derivatization yields close to 100% were observed with a 3 to 4-fold molar reagent excess in acetonitrile solvent, in the presence of borate buffer (pH 9.0) at 40 degrees C for 10 min. In addition, the detection responses for BCEOC derivatives were compared with those obtained with CEOC and FMOC as labeling reagents. The ratios I-BCEOC/I-CEOC and I-BCEOC/I-FMOC were, respectively, 1.40-2.76 and 1.36-2.92 for fluorescence responses (here, I was the relative fluorescence intensity). Separation of the amine derivatives had been optimized on an Eclipse XDB-C-8 column. Detection limits calculated from an 0.10 pmol injection, at a signal-to-noise ratio of 3, were 18.65-38.82 fmol (injection volume 10 mu L for fluorescence detection. The relative standard deviations for intraday determination (n = 6) of standard amine derivatives (50 pmol) were 0.0063-0.037% for retention times and 3.36-6.93% for peak areas. The mean intra-and inter-assay precision for all amines were <5.4% and 5.8%, respectively. The recoveries of amines ranged from 96 to 113%. Excellent linear responses were observed with correlation coefficients of >0.9994. The established method provided a simple and highly sensitive technique for the quantitative analysis of trace amounts of aliphatic amines from biological and natural environmental samples.

景物的三维信息获取及其运动分析

采用模糊熵函数对图象象素分类作出整体最优分类评价,实现了区域分割.利用矩及其函数做为各区域的特征表达,构成以区域为基元的符号特征集并描述图象内容。根据立体图象对间的几何关系,解出各区域(基元)的相对三维坐标。与象索匹配相比较,它可以获得较高精度的三维信息和可描述的景物信息.通过获取不同时空的各区域(基元)三维信息,确定了它们的空间运动状态。联系这些状态,构造出景物中物体间的空间关系和近似模型,实现了对景物的3-D识别和描述。

Vectorizing Face Images by Interpreting Shape and Texture Computations

The correspondence problem in computer vision is basically a matching task between two or more sets of features. In this paper, we introduce a vectorized image representation, which is a feature-based representation where correspondence has been established with respect to a reference image. This representation has two components: (1) shape, or (x, y) feature locations, and (2) texture, defined as the image grey levels mapped onto the standard reference image. This paper explores an automatic technique for "vectorizing" face images. Our face vectorizer alternates back and forth between computation steps for shape and texture, and a key idea is to structure the two computations so that each one uses the output of the other. A hierarchical coarse-to-fine implementation is discussed, and applications are presented to the problems of facial feature detection and registration of two arbitrary faces.

Application of capillary zone electrophoresis in the separation and determination of the curcuminoids in urine

The major components of the plant curcuma longa are the curcuminoids that include curcumin, demethoxycurcumin and bisdemethoxycurcumin. It has been reported the curcuminoids have some important activities. A new CZE method with diode array detection has been developed for the separation and determination of the curcumin, demethoxycurcumin and bisdemethoxycurcumin. Three curcuminoids could be readily separated within 7 min with a 15 mM sodium tetraborate buffer containing 10% methanol (v/v) at pH 10.8, 25 kV and 30 degrees C. The method has been validated and shows good performance with respect to selectivity, reproducibility, linearity, limits of detection and recovery. The proposed method was successfully applied to determine the curcuminoids in urine. (c) 2004 Elsevier B.V. All rights reserved.

Rapid protein identification using monolithic enzymatic microreactor and LC-ESI-MS/MS

A monolithic enzymatic microreactor was prepared in a fused-silica capillary by in situ polymerization of acrylamide, glycidyl methacrylate (GMA) and ethylene dimethacrylate (EDMA) in the presence of a binary porogenic mixture of dodecanol and cyclohexanol, followed by ammonia solution treatment, glutaraldehyde activation and trypsin modification. The choice of acrylamide as co-monomer was found useful to improve the efficiency of trypsin modification, thus, to increase the enzyme activity. The optimized microreactor offered very low back pressure, enabling the fast digestion of proteins flowing through the reactor. The performance of the monolithic microreactor was demonstrated with the digestion of cytochrome c at high flow rate. The digests were then characterized by CE and HPLC-MS/MS with the sequence coverage of 57.7%. The digestion efficiency was found over 230 times as high as that of the conventional method. in addition, for the first time, protein digestion carried out in a mixture of water and ACN was compared with the conventional aqueous reaction using MS/MS detection, and the former solution was found more compatible and more efficient for protein digestion.

ROSSI: Emergence of communication in Robots through Sensorimotor and Social Interaction

ROSSI: Emergence of communication in Robots through Sensorimotor and Social Interaction, T. Ziemke, A. Borghi, F. Anelli, C. Gianelli, F. Binkovski, G. Buccino, V. Gallese, M. Huelse, M. Lee, R. Nicoletti, D. Parisi, L. Riggio, A. Tessari, E. Sahin, International Conference on Cognitive Systems (CogSys 2008), University of Karlsruhe, Karlsruhe, Germany, 2008 Sponsorship: EU-FP7

Rubisco small subunit, chlorophyll a/b-binding protein and sucrose : fructan-6-fructosyl transferase gene expression and sugar status in single barley leaf cells in situ. Cell type specificity and induction by light

Chungui Lu, Olga A. Koroleva, John F. Farrar, Joe Gallagher, Chris J. Pollock, and A. Deri Tomos (2002). Rubisco small subunit, chlorophyll a/b-binding protein and sucrose : fructan-6-fructosyl transferase gene expression and sugar status in single barley leaf cells in situ. Cell type specificity and induction by light. Plant Physiology, 130 (3) pp.1335-1348 Sponsorship: BBSRC RAE2008

A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.

X-ray Diffraction Tomographic Imaging and Reconstruction

Material discrimination based on conventional or dual energy X-ray computed tomography (CT) imaging can be ambiguous. X-ray diffraction imaging (XDI) can be used to construct diffraction profiles of objects, providing new molecular signature information that can be used to characterize the presence of specific materials. Combining X-ray CT and diffraction imaging can lead to enhanced detection and identification of explosives in luggage screening. In this work we are investigating techniques for joint reconstruction of CT absorption and X-ray diffraction profile images of objects to achieve improved image quality and enhanced material classification. The initial results have been validated via simulation of X-ray absorption and coherent scattering in 2 dimensions.

Integrated Chest Image Analysis System "BU-MIA"

We introduce "BU-MIA," a Medical Image Analysis system that integrates various advanced chest image analysis methods for detection, estimation, segmentation, and registration. BU-MIA evaluates repeated computed tomography (CT) scans of the same patient to facilitate identification and evaluation of pulmonary nodules for interval growth. It provides a user-friendly graphical user interface with a number of interaction tools for development, evaluation, and validation of chest image analysis methods. The structures that BU-MIA processes include the thorax, lungs, and trachea, pulmonary structures, such as lobes, fissures, nodules, and vessels, and bones, such as sternum, vertebrae, and ribs.

Bayesian Packet Loss Detection for TCP

One of TCP's critical tasks is to determine which packets are lost in the network, as a basis for control actions (flow control and packet retransmission). Modern TCP implementations use two mechanisms: timeout, and fast retransmit. Detection via timeout is necessarily a time-consuming operation; fast retransmit, while much quicker, is only effective for a small fraction of packet losses. In this paper we consider the problem of packet loss detection in TCP more generally. We concentrate on the fact that TCP's control actions are necessarily triggered by inference of packet loss, rather than conclusive knowledge. This suggests that one might analyze TCP's packet loss detection in a standard inferencing framework based on probability of detection and probability of false alarm. This paper makes two contributions to that end: First, we study an example of more general packet loss inference, namely optimal Bayesian packet loss detection based on round trip time. We show that for long-lived flows, it is frequently possible to achieve high detection probability and low false alarm probability based on measured round trip time. Second, we construct an analytic performance model that incorporates general packet loss inference into TCP. We show that for realistic detection and false alarm probabilities (as are achievable via our Bayesian detector) and for moderate packet loss rates, the use of more general packet loss inference in TCP can improve throughput by as much as 25%.

Learning a Family of Detectors

Object detection and recognition are important problems in computer vision. The challenges of these problems come from the presence of noise, background clutter, large within class variations of the object class and limited training data. In addition, the computational complexity in the recognition process is also a concern in practice. In this thesis, we propose one approach to handle the problem of detecting an object class that exhibits large within-class variations, and a second approach to speed up the classification processes. In the first approach, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly solved with using a multiplicative form of two kernel functions. One kernel measures similarity for foreground-background classification. The other kernel accounts for latent factors that control within-class variation and implicitly enables feature sharing among foreground training samples. For applications where explicit parameterization of the within-class states is unavailable, a nonparametric formulation of the kernel can be constructed with a proper foreground distance/similarity measure. Detector training is accomplished via standard Support Vector Machine learning. The resulting detectors are tuned to specific variations in the foreground class. They also serve to evaluate hypotheses of the foreground state. When the image masks for foreground objects are provided in training, the detectors can also produce object segmentation. Methods for generating a representative sample set of detectors are proposed that can enable efficient detection and tracking. In addition, because individual detectors verify hypotheses of foreground state, they can also be incorporated in a tracking-by-detection frame work to recover foreground state in image sequences. To run the detectors efficiently at the online stage, an input-sensitive speedup strategy is proposed to select the most relevant detectors quickly. The proposed approach is tested on data sets of human hands, vehicles and human faces. On all data sets, the proposed approach achieves improved detection accuracy over the best competing approaches. In the second part of the thesis, we formulate a filter-and-refine scheme to speed up recognition processes. The binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the face recognition grand challenge version 2 data set, hand shape detection and parameter estimation on a hand data set, and vehicle detection and estimation of the view angle on a multi-pose vehicle data set. On all data sets, our approach is at least five times faster than simply evaluating all foreground state hypotheses with virtually no loss in classification accuracy.

Is a Detector Only Good for Detection?

A common design of an object recognition system has two steps, a detection step followed by a foreground within-class classification step. For example, consider face detection by a boosted cascade of detectors followed by face ID recognition via one-vs-all (OVA) classifiers. Another example is human detection followed by pose recognition. Although the detection step can be quite fast, the foreground within-class classification process can be slow and becomes a bottleneck. In this work, we formulate a filter-and-refine scheme, where the binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the FRGC V2 data set, hand shape detection and parameter estimation on a hand data set and vehicle detection and view angle estimation on a multi-view vehicle data set. On all data sets, our approach has comparable accuracy and is at least five times faster than the brute force approach.