OpenLLM-France/wikimedia huggingface dataset:
# Structural Biochemistry/Protein function/Ligand/Metal Binding Sites
## Overview

The ability of a protein to bind metal ligands is often essential to a protein’s function. Furthermore, recent studies have indicated the existence of a new type of protein-metal interaction in which the metal confers a particular function to the protein. Using the familiar example of hemoglobin and its heme group, this new type of interaction would be analogous to attaching a heme group to a protein not naturally possessing one to allow that protein to transport oxygen. In addition, many metals have the ability to induce a conformational change in the protein that they bind to, thus altering its function once again.

## Applications
### Amyloid Mimetics

A practical application of this concept is the creation of amyloid mimetics. Because of their strength and mechanical stiffness, amyloids are attractive compounds for the construction of nanomaterials. This can be done by employing peptides with a secondary structure of alpha helices and a primary structure containing amino acids with metal-ligating side chains such as His and Cys. When metals such as Cu(II) or Zinc(II) are introduced, they will bind as ligands at the center of the helix to create the desired nanomaterials. This process can be taken further by creating a coil within a coil. This is usually done using heavy metals such as Pb and As, and results in a diverse array of nanomaterials. Alternatively, peptides can be designed to interact with a metal in a desired way, thus yielding different structures and functions. For example, addition of Cd(II) may induce a conformation in which there are four connections to the metal, whereas addition of Cu(I) may only form two connections with that same peptide.

### Inorganic Substrates

Another area of interest involving protein-metal interaction; is the for inorganic substrates in the solid state such as metals and carbon. This type of behavior has been observed in the formation of pearls, as well as in bones and tooth enamel in humans. Although a definitive relationship between protein affinity and substrate identity has not yet been established, certain observations pervade this phenomenon. For example, proteins with an affinity for Ag substrates tend to contain proportionally larger amounts of Ser and Pro, whereas proteins with an affinity for C (nanotubes) tend to contain proportionally smaller amounts of Typ and His. Another observed trend is the affinity of unfolded proteins for Au and the affinity of folded proteins for Pt.

### GFP

Protein-metal interaction is also being studied in green fluorescent protein (GFP). The concept is to create a “metal sensor” by combining the easy visibility of fluorescent compounds with the ability of proteins to bind metal ligands. Through modification of GFP, variations of GFP can be created that are specifically designed to bind a certain number of a certain metal atom, thus the amount of GFP will correspond to the unknown amount of metal ions in the sample solution. Recent studies indicate that the best results are with metals in the 2+ oxidation state, particularly Zn(II), with the exception of Ca(II) and Mg(II).
# Structural Biochemistry/Protein function/Lock and Key

In the Lock and Key Model, first presented by Emil Fisher, the lock represents an enzyme and the key represents a substrate. It is assumed that both the enzyme and substrate have fixed conformations that lead to an easy fit. Because the enzyme and the substrate are at a close distance with weak attraction, the substrate must need a matching shape and fit to join together. At the active sites, the enzyme has a specific geometric shape and orientation that a complementary substrate fits into perfectly. The theory behind the Lock and Key model involves the complementarity between the shapes of the enzyme and the substrate. Their complementary shapes make them fit perfectly into each other like a lock and a key. According to this theory, the enzyme and substrate shape do not influence each other because they are already in a predetermined perfectly complementary shape. As a result, the substrate will be stabilized. This theory was replaced by the induced fit model which takes into account the flexibility of enzymes and the influence the substrate has on the shape of the enzyme in order to form a good fit.
The active site is the binding site for catalytic and inhibition reaction of the enzyme and the substrate; structure of active site and its chemical characteristic are of specificity for binding of substrate and enzyme. Three models of enzyme-substrate binding are the lock-and-key model, the induced fit model, and the transition-state model. The lock-and-key model assumes that active site of enzyme is good fit for substrate that does not require change of structure of enzyme after enzyme binds substrate.
# Structural Biochemistry/Protein function/Major Histocompatibility Complex (MHC)/MHC Class I
## Class I MHC molecules

The Class I MHC molecules are found on all nucleated cells in the body (including cells expressing Class II MHC such as antigen presenting cells, dendritic cells, macrophages, and B cells), but are not found on non-nucleated cells such as red blood cells. It is found extending from the plasma membrane of body cells that have been infected with a virus or are otherwise abnormal (for example, cancerous). MHC Class I proteins bind to foreign antigen fragments (peptides) that are derived from intracellular proteins. For example, any cell that can become infected with a form of cancer will synthesize foreign antigens and then present them on the surface of the cell by the MHC I molecule. The T cell receptor of a Cytotoxic T cell that recognizes the MHC I-peptide complex binds to it with the aid of CD8, a protein found on the surface of cytotoxic T cells. This activates the cytotoxic T cell, stimulating its release of perforin, which increases permeability of the target cell membrane, and granzymes, which enter the target cell and initiate apoptosis, killing the target cell and fragmenting both its DNA and that of the virus. The cytotoxic T cell then detaches from the MHC I molecule and proliferates forming active cytotoxic T cells, which attack other infected body cells expressing the same antigen fragment, and memory cytotoxic T cells which will help produce a faster response to the virus if it attacks again.
Properly functioning MHC I molecules are essential in fighting cancer. That is, the immune cells of a person with defective MHC I molecules would not be able to recognize that cells had become cancerous, so no immune response would take place.
The interaction of T cells with antigen-presenting cells
1 A fragment of foreign protein (antigen) inside the cell associates with the components of an MHC molecule on the endoplasmic reticulum and is transported to the cell surface 2 The combination of MHC molecule and antigen fragment is recognized by a cytotoxic T cell 3 Cytotoxic T cells kill the infected cell.
# Structural Biochemistry/Protein function/Myoglobin's Oxygen Binding Curve

The oxygen binding curve for Myoglobin forms an asymptotic shape, which shows a simple graph that rises sharply then levels off as it reaches the maximum saturation. The half-saturation, the point at which half of the myoglobin is binded to oxygen, is reached at 2 torr which is relatively low compared to 26 torr for hemoglobin.
Myoglobin has a strong affinity for oxygen when it is in the lungs, and where the pressure is around 100 torr. When it reaches the tissues, where it's around 20 torr, the affinity for oxygen is still quite high. This makes myoglobin less efficient of an oxygen transporter than hemoglobin, which loses it's affinity for oxygen as the pressure goes down and releases the oxygen into the tissues. Myoglobin's strong affinity for oxygen means that it keeps the oxygen binded to itself instead of releasing it into the tissues.
# Structural Biochemistry/Protein function/Major Histocompatibility Complex (MHC)/MHC Class II

The Class II MHC molecules are made by antigen presenting cells (dendritic cells, macrophages, and B cells) which help trigger an acquired immune response. Antigen presenting cells ingest foreign antigens via phagocytosis or endocytosis and break it down into peptide fragments that bind to MHC Class II molecules which bring the antigen fragment to the surface of the cell to be recognized by the T cell receptor on the surface of Helper T Cells. The T cell receptor binds to the MHC II-antigen fragment complex with the aid of the CD4, a protein receptor on the surface of helper T cells, and once bound, the helper T cell becomes active and begins proliferating and secreting cytokines.
When Helper T cells proliferate, both memory helper T cells, and to a larger extent, active helper T cells are made. Memory helper T cells contribute to the acquired immune response by quickly proliferating in the case of a secondary "attack" by the antigen producing a faster, stronger response to the antigen. Active helper T cells have a shorter life span than memory helper T cells and help initiate an immune defense against the pathogen by secreting cytokines that stimulate the activation of nearby B cells and Cytotoxic T cells. Once activated, cytotoxic T cells destroy infected cells via apoptosis while the plasma B cells secrete specific antibodies that neutralize and eliminate the pathogen. The cytotoxic T cells and B cells will also proliferate to produce their respective memory cells for use in a second attack by the pathogen.
MHC CLASS II
In the case of HIV, the Helper T cells are the cells that are directly attacked by the virus. They are completely killed off, which means that no more Helper T cells are able to create cytokines and call the other phagocytic cells to reproduce and create an immune response. This causes the death of all Helper T cells, as well as the death of the immune system. Because of this, the person that has HIV/AIDS does not die of the virus, but can die of (i.e.) a common cold, because the immune system has no way to respond to it.
# SwisTrack/Components/BackgroundSubtractionGray
## Description

This component subtracts a background image from the input image. If the camera is fixed, only the objects added to the scene remain after this subtraction. However, light condition variations or the camera noise can also produce differences between the two images. Subtracting the average intensity of the two images makes the system more robust to changing light conditions.
Most of the time, a threshold is applied after this method.

## Input

A grayscale image.

## Output

A grayscale image.

## Parameters
### Background Image

Path to the background image file (grayscale). The background image must be a picture of the arena without the object to track.

### Mode

The subtraction mode:
* Abs(I - B) calculates the absolute difference between the input image and the background image. If an object has moved between the background image and the input image, this object will appear twice (indistinguishable) on the resulting image.
* Truncate(I - B, 0..255) subtracts the background image from the input image and sets all negative values to zero. Hence, objects that have moved between the background image and the input image will only appear once.
* Truncate(B - I, 0..255) is similar, but subtracts the input image from the background.

### Correct Mean

Specifies whether to correct the average intensity value of the input image with the average intensity value of the background image. This option makes the tracking less sensitive to varying light conditions.
# SwisTrack/Components/BinaryDilation
## Description

Dilation is a method used in image processing to enlarge the white areas of the binary image. The structuring element is a 3x3 rectangle (using the 8 nearest pixels). Combining erosion and dilation allows you to perform opening and closing operations, which remove individual black or white pixels, or smooth out the borders.
It is generally adviced to use the same number of dilation and erosion interactions.

## Input

A binary (black and white) image.

## Output

A binary (black and white) image.

## Parameters
### Iterations

Number of dilations to apply on the image.
# SwisTrack/Components/AdaptiveBackgroundSubtractionColor
## Description

This component subtract the background image from the input image. If the camera is fixed, only the objects added to the scene remain after this subtraction. However, lighting condition variations or the camera noise can also produce a difference between the two images. Subtracting average intensity of the two images makes the system less sensible to lighting conditions.
Method doesn't need a background image as it approximate the background from the prior frames. However the moving objects tend to let a trail behind them.
Most of the time, a threshold is applied after this method, to separate the background from the tracked objects.

## Input

A color image.

## Output

A color image.

## Parameters
### Correct mean

Specify whether to correct the average intensity value of the input image with the average intensity value of the background image. This option makes the tracking less sensible to lighting conditions variations.

### Update proportion

The background image is continuously update with the update image following the following equation:
$backgroundImage_{t}=backgroundImage_{t-1}\cdot (1-updateProportion)+inputImage_{t}\cdot updateProportion$
Therefore, increasing the proportion makes the update of the background more reactive, but will remove object moving slowly.
# SwisTrack/Components/BinaryMask
## Description

This component will mask certain areas of your image, and therefore allow you to remove particles that are not in your area of interest (e.g. the arena).

## Input

File:SwisTrack BinaryMaskInput.png
A binary image.

## Output

File:SwisTrack BinaryMaskOutput.png
A binary image.

## Parameters
### Mask image

A binary (black-and-white) image with the mask.

### Mode

One of the following combination modes:
* Black -> black: Black pixels in the mask image will be black in the output image. All other pixels remain unchanged.
* White -> white: White pixels in the mask image will be white in the output image. All other pixels remain unchanged.
* Black -> white: Black pixels in the mask image will be white in the output image. All other pixels remain unchanged.
* White -> black: White pixels in the mask image will be black in the output image. All other pixels remain unchanged.
# SwisTrack/Components/BackgroundSubtractionColor
## Description

This component subtracts a background image from the input image. If the camera is fixed, only the objects added to the scene remain after this subtraction. However, lighting condition variations or camera noise can also produce differences between the two images. Subtracting average intensities of the two images makes the system more robust towards changing lighting conditions.
Most of the time, a threshold is applied after this method, to separate the background from the tracked objects.

## Input

A color image.

## Output

A color image.

## Parameters
### Correct Mean

Specify whether to correct the average intensity value of the input image with the average intensity value of the background image. This option makes the tracking less sensible to lighting conditions variations.

### Background Image

Path to the background color image file. The background image must be a picture of the arena without the object to track.
# SwisTrack/Components/AdaptiveBackgroundSubtractionCheungKamath
## Description

Super awesome foreground segmentation, based on the paper by Cheung and Kamath (2005). I just copied the paper. Short story: Take a short term background model, like frame differencing, and a long term background model, like median, and combine them. Short-term subtraction gives you leading edges of moving objects. Long-term subtraction gets you filled in foreground objects, but also "ghosts" and "shadows" trailing behind them, that are really background. By combining them you do really well. The long-term background model comes from mDataStructureImageBinary, and it does the short term model itself with frame differencing. I use AdaptiveBackgroundSubtractionMedian for the long term model.

## Parameters
### Threshold

Threshold to use when computing the short-term forground model (which is done with frame differencing.)
# SwisTrack/Components/AdaptiveBackgroundSubtractionMedian

Implements the Apprixmated Mean Filter for background modeling described in N. J. B. McFarlane1 and C. P. Schofield1, Segmentation and tracking of piglets in Images.
Median is better for background subtraction because it does better rejection of outliers. This algorithm recursively approximates the median image, and then subtracts the background model from the current frame to find foreground objects. Its disadvantage is that it takes a long time to update the background when an object starts moving after being still for a long time. Combine it with BackgroundSubtractionCheungKamath to reject that type of noise.
# SwisTrack/Components/BlobDetectionMinMax
## Description

This component detects the blobs resulting of a thresholding step and keeps those with their area, orientation and compactness between specified bondaries. A maximum number of object can be selected, and in this case, only the biggest objects will be kept.

## Input

A binary (black and white) image.

## Output

Particles.

## Parameters
### Max. blobs

Maximum number of blobs kept. Only the biggest blobs are selected.

### Area Selection

Choose if a selection based on the blobs area is done.

### Min. blob size

Minimal area of a selected blob in pixels.

### Max. blob size

Maximal area of a selected blob in pixels.

### Compactness Selection

Choose if a selection based on the blobs compactness is done.

### Min. compactness

Minimal compactness of a selected blob (between 0 and 1).

### Max. compactness

Maximal compactness of a selected blob (between 0 and 1).

### Orientation Selection

Choose if a selection based on the blobs orientation is done.

### Min. orientation

Minimal orientation of a selected blob in degrees.

### Max. orientation

Maximal orientation of a selected blob in degrees.
# SwisTrack/Components/AdaptiveBackgroundSubtractionGray
## Description

This component subtracts a background image from the input image. If the camera is fixed, only the objects added to the scene remain after this subtraction. However, changing light condition or camera noise can also produce a difference between the two images. Subtracting the average intensity of the two images makes the system more robust towards changing light conditions.
This method doesn't need a background image file, as it approximates the background from the prior frames. However, depending on the configuration, moving objects tend to leave a trail behind them, or disappear if they stay too long at the same place.
Optionally, this component allows you to take the current image as background image, simply by clicking on a button. This comes in handy if you are changing your experimental setup in an ad hoc fashion (but not during one experiment), and avoid the overhead of saving the background image to a file. In this case, it is advised to set the update rate to zero.
Most of the time, a threshold is applied after this method, to separate the background from the tracked objects.

## Input

A grayscale image.

## Output

A grayscale image.

## Parameters
### Correct mean

Specifies whether to correct the average intensity value of the input image with the average intensity value of the background image. This option makes the tracking less sensitive to varying light conditions.

### Mode

The subtraction mode:
* Abs(I - B) calculates the absolute difference between the input image and the background image. This will cause moving objects to leave a trail behind them.
* Truncate(I - B, 0..255) subtracts the background image from the input image and sets all negative values to zero. This will cause bright moving objects to slightly fade out towards where they are coming from. Dark moving objects will not be visible, but their trail will.
* Truncate(B - I, 0..255) is similar, but subtracts the input image from the background. This inverts the behavior for bright and dark moving objects.

### Update rate

The background image is continuously updated with the update image following the following equation:
$backgroundImage_{t}=backgroundImage_{t-1}\cdot (1-updateRate)+inputImage_{t}\cdot updateRate$
Therefore, increasing the proportion makes the update of the background more reactive, but will remove object moving slowly.

### Reset background image

Takes the next frame as background image, i.e.
$backgroundImage_{t}=inputImage_{t}$
instead of updating it. This is useful if you changed the environment significantly.
# SwisTrack/Components/BinaryErosion
## Description

Erosion is a method used in image processing to enlarge the black areas of the binary image. The structuring element is a 3x3 rectangle (using the 8 nearest pixels). Combining erosion and dilation allows you to perform opening and closing operations, which remove individual black or white pixels, or smooth out the borders.
It is generally adviced to use the same number of dilation and erosion interactions.

## Input

A binary (black and white) image.

## Output

A binary (black and white) image.

## Parameters
### Iterations

Number of erosions to apply on the image.
# Swing Dancing/Musicality

You may have noticed on the dance floor that some dancers are able to perform moves which match changes in the flow of the music. These dancers haven't necessarily memorized the music -- most swing music actually has a regular and predictable structure. The chorus of most swing songs consists of 32 bars which as a whole follow an AABA structure. Each individual letter consists of four 8-count sections and the B letter has a different melody. The fourth 8-count section of each letter is often an ideal time to execute a break.
If you have swing music available, try listening to it now and see if you can notice this structure.
# SwisTrack/Batch

SwisTrack supports a headless mode, that runs without a GUI for batch processing. It lives in swistrack/Code/batch as a separate application binary. To use it, compile it by running "make" in that directory. Run it by passing the configuration file on the command line.
Here's an example:
# Swing Dancing/Terminology

Charleston-what you do- rock, kick, step, double kick, & step.
# Swing Dancing/Skip up

Skip ups are traditional Lindy Hop dance moves that cannot quite be categorized as either basic, side pass, swing out, or aerial dance moves.
The names of these moves are somewhat arbitrary, and different people call the same move by different names.

## Skip Ups (Walk the Dame, Jig Walks)

The basic skip up is a six count move, as follows:
On counts 1 and 2, the lead rock steps. On counts 3 and 4, he kicks left and steps left. On counts 5 and 6, he kicks right and steps right. (The kick step left and kick step right may be repeated any number of times.)
Skip Ups in Closed: Partners need to be standing offset, to not kick each other. One kick goes inbetween the partners legs (but the kick should be kept very low anyway). End with tuck turn or big kick at end. Both directions.
Skip Ups in Open:
Change Direction: Instead of repeating kicks, the next kick left should push on hip, and on step, they turn and rock step. This switches the feet orientation.
Free Spin: Instead of changing direction, they can do a free spin and come back together in the same position.
Tempo: Fast or slow.
Feet: Step, kick step, kick ball changes.
Arms: The arms usually hold onto the partner, so there are few variations (tied up).

## Variations

8-count variation: counts 1-5 remain as before, 5-8 are kick right, kick left (and step on right), kick right (and step on left), kick left (and step on right). These kicks are faster, just one count each, so the rhythm is rock - step - kick - step - kick - kick - kick - kick.
Direction: Forward and backward.
Speed:
Footwork: They can step, step; kick step, kick step; or other.