Function Sampling Points
Function Sampling Points - Related Videos
14:39
Isolation and Functional Characterization of Human Ventricular Cardiomyocytes from Fresh Surgical Samples
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Cited by 44 •
2014
Current knowledge on the cellular basis of cardiac diseases mostly relies on studies on animal models. Here we describe and validate a novel method to obtain single viable cardiomyocytes from small surgical samples of human ventricular myocardium. Human ventricular myocytes can be used for electrophysiological studies and drug testing.
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An All-in-one Sample Holder for Macromolecular X-ray Crystallography with Minimal Background Scattering
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Cited by 4 •
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A novel sample holder for macromolecular X-ray crystallography along with a suitable handling protocol is presented. The system allows crystal growth, crystal soaking and in situ diffraction data collection at both, ambient and cryogenic temperature without the need of any crystal manipulation or mounting.
10:16
Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
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Cited by 186 •
2016
The protocol below describes the methodology for: microplastics sampling on the sea surface, separation of microplastic and chemical identification of particles. This protocol is in line with the recommendations for microplastics monitoring published by the MSFD Technical Subgroup on Marine Litter.
01:18
Sampling Methods: Sample Types
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2024
Sampling materials are classified into three main types: solid, liquid, and gas. Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
02:45
Functional Groups
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2020
Functional groups are a group of atoms with characteristic properties, which when linked to the carbon skeleton of a molecule, alter the properties of that molecule. For example, the presence of certain functional groups on a molecule will make them hydrophilic, whereas others will make them hydrophobic. These functional groups are an indispensable part of organic chemistry and important components of biological molecules, such as carbohydrates, proteins, lipids, and nucleic acids. Each...
FAQs
Adaptive sampling places more input values in regions where the function changes rapidly and can use fewer points where behavior is smoother. This distribution helps preserve important features that uniform spacing might overlook while controlling the total number of evaluations. The approach is therefore useful when function behavior is uneven and computational resources are limited.
Spacing determines how much behavior lies between observed values. If points are too sparse or poorly located, the resulting discrete representation may miss important features of the function. More appropriate spacing generally provides a more reliable approximation, but it also requires additional function evaluations. Selecting spacing therefore balances accuracy against computational cost.
The evaluated pairs formed from selected inputs and their function values provide discrete information for estimating behavior between points and for approximating accumulated quantities. In interpolation, the samples help represent values not directly evaluated. In numerical integration, they supply the function information used to approximate an integral, so their placement can influence the quality of the result.
First select input values appropriate to the behavior being studied, using uniform spacing when it is sufficient or concentrating points where changes are rapid. Next evaluate the function at each selected input and organize the resulting values as discrete data. Finally, use those data for graphing, interpolation, numerical integration, optimization, or computational modeling.
They are useful when a graph must be produced from discrete evaluations rather than continuous access to every input value. Evaluating the function at selected inputs creates points that reveal its behavior visually. Adequate coverage and spacing matter because a sparse set may fail to show important changes, while a well-chosen set supports a more dependable graphical representation.
In optimization and computational modeling, sampled function values provide the discrete information used to examine or represent a function’s behavior. Poorly chosen inputs can omit relevant features and weaken the resulting analysis or model. Appropriate selection improves the representation while limiting unnecessary evaluations, making the computational process more efficient without discarding important behavior.