Hybrid Systems
Hybrid Systems - Related Videos
03:24
Hybridization of Atomic Orbitals I
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2020
The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
03:35
Hybridization of Atomic Orbitals II
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2020
sp3d and sp3d 2 Hybridization To describe the five bonding orbitals in a trigonal bipyramidal arrangement, we must use five of the valence shell atomic orbitals (the s orbital, the three p orbitals, and one of the d orbitals), which gives five sp3d hybrid orbitals. With an octahedral arrangement of six hybrid orbitals, we must use six valence shell atomic orbitals (the s orbital, the three p orbitals, and two of the d orbitals in its valence shell), which gives six sp3d 2 hybrid orbitals.
08:00
Whole-Mount In Situ Hybridization
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2023
Whole-mount in situ hybridization (WMISH) is a common technique used for visualizing the location of expressed RNAs in embryos. In this process, synthetically produced RNA probes are first complementarily bound, or "hybridized," to the transcripts of target genes. Immunohistochemistry or fluorescence is then used to detect these RNA hybrids, revealing spatial and temporal patterns of gene expression. Unlike traditional in situ hybridization techniques, which require thin tissue sections whose...
13:19
The Use of Chemostats in Microbial Systems Biology
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Cited by 67 •
2013
Cell growth rate is a regulated process and a primary determinant of cell physiology. Continuous culturing using chemostats enables extrinsic control of cell growth rate by nutrient limitation facilitating the study of molecular networks that control cell growth and how those networks evolve to optimize cell growth.
08:18
Construction Methods for Hybrid Polycarbonate-Silicon Microfluidic Devices
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2026
This protocol describes three straightforward sealing methods for fabricating a hybrid microfluidic device that integrates polycarbonate and silicon components. By implementing solvent bonding, thermal compression, and mechanical compression strategies, we enable reliable, low-temperature sealing techniques that avoid specialized equipment for hybrid microfluidic devices.
FAQs
Switching rules determine when the model leaves one mode and enters another, while thresholds provide decision points based on changing clinical measurements. A continuously evolving physiological signal can therefore trigger a discrete change, such as a new symptom category or treatment decision. Adjusting these rules changes when transitions occur and can alter monitoring, simulation, and treatment-planning results.
Continuous measurements capture gradual biological change, whereas discrete states represent clinically meaningful categories, decisions, or transitions. Modeling them together preserves the relationship between a patient’s evolving physiology and the events that require action. This combination is important when a slowly changing signal can eventually cross a rule-defined boundary and produce a sudden change in clinical status or management.
The continuous part can represent physiological variables that change over time, while discrete modes represent stages, symptom categories, or other clinically distinct conditions. Rules connect these representations by specifying when a transition occurs. Researchers can use this structure to simulate gradual disease progression alongside sudden state changes, helping examine how interventions may influence the resulting clinical trajectory.
A clinical application begins by identifying continuously changing measurements and the discrete states or decisions that matter. Researchers then link them with rules or thresholds that specify mode changes. The resulting model can connect real-time measurements to actions, represent disease transitions, or simulate treatment scenarios. Its usefulness depends on whether the selected variables and transition rules reflect the clinical question.
They are useful when monitoring must translate changing physiological signals into discrete actions or device states. As measurements evolve, predefined rules can indicate when the system should recognize a symptom category, alter a treatment decision, or switch operating modes. This supports applications in patient monitoring and adaptive medical devices where timely responses depend on both ongoing signals and explicit events.
By linking physiological measurements with symptom categories and treatment decisions, the framework provides a structured way to examine clinical responses. It can support diagnosis by relating signals to discrete states, inform treatment planning through rule-based transitions, and simulate disease progression to evaluate interventions. These models are especially relevant when outcomes depend on gradual biological changes combined with sudden clinical events.