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The polymerase chain reaction (PCR) is a method widely used to make millions to billions of copies of a specific DNA sample rapidly, allowing scientists to amplify a very small sample of DNA (or a part of it) sufficiently to enable detailed study. PCR was invented in 1983 by American biochemist Kary Mullis at Cetus Corporation; Mullis and biochemist Michael Smith, who had developed other essential ways of manipulating DNA, were jointly awarded the Nobel Prize in Chemistry in 1993.
Staining is a technique used to enhance contrast in samples, generally at the microscopic level. Stains and dyes are frequently used in histology (microscopic study of biological tissues), in cytology (microscopic study of cells), and in the medical fields of histopathology, hematology, and cytopathology that focus on the study and diagnoses of diseases at the microscopic level. Stains may be used to define biological tissues (highlighting, for example, muscle fibers or connective tissue), cell populations (classifying different blood cells), or organelles within individual cells.
A real-time polymerase chain reaction (real-time PCR, or qPCR when used quantitatively) is a laboratory technique of molecular biology based on the polymerase chain reaction (PCR). It monitors the amplification of a targeted DNA molecule during the PCR (i.e., in real time), not at its end, as in conventional PCR. Real-time PCR can be used quantitatively and semi-quantitatively (i.e., above/below a certain amount of DNA molecules).
The present invention concerns a thermal sensing device (1) and a sensory feedback system and method using such thermal sensing device, comprising at least one film (19) of electrically insulating polymer defining a global surface of the thermal sensing de ...
Mycobacterium Tuberculosis is a highly effective pathogen infecting nearly a third of the world's population. An M. tuberculosis infection starts when droplets containing bacteria enter an individual's lungs. The first host cells to contact the bacteria ar ...
In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...
