Purification of a ligand for the EPH-like receptor HEK using a biosensor-based affinity detection approach.

Advances in screening technologies allowing the identification of growth factor receptors solely by virtue of DNA or protein sequence comparison call for novel methods to isolate corresponding ligand growth factors. The EPH-like receptor tyrosine kinase (RTK) HEK (human EPH-like kinase) was identified previously as a membrane antigen on the LK63 human pre-B-cell line and overexpression in leukemic specimens and cell lines suggested a role in oncogenesis. We developed a biosensor-based approach using the immobilized HEK receptor exodomain to detect and monitor purification of the HEK ligand. A protein purification protocol, which included HEK affinity chromatography, achieved a 1.8 X 10(6)-fold purification of an approximately 23-kDa protein from human placental conditioned medium. Analysis of specific sHEK (soluble extracellular domain of HEK) ligand interactions in the first and final purification steps suggested a ligand concentration of 40 pM in the source material and a Kd of 2-3 nM. Since the purified ligand was N-terminally blocked, we generated tryptic peptides and N-terminal amino acid sequence analysis of 7 tryptic fragments of the S-pyridylethylated protein unequivocally matched the sequence for AL-1, a recently reported ligand for the related EPH-like RTK REK7 (Winslow, J.W., Moran, P., Valverde, J., Shih, A., Yuan, J.Q., Wong, S.C., Tsai, S.P., Goddard, A., Henzel, W.J., Hefti, F., Beck, K.D., & Caras, I.W. (1995) Neuron 14, 973-981). Our findings demonstrate the application of biosensor technology in ligand purification and show that AL-1, as has been found for other ligands of the EPH-like RTK family, binds more than one receptor.

A new modularity measure for Fuzzy Community detection problems based on overlap and grouping functions

One of the main challenges of fuzzy community detection problems is to be able to measure the quality of a fuzzy partition. In this paper, we present an alternative way of measuring the quality of a fuzzy community detection output based on n-dimensional grouping and overlap functions. Moreover, the proposed modularity measure generalizes the classical Girvan–Newman (GN) modularity for crisp community detection problems and also for crisp overlapping community detection problems. Therefore, it can be used to compare partitions of different nature (i.e. those composed of classical, overlapping and fuzzy communities). Particularly, as is usually done with the GN modularity, the proposed measure may be used to identify the optimal number of communities to be obtained by any network clustering algorithm in a given network. We illustrate this usage by adapting in this way a well-known algorithm for fuzzy community detection problems, extending it to also deal with overlapping community detection problems and produce a ranking of the overlapping nodes. Some computational experiments show the feasibility of the proposed approach to modularity measures through n-dimensional overlap and grouping functions.

Algorithm for the detection of outliers based on the theory of rough sets

Outliers are objects that show abnormal behavior with respect to their context or that have unexpected values in some of their parameters. In decision-making processes, information quality is of the utmost importance. In specific applications, an outlying data element may represent an important deviation in a production process or a damaged sensor. Therefore, the ability to detect these elements could make the difference between making a correct and an incorrect decision. This task is complicated by the large sizes of typical databases. Due to their importance in search processes in large volumes of data, researchers pay special attention to the development of efficient outlier detection techniques. This article presents a computationally efficient algorithm for the detection of outliers in large volumes of information. This proposal is based on an extension of the mathematical framework upon which the basic theory of detection of outliers, founded on Rough Set Theory, has been constructed. From this starting point, current problems are analyzed; a detection method is proposed, along with a computational algorithm that allows the performance of outlier detection tasks with an almost-linear complexity. To illustrate its viability, the results of the application of the outlier-detection algorithm to the concrete example of a large database are presented.

Chemical Sensor Based on a Long-Period Fibre Grating Modified by a Functionalized Polydimethylsiloxane Coating

A chemical sensor based on a coated long-period grating has been prepared and characterized. Designer coatings based on polydimethylsiloxane were prepared by the incorporation of diphenylsiloxane and titanium cross-linker in order to provide enhanced sensitivity for a variety of key environmental pollutants and optimal refractive index of the coating. Upon microextraction of the analyte into the polymer matrix, an increase in the refractive index of the coating resulted in a change in the attenuation spectrum of the long-period grating. The grating was interrogated using ring-down detection as a means to amplify the optical loss and to gain stability against misalignment and power fluctuations. Chemical differentiation of cyclohexane and xylene was achieved and a detection limit of 300 ppm of xylene vapour was realized.

New PON1-based biosensor for the detection of homocysteine-thiolactone in human plasma

Poster presented at the XXIII International Symposium on Bioelectrochemistry and Bioenergetics, 14-18 June 2015, Malmo, Sweden.

Research on vehicle-based driver status/performance monitoring; development, validation, and refinement of algorithms for detection of driver drowsiness.

National Highway Traffic Safety Administration, Washington, D.C.

Marine mammal species detection and classification

Thesis (Ph.D.)--University of Washington, 2016-04

Development of PCR-based markers for detection of Leifsonia xyli subsp. xyli in fibrovascular fluid of infected sugarcane plants

DNA of Leifsonia xyli subsp. xyli (Lxx), the causal agent of ratoon stunting disease of sugarcane, was detected in the fibrovascular fluid of sugarcane plants using random amplified polymorphic DNA PCR-based amplification using two 10-mer oligonucleotide primers. The primers OPC-02 and OPC-11 produced Lxx-specific markers of approximately 800 bp and 1000 bp, respectively. A cloned DNA fragment from the 800 bp PCR product (pSKC2-800) hybridised to a single genomic DNA fragment from Lxx when used as a probe in Southern hybridisation. This cloned fragment did not hybridise to L. xyli subsp. cynodontis (Lxc), or L. xyli-like bacteria isolated from grasses in Australia, indicating the usefulness of this DNA fragment as a specific probe for Lxx. A cloned fragment from the 1000 bp PCR product ( pSKC11-1000) hybridised to three genomic fragments in Lxx isolates, one genomic fragment in two of the four isolates of L. xyli-like bacteria, and in two of the four isolates of Lxc isolated from the USA. These results indicate that L. xyli-like bacteria are more likely to be related to Lxc than Lxx. These probes did not hybridise to the DNA from strains of the species of Clavibacter, Rathayibacter, Acidovorax, Ralstonia, Pseudomonas and Xanthomonas tested. Two oligonucleotide primers (21-mer) designed from the pSKC2-800 sequences specifically amplified template DNA from Lxx and detected as few as 5 x 10(4) cells/mL in fibrovascular fluid from sugarcane plants infected with Lxx.

Development of an oligonucleotide-based SNP detection method on lateral flow strips using hexapet tages

Detection of point mutations or single nucleotide polymorphisms (SNPs) is important in relation to disease susceptibility or detection in pathogens of mutations determining drug resistance or host range. There is an emergent need for rapid detection methods amenable to point-of-care applications. The purpose of this study was to reduce to practice a novel method for SNP detection and to demonstrate that this technology can be used downstream of nucleic acid amplification. The authors used a model system to develop an oligonucleotide-based SNP detection system on nitrocellulose lateral flow strips. To optimize the assay they used cloned sequences of the herpes simplex virus-1 (HSV-1) DNA polymerase gene into which they introduced a point mutation. The assay system uses chimeric polymerase chain reaction (PCR) primers that incorporate hexameric repeat tags ("hexapet tags"). The chimeric sequences allow capture of amplified products to predefined positions on a lateral flow strip. These "hexapet" sequences have minimal cross-reactivity and allow specific hybridization-based capture of the PCR products at room temperature onto lateral flow strips that have been striped with complementary hexapet tags. The allele-specific amplification was carried out with both mutant and wild-type primer sets present in the PCR mix ("competitive" format). The resulting PCR products carried a hexapet tag that corresponded with either a wild-type or mutant sequence. The lateral flow strips are dropped into the PCR reaction tube, and mutant sequence and wild-type sequences diffuse along the strip and are captured at the corresponding position on the strip. A red line indicative of a positive reaction is visible after 1 minute. Unlike other systems that require separate reactions and strips for each target sequence, this system allows multiplex PCR reactions and multiplex detection on a single strip or other suitable substrates. Unambiguous visual discrimination of a point mutation under room temperature hybridization conditions was achieved with this model system in 10 minutes after PCR. The authors have developed a capture-based hybridization method for the detection and discrimination of HSV-1 DNA polymerase genes that contain a single nucleotide change. It has been demonstrated that the hexapet oligonucleotides can be adapted for hybridization on the lateral flow strip platform for discrimination of SNPs. This is the first step in demonstrating SNP detection on lateral flow using the hexapet oligonucleotide capture system. It is anticipated that this novel system can be widely used in point-of-care settings.

Development of a DNA-based method for detection and identification of Phytophthora species

Phytophthora diseases cause major losses to agricultural and horticultural production in Australia and worldwide. Most Phytophthora diseases are soilborne and difficult to control, making disease prevention an important component of many disease management strategies. Detection and identification of the causal agent, therefore, is an essential part of effective disease management. This paper describes the development and validation of a DNA-based diagnostic assay that can detect and identify 27 different Phytophthora species. We have designed PCR primers that are specific to the genus Phytophthora. The resulting amplicon after PCR is subjected to digestion by restriction enzymes to yield a specific restriction pattern or fingerprint unique to each species. The restriction patterns are compared with a key comprising restriction patterns of type specimens or representative isolates of 27 different Phytophthora species. A number of fundamental issues, such as genetic diversity within and among species which underpin the development and validation of DNA-based diagnostic assays, are addressed in this paper.

Seizure detection algorithm for neonates based on wave-sequence analysis

Objective: The description and evaluation of the performance of a new real-time seizure detection algorithm in the newborn infant. Methods: The algorithm includes parallel fragmentation of EEG signal into waves; wave-feature extraction and averaging; elementary, preliminary and final detection. The algorithm detects EEG waves with heightened regularity, using wave intervals, amplitudes and shapes. The performance of the algorithm was assessed with the use of event-based and liberal and conservative time-based approaches and compared with the performance of Gotman's and Liu's algorithms. Results: The algorithm was assessed on multi-channel EEG records of 55 neonates including 17 with seizures. The algorithm showed sensitivities ranging 83-95% with positive predictive values (PPV) 48-77%. There were 2.0 false positive detections per hour. In comparison, Gotman's algorithm (with 30 s gap-closing procedure) displayed sensitivities of 45-88% and PPV 29-56%; with 7.4 false positives per hour and Liu's algorithm displayed sensitivities of 96-99%, and PPV 10-25%; with 15.7 false positives per hour. Conclusions: The wave-sequence analysis based algorithm displayed higher sensitivity, higher PPV and a substantially lower level of false positives than two previously published algorithms. Significance: The proposed algorithm provides a basis for major improvements in neonatal seizure detection and monitoring. Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology.

Adipose tissue engineering based on the controlled release of fibroblast growth factor-2 in a collagen matrix

Adipose tissue forms when basement membrane extract ( Matrigel (TM)) and fibroblast growth factor-2 (FGF-2) are added to our mouse tissue engineering chamber model. A mouse tumor extract, Matrigel is unsuitable for human clinical application, and finding an alternative to Matrigel is essential. In this study we generated adipose tissue in the chamber model without using Matrigel by controlled release of FGF-2 in a type I collagen matrix. FGF-2 was impregnated into biodegradable gelatin microspheres for its slow release. The chambers were filled with these microspheres suspended in 60 mu L collagen gel. Injection of collagen containing free FGF-2 or collagen containing gelatin microspheres with buffer alone served as controls. When chambers were harvested 6 weeks after implantation, the volume and weight of the tissue obtained were higher in the group that received collagen and FGF-2 impregnated microspheres than in controls. Histologic analysis of tissue constructs showed the formation of de novo adipose tissue accompanied by angiogenesis. In contrast, control groups did not show extensive adipose tissue formation. In conclusion, this study has shown that de novo formation of adipose tissue can be achieved through controlled release of FGF-2 in collagen type I in the absence of Matrigel.