Littérature scientifique sur le sujet « FFxIVF »

Hereditary factor XI (FXI) deficiency is characterized as an autosomal mild to moderate coagulopathy in humans and domestic animals. Coagulation testing revealed FXI deficiency in a core family of Maine Coon cats (MCCs) in the United States. Factor XI-deficient MCCs were homozygous for a guanine to adenine transition resulting in a methionine substitution for the highly conserved valine-516 in the FXI catalytic domain. Immunoblots detected FXI of normal size and quantity in plasmas of MCCs homozygous for V516M. Some FXI-deficient MCCs experienced excessive post-operative/traumatic bleeding. Screening of 263 MCCs in Europe revealed a mutant allele frequency of 0.232 (23.2%). However, V516M was not found among 100 cats of other breeds. Recombinant feline FXI-M516 (fFXI-M516) expressed ~4% of the activity of wild-type fFXI-V516 in plasma clotting assays. Furthermore, fFXIa-M516 cleaved the chromogenic substrate S-2366 with ~4.3-fold lower catalytic efficacy (kcat/Km) than fFXIa-V516, supporting a conformational alteration of the protease active site. The rate of FIX activation by fFXIa-M516 was reduced >3-fold compared with fFXIa-V516. The common missense variant FXI-V516M causes a cross-reactive material positive FXI deficiency in MCCs that is associated with mild-moderate bleeding tendencies. Given the prevalence of the variant in MCCs, genotyping is recommended prior to invasive procedures or breeding.

In movement science with inertial sensor many different methodologies resolving specific aspects of movement recognition have been proposed. They are very interesting, and useful, but none of them are generally explicative of what is going on in the semantic sense. When we go down to the movement recognition/classification area (for example in Ambient Intelligence) we do not have a feasible model that can be considered generally predictive or usable for activity recognition. Also, in the field of movement recognition with inertial sensors many technological issues arise: technological diversity, calibration matters, sensor model problems, orientation and position of sensors, and a lot of numerous specificities that, with all the above aspects, and the lack of public dataset of movements sufficiently generic and semantically rich, contribute to create a strong barrier to any approach to a classification matters with wearable sensors. We have also to notice that a movement is a phenomenon explicitly or implicitly (voluntary or involuntary) controlled by brain. The individual free-will introduce a further matter when we want to temporary predict the movements looking at the close past. Pattern can change at any time when ambient, psychological context, age of the subject change. Also, pathological issues, and physiological differences and the will of the subject, introduce important differences. For all these reasons I considered that a semantical /lexical approach to movement recognition with sensors, driven by machine learning techniques could be a promising way to solve some of these challenge and problems. In this Ph.D. Thesis wearable inertial sensors has been used to classify movements, the choice of inertial sensors has been driven by technological and practical advantages, they are cheap, lightweight, and - differently from video cameras - are not prone to the hidden face, or luminance problems. The main idea is to use inertial sensor to understand what a person is doing for ambient-intelligent, healthcare, medical-sport applications. My principal concerns was to propose a method that was not centered on technology issues but on data analysis, that could be a general framework and could also create a general representation of movement,that could be useful also in other area of research, like reasoning. Inertial sensors are treated just as an example, a particular type of sensors, the method is new, reusable, algorithmically simple, net and easy to understand. Accuracy is very high outperforming the best results given in literature, reducing the error rate of 4 times.

碩士
國立臺北科技大學
管理學院資訊與財金管理EMBA專班
106
In the market economy, Harvard Business Review: The Experience Economy comes with the Experience Advantage of the products, “expressing the brand image, advocating a new lifestyle, creating a communication atmosphere, in-depth experience with quality products, and standardizing the business”.