FPGA based speeded up robust features

We present an implementation of the Speeded Up Robust Features (SURF) on a Field Programmable Gate Array (FPGA). The SURF algorithm extracts salient points from image and computes descriptors of their surroundings that are invariant to scale, rotation and illumination changes. The interest point detection and feature descriptor extraction algorithm is often used as the first stage in autonomous robot navigation, object recognition and tracking etc. However, detection and extraction are computationally demanding and therefore can't be used in systems with limited computational power. We took advantage of algorithm's natural parallelism and implemented it's most demanding parts in FPGA logic. Several modifications of the original algorithm have been made to increase it's suitability for FPGA implementation. Experiments show, that the FPGA implementation is comparable in terms of precision, speed and repeatability, but outperforms the CPU and GPU implementation in terms of power consumption. Our implementation is intended to be used in embedded systems which are limited in computational power or as the first stage preprocessing block, which allows the computational resources to focus on higher level algorithms

Organelle trafficking of chimeric ribozymes and genetic manipulation of mitochondria

With the expansion of the RNA world, antisense strategies have become widespread to manipulate nuclear gene expression but organelle genetic systems have remained aside. The present work opens the field to mitochondria. We demonstrate that customized RNAs expressed from a nuclear transgene and driven by a transfer RNA-like (tRNA-like) moiety are taken up by mitochondria in plant cells. The process appears to follow the natural tRNA import specificity, suggesting that translocation indeed occurs through the regular tRNA uptake pathway. Upon validation of the strategy with a reporter sequence, we developed a chimeric catalytic RNA composed of a specially designed trans-cleaving hammerhead ribozyme and a tRNA mimic. Organelle import of the chimeric ribozyme and specific target cleavage within mitochondria were demonstrated in transgenic tobacco cell cultures and Arabidopsis thaliana plants, providing the first directed knockdown of a mitochondrial RNA in a multicellular eukaryote. Further observations point to mitochondrial messenger RNA control mechanisms related to the plant developmental stage and culture conditions. Transformation of mitochondria is only accessible in yeast and in the unicellular alga Chlamydomonas. Based on the widespread tRNA import pathway, our data thus make a breakthrough for direct investigation and manipulation of mitochondrial genetics

Chirurgicke leceni chronickeho zanetu slinivky brisni.

Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi

Development and Validation of an International Risk Prediction Algorithm for Episodes of Major Depression in General Practice Attendees The PredictD Study

Funding: The research in Europe was funded by a grant from the European Commission, reference PREDICT-QL4-CT2002-00683. Funding in Chile was provided by project FONDEF DO2I-1140. Partial support in Europe was from the Estonian Scientific Foundation (grant 5696), the Slovenian Ministry for Research (grant 4369-1027), the Spanish Ministry of Health (grant field-initiated studies program references PI041980, PI041771, and PI042450), the Spanish Network of Primary Care Research (redIAPP) (ISCIII-RETIC RD06/ 0018), and SAMSERAP group. The UK National Health Service Research and Development office provided service support costs in the United Kingdom.Context: Strategies for prevention of depression are hindered by lack of evidence about the combined predictive effect of known risk factors. Objectives: To develop a risk algorithm for onset of major depression. Design: Cohort of adult general practice attendees followed up at 6 and 12 months. We measured 39 known risk factors to construct a risk model for onset of major depression using stepwise logistic regression. We corrected the model for overfitting and tested it in an external population. Setting: General practices in 6 European countries and in Chile. Participants: In Europe and Chile, 10 045 attendees were recruited April 2003 to February 2005. The algorithm was developed in 5216 European attendees who were not depressed at recruitment and had follow-up data on depression status. It was tested in 1732 patients in Chile who were not depressed at recruitment. Main Outcome Measure: DSM-IV major depression. Results: Sixty-six percent of people approached participated, of whom 89.5% participated again at 6 months and 85.9%, at 12 months. Nine of the 10 factors in the risk algorithm were age, sex, educational level achieved, results of lifetime screen for depression, family history of psychological difficulties, physical health and mental health subscale scores on the Short Form 12, unsupported difficulties in paid or unpaid work, and experiences of discrimination. Country was the tenth factor. The algorithm's average C index across countries was 0.790 ( 95% confidence interval [ CI], 0.767-0.813). Effect size for difference in predicted log odds of depression between European attendees who became depressed and those who did not was 1.28 ( 95% CI, 1.17-1.40). Application of the algorithm in Chilean attendees resulted in a C index of 0.710 ( 95% CI, 0.670-0.749). Conclusion: This first risk algorithm for onset of major depression functions as well as similar risk algorithms for cardiovascular events and may be useful in prevention of depression.publishersversionpublishe

Development and validation of an international risk prediction algorithm for episodes of major depression in general practice attendees : the PredictD study

Context: Strategies for prevention of depression are hindered by lack of evidence about the combined predictive effect of known risk factors. Objectives: To develop a risk algorithm for onset of major depression. Design: Cohort of adult general practice attendees followed up at 6 and 12 months. We measured 39 known risk factors to construct a risk model for onset of major depression using stepwise logistic regression. We corrected the model for overfitting and tested it in an external population. Setting: General practices in 6 European countries and in Chile. Participants: In Europe and Chile, 10 045 attendees were recruited April 2003 to February 2005. The algorithm was developed in 5216 European attendees who were not depressed at recruitment and had follow-up data on depression status. It was tested in 1732 patients in Chile who were not depressed at recruitment. Main Outcome Measure: DSM-IV major depression. Results: Sixty-six percent of people approached participated, of whom 89.5% participated again at 6 months and 85.9%, at 12 months. Nine of the 10 factors in the risk algorithm were age, sex, educational level achieved, results of lifetime screen for depression, family history of psychological difficulties, physical health and mental health subscale scores on the Short Form 12, unsupported difficulties in paid or unpaid work, and experiences of discrimination. Country was the tenth factor. The algorithm's average C index across countries was 0.790 ( 95% confidence interval [ CI], 0.767-0.813). Effect size for difference in predicted log odds of depression between European attendees who became depressed and those who did not was 1.28 ( 95% CI, 1.17-1.40). Application of the algorithm in Chilean attendees resulted in a C index of 0.710 ( 95% CI, 0.670-0.749). Conclusion: This first risk algorithm for onset of major depression functions as well as similar risk algorithms for cardiovascular events and may be useful in prevention of depression