An Intelligent System for Automated DNA Base Calling

An investigation into improving the performance of DNA base calling algorithms was conducted. The results have shown that the preprocessing steps performed by ABI sequencer on raw data adversely affects the accuracy of DNA sequencing. This adverse effect has been responsible for relatively high error rates, between 3.5% to 6%, in both ABI and Phred sequencing software. Please note that Phred also uses the processed data generated by ABI sequencer; only their base-calling algorithm is different. To remedy this effect, we have developed and implemented a new filtering technique that preserves the initial information contained in the raw data. This provides qualitatively superior data for the future base calling step. Our proposed filtering step provides mechanical shift compensation, cross-talk filtering, and baseline adjustment. These have been briefly described below. Application of our filtering step on a limited number of DNA data has provided sequences with lower error rate

Accurate DNA Base Caller

The major goal of this project is to develop a new base calling technique that will improve the efficiency of the DNA sequencing process. This will be achieved by increasing the average length of error-free sequencing and enhancing the base identification process at the beginning and end of sequences. This will increase sequencing throughput and reduce the cost of DNA sequencing. Previous work by the PI has demonstrated the ability to extend the error-free read by 30%. This was achieved through work on cross-talk filtering, baseline adjustment, base-spacing prediction and development of a fuzzy base-calling algorithm. Further adaptive capabilities as well as full development and implementation of the methodology is planned. The software will be tested on a large number of DNA sequences and remove specific hardware and operating system requirements, as well as be exploitable over the web. Accurate, inexpensive genomic DNA sequencing will be a cornerstone of 21st century biology

Biodiversity and Ecosystem Informatics - BDEI - Planning Workshop on Biodiversity and Ecosystem Informatics for the Indian River Lagoon, Florida

This proposal solicits funding to organize and conduct a planning workshop that will establish and facilitate research on the informatics needed to address complex issues of biodiversity and ecosystem processes within the Indian River Lagoon. This workshop will provide the opportunity and resources for collaboration and discussion among scientists from diverse fields of biodiversity, ecological sciences, remote sensing, geographic information systems, computer science and intelligent systems. The topics to be discussed will include investigation of novel computational intelligence techniques for modeling, prediction, analysis and database management of the disparate and complex data for the Indian River Lagoon. The explicit products of the proposed workshop will be a white paper and technical report, a formal research agenda that incorporates informatics into existing and planned research, and preparation of a competitive proposal based on the recommendations and preliminary work defined by the workshop

GOALI/IUCP: Prediction of Wood Pulp K-Number with Neural Networks

Lignin holds wood fibers together, and must be removed to produce high strength pulp for kraft paper. The Kappa- or K-number indicates the degree of lignin removal by a pulping process, and is probably the key variable for measuring quality in this process. A difficulty is that it is an off-line measurement. More importantly, there is usually a four hour process delay between when raw materials enter a pulping digester and when the K-number is measured. This makes modeling and control difficult. This Grant Opportunity for Academic Liaison with Industry project uses neural network models to predict K-number as a function of a number of more readily available process parameters. This is a first step in improving the control and responsiveness of this process to changes in chip feed stock. The research team from the University of Maine and S.D. Warren Company will develop characterization and prediction models using data from an operating plant, and compare their long term predicative capability when integrated into digester operations. Throughout, seminar and workshops are part of the technology transfer and model improvement. The impact of this research will be more uniform quality of pulp, even with variable feed stock, and more uniform quality in subsequent bleaching and papermaking processes

Ecosystem physio-phenology revealed using circular statistics

Quantifying responses of vegetation phenology to climate variability is a key prerequisite to predict shifts in how ecosystem dynamics due to climate change. So far, many studies have focused on responses of classical phenological events (e.g. budburst or flowering) to climatic variability for individual species. Comparatively little is known on physio-phenological events such as the timing of the maximum gross primary production (DOYGPPmax). However, understanding this type of physio-phenological phenomena is an essential element in predicting the response of the terrestrial carbon cycle to climate variability. In this study, we aim to understand how DOYGPPmax depends on climate drivers across 52 eddy-covariance (EC) sites in the FLUXNET network for different regions of the world. Most phenological studies rely on linear methods that cannot be generalized across both hemispheres and therefore do not allow for deriving general rules that can be applied for future predictions. Here we explore a new class of circular-linear (here called circular) regression approach that may show a path ahead. Circular regression allows relating circular variables (in our case phenological events) to linear predictor variables (e.g. climate conditions). As a proof of concept, we compare the performance of linear and circular regression to recover original coefficients of a predefined circular model on artificial and EC data. We then quantify the sensitivity of DOYGPPmax to air temperature, short-wave incoming radiation, precipitation and vapor pressure deficit using circular regressions. Finally, we evaluate the predictive power of the regression models for different vegetation types. Our results show that the DOYGPPmax of each FLUXNET site has a unique signature of climatic sensitivities. Overall radiation and temperature are the most relevant controlling factors of DOYGPPmax across sites. The circular approach gives us new insights at the site level. In a Mediterranean shrub-land, for instance, we find that the two growing seasons are controlled by different climatic factors. Although the sensitivity of the DOYGPPmax to the climate drivers is very site specific, it is possible to extrapolate the circular regression model across vegetation types. From a methodological point of view, our results reveal that circular regression is a robust alternative to conventional phenological analytic frameworks. In particular global analyses can benefit, where phase shifts play a role or double peaked growing seasons may occur

Gelatinases Increase in Bleomycin-induced Systemic Sclerosis Mouse Model

Systemic sclerosis is a fibrotic autoimmune disease in which aberrant remodeling of the extracellular matrix in organs disturbs their functionalities. The aim of this study was to investigate the expression of gelatinases on systemic sclerosis. Consequently, a mouse model of systemic sclerosis was employed and the gelatinolytic activity of gelatinases was evaluated on the fibrotic tissues of this model. Two groups of ten mice were considered in this work: a group of systemic sclerosis model and control group. For the generation of systemic sclerosis model, mice received bleomycin, while the control group was subjected to phosphate buffered saline (PBS) reception. Mice were tested for fibrosis by using trichrome staining, hydroxyproline measurement and α-SMA detection in tissue sections. Additionally, the gelatinolytic activity of matrix metalloproteinase 2 and matrix metalloproteinase 9 were measured using gelatin zymography in lungs and skin tissue homogenates. The obtained results indicated that subcutaneous injection of bleomycin-induced fibrosis in skin and lung tissues of mice. Pro and active forms of matrix methaloproteinase 9 were increased in fibrotic lung tissues (p<0.05 and p<0.01, respectively), while, the gelatinolytic activity of MMP2 was unaffected in these tissues. Additionally, in skin tissues of bleomycin-treated animals, both pro and active forms of MMP9 and MMP2 were increased (p<0.05). Pro and active forms of gelatinases increase differently in skin and lung tissues of bleomycin-induced scleroderma