DETERMINING INTERACTIONS BETWEEN CELLULAR BODIES AND A FUNCTIONALIZED WALL SURFACE

Abstract

A method for determining interaction between cellular bodies and a functionalized wall surface comprises: obtaining a sequence of images representing manipulating cellular bodies in a holding space having a functionalized wall surface configured to bind the cellular bodies that includes applying a force; tracking locations of pixel groups in respective images out of the sequence of images, each pixel group in the images representing a cellular body out of the cellular bodies, the locations in the respective images defining a trajectory of the cellular body moving relative to the functionalized wall surface; determining one or more speed values of the cellular body at one or more locations of the trajectory, the one or more speed values being higher than zero; and, classifying that the cellular body is attached to the functionalized wall surface based on the one or more speed values and at least one threshold speed value.

Claims

1. A method for determining interaction between cellular bodies and a functionalized wall surface, the method comprising: obtaining a sequence of images representing manipulating cellular bodies in a holding space, the holding space including a functionalized wall surface configured to bind the cellular bodies, the manipulating including applying a force on cellular bodies bound to the functionalized wall surface; tracking first locations of first pixel groups in respective first images out of the sequence of images, each first pixel group in the first images representing a first cellular body out of the cellular bodies, the first locations in the respective first images defining a first trajectory of the first cellular body moving relative to the functionalized wall surface; determining one or more first speed values of the first cellular body at one or more locations of the first trajectory, the one or more speed values being higher than zero; and, classifying that the first cellular body is attached to the functionalized wall surface based on the one or more first speed values and at least one threshold speed value.

2. The method according to claim 1, further comprising: tracking second locations of second pixel groups in respective second images out of the sequence of images, each second pixel group in the second images representing a second cellular body out of the cellular bodies, wherein the second locations in the respective second images define a second trajectory of the second cellular body moving relative to the functionalized wall surface; determining one or more second speed values of the second cellular body at one or more positions of the second trajectory; classifying that the second cellular body is detached from the functionalized wall surface based on the one or more second speed values and the at least one threshold speed.

3. The method according to claim 1, further comprising: tracking third locations of third pixel groups in respective third images out of the sequence of images, the third images being later in the sequence of images than the first images, each third pixel group representing the first cellular body, wherein the third locations in the respective third images define a further part of the first trajectory; determining one or more further speed values of the first cellular body relative to the functionalized wall surface at one or more points of the further trajectory; classifying that first cellular body is detached from the functionalized wall surface based on the one or more further speed values.

4. The method according to claim 1, further comprising: determining that the first cellular body is located in a cluster, a cluster being an aggregation of cellular bodies, and refraining from classifying the first cellular body in the cluster as attached to the functionalized wall surface.

5. The method according to claim 1, wherein determining a first speed of the first cellular body at a location at the first trajectory comprises: determining or receiving time instances associated with two or more images in which the first cellular body is located at or around one of the one or more locations of the first trajectory; and, determining the first speed based on the time instances and the locations of the first cellular body in the two or more images.

6. The method according to claim 1, wherein the sequence of images includes one or more images of the holding space before applying the force to the cellular bodies, the method further comprising: detecting the location of the first cellular body in at least one of the one or more images of the holding space before applying the force to the cellular bodies, the location defining an initial location of the first cellular body bound to the functionalized wall.

7. The method according to claim 6, wherein a distance between the initial location and a further location on the first trajectory and/or a shape of the first trajectory is used to determine that the first cellular body is attached to the functionalized wall surface by means of a tether.

8. The method according to claim 6, wherein the sequence of images comprises initialization images representing the one or more cellular bodies binding to the functionalized wall surface, and wherein detecting a pixel group at an initial location in the initial image comprises: detecting initialization pixel groups in the respective initialization images, the initialization pixel groups representing an initialization movement of the first cellular body, and tracking the location of the first cellular body in the initialization images, wherein for each initialization image the first cellular body is classified as being trackable or not trackable, and determining a settling event if during tracking the first cellular body it is classified as non-trackable, the location in the initialization image at which the settling event is detected defining the initial location in the initial image and thus defining a pixel group at the initial location in the initial image, and wherein the detected pixel groups in the respective first images are detected at different locations in the respective first images, the different locations forming a tracking path, the tracking starting point of the tracking path defining a pop-up location in a tracking starting image out of the first images, the tracking starting image being associated with a tracking starting time instance, the method comprising determining a distance between the initial location and the pop-up location, and determining a time difference between the tracking starting time instance and a force application time instance indicating a time instance at which a force was applied to the first cellular body, determining a speed based on the time difference between the tracking starting time instance and the force application time instance and on the distance between the initial location and the pop-up location, determining that this speed is lower than a second threshold speed and/or higher than a third threshold speed, and based on this determination, determining that the first cellular body is associated with the initial location.

9. The method according to claim 1, comprising determining detachment images out of the sequence of images, the detachment images being the earliest images in the sequence of images that comprise pixel groups representing a speed of movement of the first cellular body that is higher than said threshold speed.

10. The method according to claim 1, wherein detecting the pixel groups in the respective first images comprises: detecting a pixel group in an image out of the first images, the detected pixel group comprising pixels representing the first cellular body, said pixels having respective pixel values that distinguish them from pixels representing a background of the image, and determining a region of interest in the image, the region of interested comprising the pixels representing the first cellular body and pixels representing the background of the image, and analyzing said region of interest in a subsequent image out of the first images, and identifying pixels in said region of interest having respective pixel values that distinguish them from pixels representing a background of the subsequent image, determining said distinct pixels in the region of interest to represent at least part of the first cellular body, based on the identified pixels in the region of interest in the subsequent image, determining a further pixel group in the subsequent image representing the first cellular body.

11. The method according to claim 10, further comprising updating the region of interest such that it comprises the further pixel group in the subsequent image, and analyzing said updated region of interest in a further subsequent image, subsequent to the subsequent image, out of the first images, and identifying in the updated region of interest pixels having respective pixel values that distinguish them from pixels representing a background of the further subsequent image, determining said distinct pixels in the updated region of interest to represent at least part of the first cellular body, and based on the identified pixels in the region of interest in the subsequent image, determining an even further pixel group in the further subsequent image representing the first cellular body.

12. A method for determining interaction between cellular bodies, the method comprising: obtaining a sequence of images representing manipulating cellular bodies in a holding space, the holding space including a functionalized wall configured to bind the cellular bodies, the manipulating including applying a force on the settled cellular bodies away from the functionalized holding space; for each of a plurality of cellular bodies represented in the sequence of images: tracking locations of pixel groups in respective images out of the sequence of images, each pixel group in the images representing a cellular body out of the cellular bodies, the locations in the respective images defining a trajectory of the cellular body moving relative to the functionalized wall surface; determining one or more first speed values of the cellular body at one or more locations of the trajectory, the one or more speed values being higher than zero; classifying that the cellular body is attached to or detached from the functionalized wall surface based on the one or more speed values and at least one threshold speed value.

13. A method for determining interaction between cellular bodies comprising providing a sample holder comprising a holding space, wherein the holding space comprises a fluid medium and comprises the functionalized wall surface, and the one or more cellular bodies in the fluid medium, wherein the one or more cellular bodies are bound to the functionalized wall surface, applying a force to the one or more cellular bodies for separating at least some of the one or more cellular bodies from the functionalized wall surface, capturing a sequence of images from the one or more cellular bodies while said force is applied, and determining interaction between the one or more cellular bodies and functionalized wall surface based on the captured images in accordance with claim 1.

14. A data processing system comprising a a computer readable storage medium having computer readable program code embodied therewith, and a processor, preferably a microprocessor, coupled to the computer readable storage medium, wherein responsive to executing the computer readable program code, the processor is configured to perform the method according to claim 1.

15. A computer program or suite of computer programs comprising at least one software code portion or a computer program product storing at least one software code portion, the software code portion, when run on a computer system, being configured for executing the method according to claim 1.

16. A system for determining interaction between cellular bodies and a functionalized wall surface, the system comprising a sample holder comprising a holding space for holding a fluid medium, a functionalized wall surface and one or more cellular bodies, and a force generator for providing a force to the one or more cellular bodies in the holding space, and an imaging system for capturing images of the one or more cellular bodies in the holding space, and a data processing system according to claim 14.

17. The method according to claim 1 wherein classifying that the first cellular body is attached to the functionalized wall surface based on the one or more first speed values and at least one threshold speed value includes determining that the one or more first speed values are lower than the at least one threshold speed value.

18. The method according to claim 2, wherein classifying that the second cellular body is detached from the functionalized wall surface based on the one or more second speed values and the at least one threshold speed includes determining that the one or more second speed values are higher than the at least one threshold speed.

19. The method according to claim 3, wherein classifying that first cellular body is detached from the functionalized wall surface based on the one or more further speed values includes determining that the further speed is higher than the at least one threshold speed.

20. The method according to claim 8, wherein determining that the first cellular body is associated with the initial location comprises determining that the first cellular body is attached to the functionalized wall surface at the initial location.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Aspects of the invention will be explained in greater detail by reference to exemplary embodiments shown in the drawings, in which:

[0093] FIG. 1 schematically depicts a force spectroscopy system according to an embodiment;

[0094] FIG. 2A-2C schematically depict cross-sectional views of a sample holder according to an embodiment;

[0095] FIG. 3A-3D depict schematics of processes occurring in a holding space according to an embodiment;

[0096] FIG. 4 illustrate examples of avidity curves measured using an acoustic force spectroscopy system;

[0097] FIG. 5 schematically illustrates a sequence of images A-F representing several cellular bodies in the holding space according to an embodiment;

[0098] FIG. 6A is a combined image in which all pixel groups representing cellular bodies in images A-F are indicated;

[0099] FIG. 6B depicts an image taken by a camera of an acoustic force spectroscopy system;

[0100] FIG. 7 illustrates how tracking pixel groups representing a movement of a cellular body may be performed according to an embodiment;

[0101] FIG. 8 depicts a flow diagram of a method of determining an interaction between cellular bodies and a functionalized wall according to an embodiment of the invention.

[0102] FIG. 9 depicts examples of avidity curves determined based on a method according to an embodiment of the invention and associated RMS errors.

[0103] FIG. 10 illustrates an embodiment using centrifugal force application.

[0104] FIG. 11 illustrates a data processing system according to an embodiment.

DETAILED DESCRIPTION OF THE DRAWINGS

[0105] In the figures, identical reference numbers indicate identical or similar elements. FIG. 1 is a schematic drawing of an embodiment of an acoustic force spectroscopy AFS system 2 that can be used with the embodiments described in this application. FIG. 2A depicts a cross section of a sample holder and FIG. 2B illustrates a detail of the sample holder of FIG. 2A as indicated with IIB. The system 2 comprises a sample holder 3 comprising a holding space 4 for holding a sample 6 comprising one or more biological cellular bodies 51 in a fluid medium 40 as exemplary cellular bodies of interest.

[0106] At the beginning of a measurement the cellular bodies are typically attached to the functionalized wall surface. The holding space 4 may be part of a flow cell (also referred to as a microfluidic cell). The system 2 may comprises a force generator 8 for providing a force to the one or more cellular bodies in the holding space 4. The force generator, in an embodiment, may be an acoustic wave generator based on a piezo element, connected to the sample holder 3 for generating a bulk acoustic wave in the holding space 4 so that a force is exerted on cellular bodies that may be present in the holding space 4. The force field generator 8 may be connected to a controller 10, which may be connected to a data processing system 100 as described herein, so that the force exerted on the cellular bodies can be controlled.

[0107] Further, the depicted system may comprise an imaging system configured to capture images of the one or more cellular bodies in the holding space 4. The imaging system may include a microscope 12 including optics, e.g. adjustable objective 14, and a camera 16 for capturing images, e.g. video frames, of the processes in the holding space 4. The imaging system may be connected to the data processing system 100 that is configured to perform any of methods, in particular the image analyses, as described in this application. The data processing system may also be configured to control any of the elements of the depicted system, such as the controller 10 and thus the force generator, and one or more, e.g. all, elements of the imaging system, which are further described below. An embodiment of the data processing system 100 is described in more detail with reference to FIG. 11 below.

[0108] The imaging system may comprise a light source 1 for illuminating the sample, including the functionalized wall surface described herein, using any suitable optics (not shown) to provide a desired illumination intensity and intensity pattern, e.g. plane wave illumination, Khler illumination, etc., known per se. Here, the light 22 emitted from the light source 1 may be directed through the force field generator 8 to (the sample in) the sample holder 3 and sample light 24 from the sample is transmitted through the objective 14 and through an optional tube lens 26 and/or further optics (not shown) to the camera 16. The objective and the camera may be integrated. In an embodiment, two or more optical detection systems, e.g. with different magnifications, may be used simultaneously for detection of sample light, e.g. using a beam splitter.

[0109] In another embodiment, not shown but discussed in detail in WO2014/200341, the system 2 may comprise a partially reflective reflector and light emitted from the light source is directed via the reflector through the objective and through the sample, and light from the sample is reflected back into the objective, passing through the partially reflective reflector and directed into a camera via optional intervening optics. Further embodiments may be apparent to the reader.

[0110] The sample light 24 may comprise light affected by the sample (e.g. scattered and/or absorbed) and/or light emitted by one or more portions of the sample itself e.g. by chromophores/fluorophores attached to the cellular bodies.

[0111] Some optical elements in the imaging system may be at least one of partly reflective, dichroic (having a wavelength specific reflectivity, e.g. having a high reflectivity for one wavelength and high transmissivity for another wavelength), polarization selective and otherwise suitable for the shown setup. Further optical elements e.g. lenses, prisms, polarizers, diaphragms, reflectors etc. may be provided, e.g. to configure the system 2 for specific types of microscopy.

[0112] The sample holder 3 may be formed by a single piece of material with a channel inside, e.g. glass, injection moulded polymer, etc. (not shown) or by fixing different layers of suitable materials together more or less permanently, e.g. by welding, glass bond, gluing, taping, clamping, etc., such that a holding space is formed in which the fluid and functionalized wall surface are contained, at least for the duration of an experiment. While, the system of FIG. 1 includes an acoustic force generator, other ways of applying a force on the cells may be used as well. For example, a system that uses a centrifugal force generator for applying a force on the cells is described in more detail with reference to FIG. 10. Since the system of FIG. 1 is configured to apply a force in a controlled manner, by means of the force generator, the system may also be referred to as a force spectroscopy system.

[0113] With the system depicted in FIG. 1, a force can be applied to one or more cellular bodies in the holding space for separating at least some of the one or more cellular bodies from the functionalized wall surface. Then, the imaging system can be used to capture a sequence of images from the one or more cellular bodies while said force is applied. Based on the obtained images, the interaction between the one or more cellular bodies and functionalized wall surface can be determined in accordance with any of the methods described herein.

[0114] FIG. 2A-2C schematically depict cross-sectional views of a sample holder according to an embodiment. In the depicted embodiment, the sample holder is a so-called flow cell. The sample holder 3 may comprise a first base part 301 that has a recess being, at least locally, U-shaped in cross section and a cover part 302 to cover and close (the recess in) the U-shaped part providing an enclosed holding space in cross section.

[0115] Further, the sample holder 3 may be connected to a fluid flow system 32 for introducing fluid and unbound cellular bodies, such as cells, into the holding space of the sample holder 3 and/or removing fluid from the holding space, e.g. for flowing fluid through the holding space (see arrows in FIG. 2A depicting the flow direction). The fluid flow system may be comprised in or part of a manipulation and/or control system including one or more of reservoirs 34, pumps, valves, and conduits 36,38 for introducing and/or removing one or more fluids, sequentially and/or simultaneously. The sample holder 3 and the fluid flow system 32 may include connectors, which may be arranged on any suitable location on the sample holder, for coupling/decoupling. The sample holder may further include a force field generator 8, e.g. an acoustic wave generator which may be implemented based on a (at least partially transparent) piezoelectric element connected to a controller 10.

[0116] FIG. 2B schematically depicts a cross-section of part of the sample holder including objective 14 that is positioned underneath part of a sample holder (a chip) wherein the sample holder may comprise a capping layer 301, a matching layer 302, a fluid medium 40 contained in the holding space formed by the capping and the matching layer, and part of a force generator 8, e.g. a piezo element. An immersion liquid 42 between the objective and the capping layer may be used to improve the optical numerical aperture (NA) of the imaging system. Application of an AC voltage to the piezo element at an appropriate frequency will generate a resonant bulk acoustic standing wave 44 in the sample holder. The standing wave may have a nodal plane 46 in the fluid layer at a certain height above the functionalized wall surface 48 formed on the wall of the sample holder. Cellular bodies 50 are bound to the functionalized wall surface 48. The standing wave may also have lateral nodes 52.sub.1,2. Cellular bodies that have a positive acoustic contrast factor with respect to the fluid medium will experience a force towards to the nodes.

[0117] One or more software programs that run on the data processing system 100 of the system may be configured to control the camera, the force field generator and the flow cell to conduct different experiments. In a typical experiment, cellular bodies, e.g. effector cells, may be flushed into the holding space of the flow cell and may interact, e.g. bind, with elements provided on the functionalized wall surface, such as target cells or antigens. This interaction can be probed by analyzing the response of cellular bodies that are bound to the functionalized wall surface as a function of the applied force. As shown in the figure, an acoustic force is applied perpendicularly to the cellular bodies bound to the functionalized wall surface (indicated with upward open arrows) and one or more cellular bodies may detach from the target cells and migrate to the acoustic node at a certain applied force (migration vectors for the cellular bodies are indicated with solid black arrows in FIGS. 2B and 2C). This way, detachment events for different cellular bodies 51.sub.1,2 may occur wherein the detachment events are associated with different applied forces. During the ramp force, the spatiotemporal response of the cellular bodies is captured by the imaging system as described with reference to FIG. 1. By analyzing the video frames, also referred to as images, information about interaction between cellular bodies, e.g. effector and target cells, can be determined. To that end, the data processing system, also referred to as the computer, may include an image processing module comprising one or more image processing algorithms for analyzing the response of the cells when they are manipulated in the flow cell using the force field generator. The image analysis of the video frames is described hereunder in greater detail.

[0118] FIG. 2C schematically depicts a cross-section of part of the sample space that is similar to the one of FIG. 2B. In this case however, when a force is applied to the cellular bodies, some of the cellular bodies may move away from the functionalized wall surface but not fully detach from the surface. As shown in FIG. 2C, depending on the circumstances and the experiment different types of cell behavior may occur. A first example may include the formation of a so-called tether cell 55, wherein a cell will move away from the wall surface due to the applied force but will still be attached to the wall surface by a tether (e.g. a membrane tube). The tether may increase in length during the application of the force. The tether will impede or at least slow down the movement of the cell towards the acoustic node. Thus, during imaging of the holding space, such events will be visible as a cell that moves slower towards a node that fully detached cells. In some cases, if the tether reaches a certain maximum length and/or if the force reaches a certain value, the tether may break and the cell may become fully detached. At that moment, the cell may start moving faster towards the acoustic node. A second example is the formation of a so-called hinge cell 53. During the application of a force, such cell will slightly displace away from the wall surface, but will be kept connected at one point with the target cell. The connection point does not coincide with the centre of gravity of the cell so that the cell will start to rotate (indicated with the curved arrow). Hence, the connection point will function as a hinge that causes the cell to slightly displace and rotate when a force is applied. Thus, during an AFS experiment different cells can be classified, including: [0119] 1) detached cells, which start to move unhindered towards an acoustic node after a certain force is applied [0120] 2) fixed cells which do not move substantially moving substantially during the entire experiment; [0121] 3) hinge cells, which start moving a little bit close to their incubation site while still (at least partly) bound. For example, cells that are bound to the monolayer with a small part of their surface (but not directly below the center of the cell), allowing them to hinge about the connection point when a force is applied perpendicular to the functionalized wall surface. Hinge cells can either stay bound, form tethers or fully detach at some moment in the experiment; and [0122] 4) tether cells, which move away from their incubation site and extend an elongated tube of cell membrane from the functionalized wall surface.

[0123] For both hinge cells and tether cells it may be preferred not to count them as detached and/or to not count the force at which the cell first moves as a detachment force because the underlying bond at the cell-cell attachment focus has not ruptured at the moment of first movement of the cells. This way, parameters like avidity curves may be improved by not counting these events or by classifying them differently.

[0124] FIG. 3A-3D depict schematics of processes occurring in a holding space, e.g. of a microfluidic cell, of an AFS system, wherein the holding space may comprise a functionalized wall surface that is configured to bind cellular bodies. To this end, the functionalized wall surface may comprise target cells. The holding space may be part of an AFS system as described with reference to FIGS. 1 and 2. The processes in the holding space may be imaged from below or from the top using an imaging system, e.g. as described with reference to FIG. 1. As depicted in FIG. 3A, the process may start with flushing cellular bodies 50, e.g. effector cells, into the holding space, comprising a functionalized wall 48 including target cells 56. The introduction of the cells 50 into the holding space may take a predetermined period of time, e.g. between 1 and 5 seconds. After flushing, the cells are allowed to settle onto the functionalized wall 48 comprising the target cells 56 (FIG. 36). When the cellular bodies reach the functionalized wall, the cells may move around over the functionalized surface until they bind to a location on the surface, for example until they find a suitable target cell to bind to (surveillance) thus forming a bound effector-target cell pair 58 (FIG. 3C). The steps of effector cells settling onto the functionalized wall and binding to it may be referred to as the incubation phase. In a typical experiment, incubation may take up to 1-15 minutes or longer.

[0125] The incubation phase may be imaged and when the cells are introduced into the holding space and move towards the functionalized wall, groups of pixels representing cells in the captured images may be detected and tracked. After the incubation phase, a force may be applied to the cellular bodies 50 that are bound to the functionalized wall surface. The force may have a direction away from the functionalized wall surface, e.g. substantially perpendicular to the functionalized wall surface. Typically, a force ramp will be applied to the cellular bodies, so that if the force becomes larger than a binding force, they will start to move away from the functionalized wall surface in the direction of the force (FIG. 3D).

[0126] When the force is sufficiently large, a cellular body will move away from the functionalized wall surface in a direction that depends on the applied force, which may have an axial component perpendicular to the functionalized wall (e.g. the z-direction) and two lateral components in the plane of the functionalized wall (e.g. the x and y direction). As discussed with reference to FIGS. 2B and 2C different types of spatio-temporal cell movement may be observed which includes detachment events, i.e. events wherein a cell fully detaches from a target cell, without being affected by the formation of a tether or a hinge. The time at which these events occur may determine the force that is exerted on the effector cells. In a typical experiment, the force ramp may take between 2-10 minutes, but it can also be shorter or longer.

[0127] Based on a measurement scheme as described with reference to FIG. 3, various parameters of the cellular bodies can be determined. For example, FIG. 4 depicts two cell avidity curves which may be determined by applying a force ramp to the functionalized wall surface and determining the number of attached cellular bodies as a function of the applied force. Based on the type of cellular bodies, and based on target cells, or other elements such as antigens, provided on the functionalized wall surface, cellular bodies may exhibit a low cell avidity curve 60 (weak binding forces between effector and target cells) or a high cell avidity curve 62 (strong binding forces between effector and target cells).

[0128] FIG. 5 schematically illustrates a sequence of images A-F representing several cellular bodies in the holding space. The images, which may also be referred to as video frames that constitute a video, are each associated with a respective time instance. Preferably, each image is associated with a state of the force generator that is used to apply a force to the cellular bodies and/or with a force applied to the cellular bodies. A state of the force generator may for example indicate a value of angular velocity if the force generator is a centrifuge system. Alternatively, the state of the force generator may indicate the frequency and/or amplitude at which piezo element of an acoustic wave generator vibrates. Typically, the state of the force generator is monitored over time, which allows to determine for each image the corresponding state or force based on the time instance associated with the image in question. It is submitted that the images in these figures are simplified for clarity. In real experiments the images may include hundreds or even thousands of cells.

[0129] The cellular bodies are schematically depicted as black solid circles in the images. The cellular bodies are typically represented by pixel groups formed by pixels having a value that distinguishes them from pixels representing background, e.g. representing the functionalized wall surface. In an embodiment, pixels in the pixel groups representing the cellular bodies for example have a relatively high intensity. For example, cellular bodies may have been fluorescently labelled which may have caused the cellular bodies to light up against a dark background upon appropriate illumination. In another embodiment, a pixel group may have a particular features, e.g. shape and/or size, that matches features of a cellular body.

[0130] Each black solid circle in the images A-F may be understood to be a pixel group as referred to in this disclosure. To illustrate, 64 indicates a pixel group in image A, 66 indicates a pixel group in image B, 68 indicates a pixel group in image C, 70 indicates a pixel group in image D, 72 indicates a pixel group in image E, 74 indicates a pixel group in image F. Further, these pixel groups 64-74 represent the same cellular body. As such, these pixel groups 64-74 represent a movement of the cellular body relative to the sample surface. Similarly, pixel groups 76, 78, 80, 82 in respective images A, B, C, D, represent a movement relative to the functionalized wall surface of another cellular body represented in the sequence of images.

[0131] The images show that there are two clusters of cellular bodies, indicated by 84 in image A. These clusters may be formed by a standing acoustic wave generating a force that directs all separated cellular bodies to a node of the acoustic wave. This causes the cellular bodies to accumulate in regions 84. During an experiment more and more cellular bodies typically separate from the functionalized wall surface, for example due to the force applied to the cellular bodies increasing. This may result in more and more accumulation in the cluster regions. 84.

[0132] Image A may be understood to be an initial image, at least for the cellular body represented by pixel group 76 and the cellular body represented by pixel group 64 in the sense that these cellular bodies are bound to the functionalized wall surface and/or in the sense that they at the same position as they were before any force was applied to the cellular bodies, i.e. they have not moved yet during the experiment. Pixel group 76 thus defines an initial location for one cellular body and pixel group 64 another initial location for another cellular body. In fact, it can be assumed for clarity in the explanation here that each cellular body that is not in a cluster 84 at the time image A was captured is at its initial position, i.e. has not moved since the beginning of the experiment.

[0133] The pixel groups representing cellular bodies at their initial position may be detected based on the aberrant values of their pixels relative to the values representing a background of the image, e.g. representing the functionalized wall surface.

[0134] FIG. 6A is a combined image in which all pixel groups representing cellular bodies in images A-F are indicated. For clarity, the cellular bodies that were already present in the clusters 84 are not shown. This combined image clearly shows how pixel groups represent a movement of a cellular body. To illustrate, it was noted already that pixel groups 76, 78, 80 represented the same cellular body. In FIG. 6A, these pixel groups are indicated and connected by a line, which may be referred to as a tracking path. The tracking path, a trajectory of the moving cellular body through the holding space, thus indicates how the cellular body moved during the time period covered by images A-F, e.g. the time period of the force ramp. Likewise, pixel groups 64, 66, 68, 70, 72 and 74 also represent the same cellular body.

[0135] The speed of a cellular body moving along its path can be determined based on the distance between adjacent pixel groups on the path. To illustrate, the speedat the beginning of the experimentof the movement of the cellular body represented by pixel group 76 can be determined based on the distance 92 and the time difference between the time instance respectively associated with image A and image B. The distance may be expressed as a pixel distance, for example may be 3.6 pixels. The speed determined in such manner may be compared to a threshold speed. In an embodiment, the speed of cellular body represented by pixel group 76 is higher than this threshold. Based on this determination, it may be determined that this cellular body has detached from the functionalized wall surface.

[0136] Cellular body represented by pixel group 90 in image A is different in that sense. As can be seen from the figure, this cellular body has moved, yet at relatively low speed. Again, this speed may be compared, in an embodiment, to a threshold value, preferably the same threshold value as above, and it may be determined that this cellular body is still attached to the functionalized wall surface. Even further, it can be seen that this cellular body does not move further from its initial position, indicated by pixel group 90 (the solid circle) than a threshold distance. The threshold distance is indicated by circle 98. Based on determining that this cellular body has not travelled further than this threshold distance, it may be determined that this cellular body is a hinged cell, meaning that it is tightly attached to the functionalized surface at a point which is displaced laterally with respect to the center of mass of the cellular body in relation to the direction of the applied force. Cellular body represented by pixel groups 64-74 is also different. Up until image D (pixel group 70), the speed is relatively low, in this example lower than the threshold speed. Based on determining that the speed of the movement is lower than the threshold speed, it may be determined that this cellular body is attached to the functionalized wall surface, at least up until the time instance associated with image D. Even further, it can be seen that in image C (pixel group 68) this cellular body has travelled a distance 99 from its initial position 64. This distance may be determined and it may be determined that this distance 99 exceeds the threshold distance indicated by the circle around pixel group 64. Based on the latter determination, it may be determined that this cellular body is attached to the functionalized wall surface by means of a tether, such as a membrane tube, at least up until image D (pixel group 70).

[0137] After image D, the speed of the movement of cellular body represented by pixel groups 64-74, suddenly increases. This speed may be determined based on distance 96 and on the time difference between the time instances associated respectively with images D and E. As a side note, the time difference between two subsequent images is typically constant and related to a so-called frame rate of the imaging system. The speed of this further movement of the cellular body exceeds the threshold speed. Based on determining that this is indeed the case, it may be determined that the cellular body has detached from the functionalized wall surface.

[0138] Typically, the speed of a cellular body throughout a video is constantly monitored so that it can be determined easily when exactly the speed exceeded the threshold speed. This may provide valuable information, for example at which forces and/or at which distances from its initial position and/or at which state of the force generator the cellular body separated from the functionalized wall surface.

[0139] In an embodiment, the method may then comprise determining detachment images which are the earliest images in the sequence of images that comprise pixel groups representing a speed of movement of the first cellular body that is higher than said threshold speed. In the above example, the detachment images would then be image D (pixel group 70) and image E (pixel group 72), because these images are the first images in the video based on which the determined speed exceeds the threshold speed. As time of detachment a time between time instance associated with image D and time instance associated with image E may be selected. Alternatively, the time instance of image D or E may be regarded as time of detachment.

[0140] Cellular body represented by pixel group 84 in image A does not move in the sequence of images A-F. Hence, it may be determined that this cellular body is attached to the functionalized wall surface.

[0141] Cellular body represented by pixel group 88 in image A can first, based on images A, B, C be determined to be attached to the functionalized wall surface, in particular as a hinged cell. However, the speed determined based on images D and C, in this example, exceeds the threshold speed. Hence, it may be determined that this cellular body has detached from the functionalized wall surface.

[0142] A cellular body classified as hinge cell may move faster than a certain threshold th1 and less than a second threshold th2 (e.g. indicated as 98 in 6A). A cellular body that moves less than th1 may be classified as an attached cell (e.g. 84) and may at a later stage turn into a cell detaching directly (such as 76->78 and 86) or it may become a hinge or tether cell.

[0143] Cellular body represented by pixel group 86 immediately travels at a speed higher than the threshold speed to cluster area 84a. Hence, it may be determined as detached from the functionalized wall surface.

[0144] Cellular body represented by pixel group 120 in image A travels at relatively low speed, and further than the threshold distance. Hence, this cellular body may at first be classified as a tethered cellular body. However, the cellular body in image E has reached a cluster 84b. Based on determining that this is the case, this cellular body may be disregarded in the experiment, for example by refraining from classifying the cellular body as detached from or attached to the surface.

[0145] FIG. 6B depicts an image taken by a camera of an acoustic force spectroscopy system which is analyzed using an image processing algorithm. As shown in this video image, tracking paths are identified. The tracking paths are determined by tracking detected cellular bodies in multiple images. The paths show that cells are accumulated in positions of acoustic nodes that exist when the force generator of the AFS system is switched on.

[0146] FIG. 7 illustrates how tracking pixel groups representing a movement of a cellular body may be performed. In the depicted example, pixel groups 76, 78 and 80 are detected. First, pixel group 76 may be detected in image A based on aberrant pixel values with respect to background pixels. Then, a region of interest ROI 122 may be determined that comprises pixel group 76. In an embodiment, the region of interest may have pixel group 76 at its center. Region of interest also comprises pixels surrounding pixel group 76 in this example.

[0147] Then, the same region of interest 122 is analyzed in image B, i.e. an image subsequent to image A. It is then found that pixel group 76 no longer has aberrant pixel values, yet only pixels representing background. In an embodiment, the method comprises identifying pixels in region of interest 122 in image B that do have pixel values that distinguish them from pixels representing background. In this example, pixels at the bottom of region interest 122 in image B are identified.

[0148] Then, based on these identified pixels, pixel group 78 may be determined. This may comprise identifying all pixels that are adjacent the identified pixel at the bottom of region interest 122, which also have values distinguishing them from the background. Then, pixel group 78 in image B may be determined to represent the same cellular body as pixel group 76 in image A.

[0149] Then, as shown in image B at the bottom left of FIG. 7, the region of interest may be updated, e.g. shifted, in order to obtain an updated region of interest 122. Preferably the updated region of interest 122 has pixel group 78 at its center.

[0150] Subsequently, this updated region of interest 122 can be analyzed in a further subsequent image C and again pixels can be identified having aberrant values. These pixels may be determined to also represent the same cellular body that is tracked. Again, based on the identified pixels in the updated region of interest 122 in image C, a pixel group 80 in image C can be determined to represent the same cellular body.

[0151] These steps may be repeated, which results in the tracking of a cellular body throughout the video wherein the locations of the cellular body in subsequent images may form a trajectory of the cellular body moving in the flow cell due to the applied force.

[0152] It should be understood that sometimes the cellular body moves so fast that its movement cannot be tracked, which may result in a tracking error. This may also be dependent on the size of the region of interest that is chosen. It should be appreciated that determining the speed of the movement of a cellular body may be performed by determining that a cellular body cannot be tracked. This may namely be understood to be determining the speed in the sense that it is determined that the speed is higher than the maximum speed at which a cellular body can be tracked.

[0153] Another tracking mechanism for tracking cellular bodies that may be used may be based on a global minimization of distances between pixel groups representing cellular bodies in subsequent images. This may allow tracking of faster cellular bodies or tracking using smaller regions of interest.

[0154] Below, a pseudo-code is provided that illustrates a tracking method according to an embodiment that is based on a similarly score, e.g. the so-called structural similarity index measure (SSIM) score. The SSIM score is well is known in the art, see for example the article by Wang et al, 2004 Apr. 1). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing. 13]). The SSIM comparison yields a score of 0 for no structural similarity and 1 for identical images.

[0155] Initialize ROIs and Create Reference ROI Images for SSIM Algorithm:

[0156] For a First Image in the Image Sequence [0157] 1. Fixed tracker (ROI size=1212 pixels) [0158] 2. Select particles at locations [0159] 3. Initialize selection ROI images at selection locations [0160] 4. Fixed tracker ROI ref images=selection ROI images [0161] 5. Moving tracker (ROI size=3030 pixels) [0162] 6. Initialize moving tracker ROIs at selection locations [0163] 7. Moving tracker ROI ref images=ROI image composed of a constant image median value

[0164] For all Images in the Sequence of Images:

[0165] For all Cellular Bodies:

[0166] Run Fixed ROI Tracker: [0167] 8. Compute ROI features: SSIM score, mean intensity [0168] 9. Fixed tracker presence stateFixed tracker SSIM score>0.2

[0169] Run Moving ROI Tracker: [0170] 10. If Moving tracker presence==True: (True means present, False means detached/not present) [0171] 11. Compute center of mass (COM) and update ROI location [0172] 12. Compute Features: SSIM score, mean intensity, distance from selection location and traveling speed [0173] 13. Moving tracker presence state=(Moving tracker SSIM score<0.85) and (velocity<16 pix/s)

[0174] Classify Cellular Body Type (Normal Cell, Hinge Cell, Tether Cell, Cluster Error): [0175] 14. If (fixed tracker presence state==False for last 4*framerate frames) AND (moving tracker presence state==True): Hinge cell [0176] 15. If Hinge cell AND distance from selection location>30 pixels: tethered cell [0177] 16. If moving ROI tracker ROI mean>1.5 selection ROI image mean: Cluster error (cellular body in cluster) [0178] 17. Else: normal cell

[0179] Classify Cellular Body Presence Based on Cellular Body Type and Presence Results of the 2 Trackers: [0180] 18. If normal cell: presence=fixed ROI tracker presence [0181] 19. If cluster error: exclude cellular body [0182] 20. If other cellular body type: presence=moving tracker presence

[0183] After Last Frame:

[0184] Filtering of Cellular Bodies Based on Cellular Body Type: [0185] 21. Filtering of cells based on cellular body type

[0186] In step 1 of the code, the size of a region of interest (ROI) of a so-called fixed tracker ROI size parameter is set to a size of 1212 pixels. This parameter defines the size of the area that will be used to track a cell. In step 2, the initial locations of detected cellular bodies that are bound to the functionalized wall of the flow cells are determined using a known image recognition algorithm (such as template matching or a blob selection method). In step 3, ROIs in an initial image which represent cellular bodies, are initialized at the locations determined in step 2. These ROIs comprise the pixel groups representing respective cellular bodies. An initial image may depict the situation at the beginning of an experiment when no force is actively applied to the cellular bodies. In step 4, the identified pixel groups, i.e. the cellular bodies, in the initial image are stored as ROI reference images. Thus, each cellular body is associated with its own ROI, a fixed ROI location within the image, and a ROI reference image.

[0187] In step 5, the so-called moving tracker ROI size is set to a predetermined size, e.g. 3030 pixels. In step 6, the moving tracker ROIs are initialized at locations found in step 2. In step 7, the moving tracker ROI ref images are defined. This reference image that is used by the moving tracker may comprise pixels that have the same pixel value. In an embodiment, this pixel value may represent a median value of all pixel values in the initial images. In this manner, the moving tracker reference image is a homogeneous image having pixel values that are similar to the pixel values of the background of the image, e.g. similar to the functionalized wall surface. Thus, each cellular body is additionally associated with its own moving tracker ROI, moving tracker ROI location, and moving tracker ROI reference image.

[0188] Steps 8-20 are performed for all or at least a substantial part of the cellular bodies in the video frames. Steps 8 and 9 relate to the fixed tracker, which is configured to determine whether a cellular body is positioned at its initial position or not. In step 8, a similarity score, such as an SSIM score, may be determined based on the fixed tracker reference image for a cellular body in question and the fixed ROI of the image in question. If the similarity score is higher than a predetermined threshold, 0.2 in this example, then a Fixed tracker present stateTrue, meaning that the cellular body has not moved (significantly) from its initial position (see step 9).

[0189] Steps 10-13 relate to a so-called moving ROI tracker algorithm which is configured to track a cellular body as it moves relative to the functionalized wall surface. The algorithm may be configured to determine if a pixel group representing a cellular body is still present inside the moving ROI and compute the current location of the determined pixels group. Steps 11-13 are performed while Moving tracker present state is true. In step 11, the center of mass is determined of the pixel group representing the cellular body in question in the frame in question and the ROI position is updated. This may be performed as described with reference to FIG. 7. Then, in step 12, a similarity score, such as a SSIM score, may be determined on the basis of the ROI in the image in question and the reference image for the moving tracker, e.g. the homogeneous reference image similar to the background described above. If the similarity score is lower than a predetermined threshold, 0.85 in this example, then the algorithm can still track the cellular body. If this score becomes too high, it means that the updated ROI resembles the background too much meaning that the cellular body has disappeared and can no longer be tracked. Further, a speed is determined of the movement of the cellular body, e.g. based on the current image and one or more preceding images, e.g. based on the current image and the n number of preceding images. The determined speed is compared with a threshold speed, 16 pixels per second in this example. In step 13, if both the moving tracker can still track the cellular body and the speed is lower than the threshold speed, the Moving tracker present statetrue.

[0190] Steps 14-17 relate to the classification of the cellular body. This classification may be performed for each cellular body, in each frame of the video. In step 14, if both the fixed tracker presence state has been false for at least a number of frames, for example 4*framerate (i.e. for four seconds in reality), and if the moving tracker presence state is true, then the cell is preliminary classified as a hinge cell. In step 15, it is further checked whether the distance from the initial position for the cellular body in question exceeds a predetermined threshold, 30 pixels in this example. If this is the case and if the cellular body was preliminary classified as hinge cell, then, the cellular body is classified as a tethered cellular body. In step 16, it is checked whether the cellular body has reached a cluster. The algorithm may check this based on an intensity value. In this example, if the pixels values of the updated ROI have a higher mean intensity than 1.5 times the initial ROI, then a cluster error is determined. In step 17, if the cellular body has not been classified in any of steps 14, 15, 16, then it is classified as a normal cellular body.

[0191] In step 18, a presence of a normal cellular body is determined to be given by the fixed ROI tracker presence. Thus, if the value for the fixed ROI tracker is true, then the cellular body has not moved and may be counted as a cellular body that is still attached to the functionalized wall surface. In step 19, cellular bodies in clusters are excluded. These cellular bodies are not counted as attached nor as attached. In step 20, the presence of hinge cellular bodies and tethered cellular bodies is given by the moving tracker presence.

[0192] Finally, in step 21, which is performed after all frames have been analyzed, cellular bodies are filtered based on typenormal, hinge, tether. Since the type for a cellular body may be updated, and thus changes, in each new frame, it should be understood that the type based on which the cellular bodies are filtered after the experiment may be the type they were last assigned.

[0193] By filtering, a user can choose to dismiss for example tethered cellular bodies from an avidity analysis, for example in the sense that they are not counted as detached from the functionalized wall surface. The normal cellular bodies, if their presence is true, may be counted as still attached to the functionalized wall surface. The cluster error cellular bodies may preferably not be counted. The hinge cells may be counted as attached to the functionalized wall surface. The tether cellular bodies may be disregarded from an avidity analysis altogether, because, in spite of being still bound to the functionalized wall surface, it is doubtful whether these cellular bodies showed desired binding properties that a researcher is investigating.

[0194] It should be appreciated that the above described methods for tracking a cellular body may also be employed for tracking cellular bodies in the initialization images described herein. When a cellular body can no longer be tracked by such tracking algorithm it may be determined that the cellular body has settled onto the functionalized wall surface. In such case, the cellular body may no longer be trackable because, once settled, it may not be distinguishable anymore from the functionalized wall surface.

[0195] Thus, the above described algorithm and insights allow accurate classification of cellular bodies during force spectroscopy using an AFS system or another suitable force spectroscopy system. FIG. 8 depicts a method of determining interaction between cellular bodies and a functionalized wall surface according to an embodiment of the invention. The method may start with determining or receiving a sequence of images, e.g. a video, captured by a camera of a force spectroscopy system wherein the images represent cellular bodies contacting a functionalized wall of a flow cell, wherein the functionalized wall is configured to bind the cellular bodies. The video represents the dynamics of the cellular bodies, preferably before and during the application a force (which may be a force ramp) on cellular bodies that are bound to the functionalized wall (step 200). The force for may be an acoustic force or another force such as a centrifugal force having a direction away from the functionalized wall.

[0196] Before a force is applied to the cellular bodies, initial locations of cellular bodies that are bound to the functionalized may be determined in the sequence of images using well known image recognition techniques (step 202). Thereafter, the positions of the detected cellular bodies are tracked while an increasing force (a force ramp) may be applied to the bound cellular bodies (step 204). The force may cause some of the cellular bodies to be pulled away from the functionalized wall and start moving. The positions in subsequent video frames of a tracked cellular body may define a trajectory of the cellular body moving in the holding space. During the tracking of the position of the cellular bodies in the holding space, the travelling speed of the cellular bodies may be determined and monitored (step 206). The travelling speed of a cellular body at a certain point on the trajectory may be determined based on one or more images that comprise a representation of that cellular body around that point, e.g. two or more earlier video frames.

[0197] The tracked cellular bodies may then be classified based on the determined speed (step 208). If the speed at a certain point on the trajectory is larger than a predetermined threshold value, then a cellular body may be classified as a detached cellular body. If the speed at a certain point on the trajectory is smaller than the predetermined threshold value, then the cellular body may be classified as attached (despite the fact that the cellular body is moving). Further, the class of cellular bodies which have a speed larger than zero and which are classified as attached, may be further (sub)classified on the basis of other parameters such as a distance between the initial position and a further position, the shape of the trajectory, or any other suitable parameter or combination of parameters. For example, based on the distance (or another suitable parameter), such cellular body may be either classified as a hinged cellular body or a tethered cellular body.

[0198] FIGS. 9A and 9B depict examples of avidity curves that are determined using the systems and methods described in this application. These figures illustrate some experiments and the differences obtained using a standard analysis in which all moving cells are classified as detached cells and an analysis in which moving cells are tracked and classified based on the speed cells have when they move. In this example, hinge and tether cells are counted as detached on their final rupture moment (e.g. breaking of a tether). As most hinge and tether cells finally fully detach from the surface in this particular experiment, a similar baseline signal of about 12% is reached. The results show that hinge and tether cells can be identified as well as their detachment. This detachment is typically later in the force ramp, resulting in a slower decrease in the avidity curve compared to the standard analysis. The bar graph of FIG. 8B shows the root-mean-square error in the avidity curve compared to manual annotations for several experiments. The RMS avidity error is defined with respect to manual annotations that include hinges and tethers. The annotated detachment times are the times the cell fully detaches (for example breaking of a tether). The RMS error is defined as the square root of the mean (annotated_aviditytest_avidity).sup.2.

[0199] FIG. 10 schematically depicts a further system according to an embodiment. This system includes a rotary arm 1202 that is rotatable mounted onto a rotor system that includes a rotor 1214 and a rotary joint 1216. A holding space 4, e.g. a microfluidic cell, comprising cellular bodies (e.g. effector cells and target cells attached to a functionalized wall of the holding space) may be mounted onto the rotary arm. Further, a light source 1, an adjustable objective 14, a tube lens 15 and a camera 16 may form an imaging system for capturing images of effector cells interacting with target cells. A rotor controller 10 may be used to control the rotor system to spin the rotary arm in a circular motion so that a centrifugal force is exerted onto the cellular bodies. The system may be connected to a data processing system 100 that includes a processing module 102 that is configured to control the rotor controller and the imaging system. Further, when in use, the data processing system 100 may receive captured images 1224 of the holding space while a force ramp is exerted onto the cellular bodies. Further, the processing module is configured to process the captured images in a similar way as described above. Exemplary embodiments of such force spectroscopy systems are described in WO2017/147398 and U.S. Pat. No. 8,795,143 which are hereby incorporated by reference into this application.

[0200] FIG. 11 depicts a block diagram illustrating a data processing system according to an embodiment. As shown in FIG. 11, the data processing system 100 may include at least one processor 102, referred to above as processing module, coupled to memory elements 104 through a system bus 106. As such, the data processing system may store program code within memory elements 104. Further, the processor 102 may execute the program code accessed from the memory elements 104 via a system bus 106. In one aspect, the data processing system may be implemented as a computer that is suitable for storing and/or executing program code. It should be appreciated, however, that the data processing system 100 may be implemented in the form of any system including a processor and a memory that is capable of performing the functions described within this specification.

[0201] The memory elements 104 may include one or more physical memory devices such as, for example, local memory 108 and one or more bulk storage devices 110. The local memory may refer to random access memory or other non-persistent memory device(s) generally used during actual execution of the program code. A bulk storage device may be implemented as a hard drive or other persistent data storage device. The processing system 100 may also include one or more cache memories (not shown) that provide temporary storage of at least some program code in order to reduce the number of times program code must be retrieved from the bulk storage device 110 during execution.

[0202] Input/output (I/O) devices depicted as an input device 112 and an output device 114 optionally can be coupled to the data processing system. Examples of input devices may include, but are not limited to, a keyboard, a pointing device such as a mouse, a touch-sensitive display, or the like. Examples of output devices may include, but are not limited to, a monitor or a display, speakers, or the like. Input and/or output devices may be coupled to the data processing system either directly or through intervening I/O controllers.

[0203] In an embodiment, the input and the output devices may be implemented as a combined input/output device (illustrated in FIG. 9 with a dashed line surrounding the input device 112 and the output device 114). An example of such a combined device is a touch sensitive display, also sometimes referred to as a touch screen display or simply touch screen. In such an embodiment, input to the device may be provided by a movement of a physical object, such as e.g. a stylus or a finger of a user, on or near the touch screen display.

[0204] A network adapter 116 may also be coupled to the data processing system to enable it to become coupled to other systems, computer systems, remote network devices, and/or remote storage devices through intervening private or public networks. The network adapter may comprise a data receiver for receiving data that is transmitted by said systems, devices and/or networks to the data processing system 100, and a data transmitter for transmitting data from the data processing system 100 to said systems, devices and/or networks. Modems, cable modems, and Ethernet cards are examples of different types of network adapter that may be used with the data processing system 100.

[0205] As pictured in FIG. 9, the memory elements 104 may store an application 118. In various embodiments, the application 118 may be stored in the local memory 108, the one or more bulk storage devices 110, or apart from the local memory and the bulk storage devices. It should be appreciated that the data processing system 100 may further execute an operating system (not shown in FIG. 9) that can facilitate execution of the application 118. The application 118, being implemented in the form of executable program code, can be executed by the data processing system 100, e.g., by the processor 102. Responsive to executing the application, the data processing system 100 may be configured to perform one or more operations or method steps described herein.

[0206] In one aspect of the present invention, the data processing system 100 may represent a controller 10 as described herein or a data processing system configured to perform any of the methods described herein.

[0207] Various embodiments of the invention may be implemented as a program product for use with a computer system, where the program(s) of the program product define functions of the embodiments (including the methods described herein). In one embodiment, the program(s) can be contained on a variety of non-transitory computer-readable storage media, where, as used herein, the expression non-transitory computer readable storage media comprises all computer-readable media, with the sole exception being a transitory, propagating signal. In another embodiment, the program(s) can be contained on a variety of transitory computer-readable storage media. Illustrative computer-readable storage media include, but are not limited to: (i) non-writable storage media (e.g., read-only memory devices within a computer such as CD-ROM disks readable by a CD-ROM drive, ROM chips or any type of solid-state non-volatile semiconductor memory) on which information is permanently stored; and (ii) writable storage media (e.g., flash memory, floppy disks within a diskette drive or hard-disk drive or any type of solid-state random-access semiconductor memory) on which alterable information is stored. The computer program may be run on the processor 102 described herein.

[0208] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms a, an, and the are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms comprises and/or comprising, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

[0209] The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of embodiments of the present invention has been presented for purposes of illustration, but is not intended to be exhaustive or limited to the implementations in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments were chosen and described in order to best explain the principles and some practical applications of the present invention, and to enable others of ordinary skill in the art to understand the present invention for various embodiments with various modifications as are suited to the particular use contemplated.