Simple and Rapid Quantification of Thrombocytes in Zebrafish Larvae

Platelets are a critical component of hemostasis, with disorders of number or function resulting in coagulation disturbances. Insights into these processes have primarily been realized through studies using mammalian models or tissues. Increasingly, zebrafish embryos and larvae have been used to study the protein and cellular components of hemostasis and thrombosis, including the thrombocyte, a nucleated platelet analog. However, investigations of thrombocytes have been somewhat limited due to lack of a robust and simple methodology for quantitation, an important component of platelet studies in mammals. Using video capture, we have devised an assay that produces a rapid, reproducible, and precise measurement of thrombocyte number in zebrafish larvae by counting fluorescently tagged cells. Averaging 1000 frames, we were able to subtract background fluorescence, thus limiting assessment to circulating thrombocytes. This method facilitated rapid assessment of relative thrombocyte counts in a population of 372 zebrafish larvae by a single operator in less than 3 days. This technique requires basic microscopy equipment and rudimentary programming, lends itself to high throughput analysis, and will enhance future studies of thrombopoiesis in the zebrafish.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140302/1/zeb.2014.1079.pd

Detection of Non-PCR Amplified S. enteritidis Genomic DNA from Food Matrices Using a Gold-Nanoparticle DNA Biosensor: A Proof-of-Concept Study

Bacterial pathogens pose an increasing food safety and bioterrorism concern. Current DNA detection methods utilizing sensitive nanotechnology and biosensors have shown excellent detection, but require expensive and time-consuming polymerase chain reaction (PCR) to amplify DNA targets; thus, a faster, more economical method is still essential. In this proof-of-concept study, we investigated the ability of a gold nanoparticle-DNA (AuNP-DNA) biosensor to detect non-PCR amplified genomic Salmonella enterica serovar Enteritidis (S. enteritidis) DNA, from pure or mixed bacterial culture and spiked liquid matrices. Non-PCR amplified DNA was hybridized into sandwich-like structures (magnetic nanoparticles/DNA/AuNPs) and analyzed through detection of gold voltammetric peaks using differential pulse voltammetry. Our preliminary data indicate that non-PCR amplified genomic DNA can be detected at a concentration as low as 100 ng/mL from bacterial cultures and spiked liquid matrices, similar to reported PCR amplified detection levels. These findings also suggest that AuNP-DNA biosensors are a first step towards a viable detection method of bacterial pathogens, in particular, for resource-limited settings, such as field-based or economically limited conditions. Future efforts will focus on further optimization of the DNA extraction method and AuNPbiosensors, to increase sensitivity at lower DNA target concentrations from food matrices comparable to PCR amplified DNA detection strategies

A two-tier business model and its realization for entrepreneurship

Entrepreneurs are important to economic development. Business model design is critical to assist entrepreneurs. This study proposes a two-tier business model for entrepreneurs, consisting of a conceptual model and a financial model. The conceptual model describes the idea of a new business which is useful to explain a business. The financial model provides the numbers of the new business which makes the business model accountable and measurable. The two-tier business model is more applicable in that on one hand, the model addresses the conceptual and financial issues separately to avoid confusion; on the other hand, the model integrates both the conceptual and financial models to provide a complete view of the business. Each of the conceptual and financial models provides the relationships among their components. In addition, the two-tier business model shows the relationships between both models. This study realizes the business model by the application of Internet. In addition, two real cases, including Apps and one million dollar home page, exemplify the practices

Qualitative analysis of housing demand using Google trends data

Big data analytics often refer to the breakdown of huge amounts of data into a more readable and useful format. This study utilises Google Trends big data as a proxy for an analysis of housing demand. We employ a qualitative method (fuzzy set/Qualitative Comparative Analysis, fsQCA), instead of a quantitative method, for our estimate and forecast. The empirical results show that fsQCA successfully forecasts seasonal time series, even though the dataset is small in size. Our findings fill the gap in the qualitative and time series forecasting literature, and the forecasting procedure herein also offers a good standard for industry

MLE for the parameters of bivariate interval-valued models

With contemporary data sets becoming too large to analyze the data directly, various forms of aggregated data are becoming common. The original individual data are points, but after aggregation, the observations are interval-valued (e.g.). While some researchers simply analyze the set of averages of the observations by aggregated class, it is easily established that approach ignores much of the information in the original data set. The initial theoretical work for interval-valued data was that of Le-Rademacher and Billard (2011), but those results were limited to estimation of the mean and variance of a single variable only. This article seeks to redress the limitation of their work by deriving the maximum likelihood estimator for the all important covariance statistic, a basic requirement for numerous methodologies, such as regression, principal components, and canonical analyses. Asymptotic properties of the proposed estimators are established. The Le-Rademacher and Billard results emerge as special cases of our wider derivations.Comment: Will appear in ADA

A class of constant modulus algorithms for uniform linear arrays with a conjugate symmetric constraint

A class of constant modulus algorithms (CMAs) subject to a conjugate symmetric constraint is proposed for blind beamforming based on the uniform linear array structure. The constraint is derived from the beamformer with an optimum output signal-to-interference-plus-noise ratio (SINR). The effect of the additional constraint is equivalent to adding a second step to the original adaptive algorithms. The proposed approach is general and can be applied to both the traditional CMA and its all kinds of variants, such as the linearly constrained CMA (LCCMA) and the least squares CMA (LSCMA) as two examples. With this constraint, the modified CMAs will always generate a weight vector in the desired form for each update and the number of adaptive variables is effectively reduced by half, leading to a much improved overall performance. (C) 2010 Elsevier B.V. All rights reserved

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