976 resultados para Medical genetics.
Resumo:
My project is a business plan about the set up of a company and the development of a new and innovative product aimed for the elders. I decide do this project when I discover that one of the more important needs that have the elders is to remember the medicines that they have to take. I thought that a good way could be through a smart watch. My watch have an only function, is a cheap device, easy to use, easy to understand and easy to set up, because the elders usually do not know to use complex electronics devices. There are other similar smart watches and other devices but do not have the necessary characteristics to be a good reminder for elders. My watch is centred to improve the life of the elders, but my product could also be useful for ill people who have to take many medicines during the day. After realizing this business plan, I have proved that my company is viable in the environment and profitable in the market.
Resumo:
In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques-Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description-using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.
Resumo:
Aquaculture in Africa is fairly insignificant by world standards and accounts for a mere 0.4 per cent of global aquaculture production. The application of genetics can play an important role in efforts to increase aquaculture production in Africa through methods such as selective breeding, hybridization, chromosome manipulation and use of YY “supermales”. Other issues that need to be addressed are limited genetic research facilities, funding, human capacity and suitable species for aquaculture.
Resumo:
The information presented here is extracted from the presentations and discussions at the Sixth Steering Committee Meeting of the International Network on Genetics in Aquaculture (INGA) held in Hanoi, Vietnam on 8-10 May 2001. The main topics discussed were: review of genetics research progress and planned activities in member countries and Associate Member institutions; genetics improvement technologies; strategies and action plans for distribution of improved fish breeds to small-scale farmers; ecological risk assessment for genetically improved fish breeds; methods for monitoring the uptake of improved strains and impact assessment; and network activities and collaborations.
Resumo:
In this paper we present livestock breeding developments that could be taken into consideration in the genetic improvement of farmed aquaculture species, especially in freshwater fish. Firstly, the current breeding objective in aquatic species has focused almost exclusively on the improvement of body weight at harvest or on growth related traits. This is unlikely to be sufficient to meet the future needs of the aquaculture industry. To meet future demands breeding programs will most likely have to include additional traits, such as fitness related ones (survival, disease resistance), feed efficiency, or flesh quality, rather than only growth performance. In order to select for a multi-trait breeding objective, genetic variation in traits of interest and the genetic relationships among them need to be estimated. In addition, economic values for these traits will be required. Generally, there is a paucity of data on variable and fixed production costs in aquaculture, and this could be a major constraint in the further expansion of the breeding objectives. Secondly, genetic evaluation systems using the restricted maximum likelihood method (REML) and best linear unbiased prediction (BLUP) in a framework of mixed model methodology could be widely adopted to replace the more commonly used method of mass selection based on phenotypic performance. The BLUP method increases the accuracy of selection and also allows the management of inbreeding and estimation of genetic trends. BLUP is an improvement over the classic selection index approach, which was used in the success story of the genetically improved farmed tilapia (GIFT) in the Philippines, with genetic gains from 10 to 20 per cent per generation of selection. In parallel with BLUP, optimal genetic contribution theory can be applied to maximize genetic gain while constraining inbreeding in the long run in selection programs. Thirdly, by using advanced statistical methods, genetic selection can be carried out not only at the nucleus level but also in lower tiers of the pyramid breeding structure. Large scale across population genetic evaluation through genetic connectedness using cryopreserved sperm enables the comparison and ranking of genetic merit of all animals across populations, countries or years, and thus the genetically superior brood stock can be identified and widely used and exchanged to increase the rate of genetic progress in the population as a whole. It is concluded that sound genetic programs need to be established for aquaculture species. In addition to being very effective, fully pedigreed breeding programs would also enable the exploration of possibilities of integrating molecular markers (e.g., genetic tagging using DNA fingerprinting, marker (gene) assisted selection) and reproductive technologies such as in-vitro fertilization using cryopreserved spermatozoa.