X Particles 2.5 Serial Number
Cherie Trojak
In thermodynamics, the particle number (symbol N) of a thermodynamic system is the number of constituent particles in that system.[1] The particle number is a fundamental thermodynamic property which is conjugate to the chemical potential. Unlike most physical quantities, the particle number is a dimensionless quantity, specifically a countable quantity. It is an extensive property, as it is directly proportional to the size of the system under consideration and thus meaningful only for closed systems.
A constituent particle is one that cannot be broken into smaller pieces at the scale of energy kT involved in the process (where k is the Boltzmann constant and T is the temperature). For example, in a thermodynamic system consisting of a piston containing water vapour, the particle number is the number of water molecules in the system. The meaning of constituent particles, and thereby of particle numbers, is thus temperature-dependent.
The concept of particle number plays a major role in theoretical considerations. In situations where the actual particle number of a given thermodynamical system needs to be determined, mainly in chemistry, it is not practically possible to measure it directly by counting the particles. If the material is homogeneous and has a known amount of substance n expressed in moles, the particle number N can be found by the relation : N = n N A \displaystyle N=nN_A ,where NA is the Avogadro constant.[1]
A related intensive system parameter is the particle number density, a quantity of kind volumetric number density obtained by dividing the particle number of a system by its volume. This parameter is often denoted by the lower-case letter n.
In quantum mechanical processes, the total number of particles may not be preserved. The concept is therefore generalized to the particle number operator, that is, the observable that counts the number of constituent particles.[2] In quantum field theory, the particle number operator (see Fock state) is conjugate to the phase of the classical wave (see coherent state).
One measure of air pollution used in air quality standards is the atmospheric concentration of particulate matter. This measure is usually expressed in μg/m3 (micrograms per cubic metre). In the current EU emission norms for cars, vans, and trucks and in the upcoming EU emission norm for non-road mobile machinery, particle number measurements and limits are defined, commonly referred to as PN, with units [#/km] or [#/kWh]. In this case, PN expresses a quantity of particles per unit distance (or work).
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The association between fine and ultrafine particles and respiratory health was studied in adults with a history of asthma in Erfurt, Eastern Germany. Twenty-seven nonsmoking asthmatics recorded their peak expiratory flow (PEF) and respiratory symptoms daily. The size distribution of ambient particles in the range of 0.01 to 2.5 microm was determined with an aerosol spectrometer during the winter season 1991-1992. Most of the particles (73%) were in the ultrafine fraction (smaller than 0.1 microm in diameter), whereas most of the mass (82%) was attributable to particles in the size range of 0.1 to 0.5 microm. Because these two fractions did not have similar time courses (correlation coefficient r = 0.51), a comparison of their health effects was possible. Both fractions were associated with a decrease of PEF and an increase in cough and feeling ill during the day. Health effects of the 5-d mean of the number of ultrafine particles were larger than those of the mass of the fine particles. In addition, the effects of the number of the ultrafine particles on PEF were stronger than those of particulate matter smaller than 10 microm (PM10). Therefore, the present study suggests that the size distribution of ambient particles helps to elucidate the properties of ambient aerosols responsible for health effects.
We have a protein that has a big and stable part and another big part that moves significantly relevant to the first one and in a subset of the particles falls off. We have cleaned the dataset from junk particles and are trying to disperse the particles among 2D classes but the program over-aligns everything on the first part until the floppy part is averaged out and appears as cleaved off fuzz in 3D that cannot be refined locally or brought back by 3D classification.
Bottomline, is there a way to limit the maximum number of particles per 2D class so that we can force the program to keep things separate and try to align over the entirety of the particles? If not, any other suggestions will be greatly appreciated.
With 2D classification, I would suggest trying something like the following settings:
Initial classification uncertainty factor: 3
Number of online-EM iterations: 40
Batchsize per class: 200
Number of iterations to anneal sigma: 35
Just following up with this - if you can get to 3D, could you try the new 3D Variability algorithm?
Some notes about it here: -variability-analysis/
It can generally resolve continuous conformational flexibility quite well and may shed some light into your dataset.
If we switch sigma annealing off completely however, (by setting the iteration to start annealing to higher than the total number of iterations), these classes remain separated, and sure enough when we map them back to the micrographs they correspond to junk! Mostly intact particles but right next to gold edges, or overlapping with surface contaminants.
We have also found the same approach helpful when we have a mixture of particles, some of which are better ordered than others - otherwise the less well ordered ones have a habit of getting subsumed by the better ordered species.
In this case, another round of 2D with sigma annealing switched off, and recentering switched off, allowed identification of an additional set of bad particles, which were otherwise subsumed in the good classes.
Classification parameters and the input particle set were otherwise identical - it is clear in this case that the default noise model parameters are allowing less occupied or lower resolution classes to be sucked into the majority class, obscuring binding of a protein to the filament.
Hello @olibclarke ,
Amazing finding here. I am able to benefit from your method of turning off sigma annealing off to clean off some bad classes.
I was wondering how are you able to map the particles back to the micrographs? In my experience with CryoSPARC, I was never able to export a particle file that is readable by other software. Or is there a method in CryoSPARC that allow me to map the particles back to the micrographs?
Glad it helped! You should be able to convert the particle.cs file to a star file using the csparc2star.py utility from the pyem package, written by @DanielAsarnow, and you should be able to find further information elsewhere on the forums, hope that helps!
The number of moles in a system can be determined using the atomic mass of an element, which can be found on the periodic table. This mass is usually an average of the abundant forms of that element found on earth. An element's mass is listed as the average of all its isotopes on earth.
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A three-dimensional counting rule and its integral test system, the disector, for obtaining unbiased estimates of the number of arbitrary particles in a specimen is presented. Used in combination with ordinary and recently developed stereological methods unbiased estimates of various mean particle sizes and the variance of particle volume are obtainable on sets of two parallel sections with a known separation. The same principle allows the unbiased estimation of the distribution of individual particle volumes in sets of serial sections.
I still think the solution I gave above is the right one. You said that you have to have parents to have children, but you can choose not to render the parents, and then keyframe the number count of the children. You can have only one parent (hidden) and go from zero to whatever number of children one by one.
I think it is because of the design of the particle system itself. This may not be the case but I deduce that the particles are all generated on frame 1 and their appearance in the scene, via birth and lifetime, is managed by the particle system.
There is a node based particle build available on Graphicall. This emulates a more traditional particle fountain system. Its fun! Pull it down and mess around with node based particles today. A video tutorial as well.
I am making a defense game. Since the concept of the game is Christmas, we are using Christmas-specific particles, but since the number of sprites used for particles is so small, we need a way to increase the number of sprites. Is there a way to increase it in a particle system?
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