Patent Publication Number: US-10331626-B2

Title: Minimization of surprisal data through application of hierarchy filter pattern

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of copending application Ser. No. 13/491,884 filed Jun. 8, 2012, entitled “MINIMIZATION OF SURPRISAL DATA THROUGH APPLICATION OF HIERARCHY FILTER PATTERN, which is a continuation-in-part patent application of copending application Ser. No. 13/475,183, filed May 18, 2012, entitled “MINIMIZATION OF SURPRISAL DATA THROUGH APPLICATION OF HIERARCHY OF REFERENCE GENOMES”. The aforementioned applications are hereby incorporated herein by reference. 
    
    
     BACKGROUND 
     The present invention relates to minimizing surprisal data generated when compared to a reference genome and more specifically to minimizing surprisal data through application of a hierarchy filter pattern. 
     DNA gene sequencing of a human, for example, generates about 3 billion (3×10 9 ) nucleotide bases. Currently all 3 billion nucleotide base pairs are transmitted, stored and analyzed, with each base pair typically represented as two bits. The storage of the data associated with the sequencing is significantly large, requiring at least 3 gigabytes of computer data storage space to store the entire genome, which includes only nucleotide sequenced data and no other data or information, such as annotations. If the entire genome includes other information, such as annotations, the genome may require terabytes worth of storage. The movement of the data between institutions, laboratories and research facilities is hindered by the significantly large amount of data, the significant amount of storage necessary to contain the data, and the resources necessary to directly transmit the data. For example, some research facilities can spend upwards of $2 million dollars for transmitting genetic data and sending genetic data that is large, for example terabytes of data, that includes annotations and specifics regarding the genetic sequence or genome. The transfer of a genetic sequence that is very large can take a significant amount of time over a network data processing system. 
     SUMMARY 
     According to one embodiment of the present invention, a computer program product for minimizing surprisal data representing an entire genome of an organism for compression and transmission, comprising a source computer having one or more processors and one or more computer-readable memories coupled to the one or more processors. The computer program product comprising: one or more computer-readable storage devices, and program instructions, stored on the one or more storage devices, the program instructions comprising: program instructions to, at a source computer, read and identify characteristics of the organism&#39;s medical history and background associated with a genetic sequence of an organism; program instructions to receive an input of rank of at least two identified characteristics associated with the genetic sequence of the organism; program instructions to generate a hierarchy of ranked, identified characteristics based on the rank of the at least two identified characteristics of the genetic sequence of the organism; program instructions to compare the hierarchy of ranked, identified characteristics to a repository of reference genomes; and program instructions to transmit to a destination, a compressed, minimized genome representing an entire genome by sending the surprisal data, an indication of the at least one matched reference genome, and how the reference genome were broken into pieces and not sending sequences of nucleotides that are the same in the genetic sequence of the organism and the at least one matched reference genome. If at least one reference genome from the repository matches the hierarchy of ranked, identified characteristics, program instructions to: store the at least one matched reference genome in a repository; break the at least one matched reference genome into pieces comprising nucleotides of the genetic sequence which comprises at least one gene, at least some of the pieces being associated with the identified characteristics; store the pieces which are associated with the identified characteristics in the repository; combine the stored pieces of the at least one matched reference genome into a filter pattern; compare pieces of the nucleotides of the genetic sequence of the organism which comprises at least one gene which correspond to the stored pieces of the at least one matched reference genome to the nucleotides of the filter pattern of the pieces of the at least one matched reference genome, to find differences where nucleotides of the genetic sequence of the organism which are different from the nucleotides of the at least one matched reference genome; use the differences to create surprisal data representing an entire genome of the organism and storing the surprisal data in the repository, the surprisal data comprising a starting location of the differences within the reference genome, how the reference genomes were broken into pieces, a count of a number of differences at the location within the at least one matched reference genome and the nucleotides from the genetic sequence of the organism which are different from the nucleotides of the reference genome. 
     According to another embodiment of the present invention, a computer system for minimizing surprisal data representing an entire genome of an organism for compression and transmission. The computer system comprising: a source computer having one or more processors, one or more computer-readable memories coupled to the one or more processors and one or more computer-readable storage devices coupled to the one or more processors, and program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories. The program instructions comprising: program instructions to compare nucleotides of the genetic sequence of the organism to nucleotides from a reference genome, to, at a source, read and identify characteristics of the organism&#39;s medical history and background associated with a genetic sequence of an organism; program instructions to receive an input of rank of at least two identified characteristics associated with the genetic sequence of the organism; program instructions to generate a hierarchy of ranked, identified characteristics based on the rank of the at least two identified characteristics associated with the genetic sequence of the organism; program instructions to compare the hierarchy of ranked, identified characteristics to a repository of reference genome; and program instructions for transmitting to a destination, a compressed, minimized genome representing an entire genome by sending the surprisal data and the indication of the at least one matched reference genome, and not sending sequences of nucleotides that are the same in the genetic sequence of the organism and the at least one matched reference genome. If at least one reference genome from the repository matches the hierarchy of ranked, identified characteristics, program instructions to: store the at least one matched reference genome in a repository; break the at least one matched reference genome into pieces comprising nucleotides of the genetic sequence which comprises at least one gene, at least some of the pieces being associated with the identified characteristics; store the pieces which are associated with the identified characteristics in the repository; combine the stored pieces of the at least one matched reference genome into a filter pattern; compare pieces of the nucleotides of the genetic sequence of the organism which comprises at least one gene which correspond to the stored pieces of the at least one matched reference genome to the nucleotides of the filter pattern of the pieces of the at least one matched reference genome, to find differences where nucleotides of the genetic sequence of the organism which are different from the nucleotides of the at least one matched reference genome; use the differences to create surprisal data and store the surprisal data in the repository, the surprisal data comprising a starting location of the differences within the reference genome, a count of a number of differences at the location within the at least one matched reference genome, how the reference genomes were broken into pieces and the nucleotides from the genetic sequence of the organism which are different from the nucleotides of the reference genome. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         FIG. 1  depicts an exemplary diagram of a possible data processing environment in which illustrative embodiments may be implemented. 
         FIG. 2-3  shows a flowchart of a method of minimizing the surprisal data by comparing a sequence to a hierarchy filter pattern. 
         FIG. 4  shows a schematic of the re-creation of an organism genome sequence using a reference genome and surprisal data. 
         FIG. 5  shows a schematic overview of a method of data surprisal data reduction of genetic data for transmission, storage, and analysis according to an illustrative embodiment. 
         FIG. 6  illustrates internal and external components of a client computer and a server computer in which illustrative embodiments may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     The illustrative embodiments of the present invention recognize that the difference between the genetic sequence from two humans is about 0.1%, which is one nucleotide difference per 1000 base pairs or approximately 3 million nucleotide differences. The difference may be a single nucleotide polymorphism (SNP) (a DNA sequence variation occurring when a single nucleotide in the genome differs between members of a biological species), or the difference might involve a sequence of several nucleotides. The illustrative embodiments recognize that most SNPs are neutral but some, 3-5%, are functional and influence phenotypic differences between species through alleles. Furthermore, approximately 10 to 30 million SNPs exist in the human population, of which at least 1% are functional. The illustrative embodiments also recognize that with the small amount of differences present between the genetic sequence from two humans, the “common” or “normally expected” sequences of nucleotides can be compressed out or removed to arrive at “surprisal data”-differences of nucleotides which are “unlikely” or “surprising” relative to the common sequences. The dimensionality of the data reduction that occurs by removing the “common” sequences is 10 3 , such that the number of data items and, more importantly, the interaction between nucleotides, is also reduced by a factor of approximately 10 3 —that is, to a total number of nucleotides remaining on the order of 10 3 . The illustrative embodiments also recognize that by identifying what sequences are “common” or provide a “normally expected” value within a genome, and knowing what data is “surprising” or provides an “unexpected value” relative to the normally expected value, the only data needed to re-create the entire genome in a lossless manner is the surprisal data and the reference genome used to obtain the surprisal data. The illustrative embodiment of the present invention also recognizes that specific characteristics of diseases or underlying causes of diseases can and have been attributed to specific genes or nucleotides that are associated with specific reference genomes. 
       FIG. 1  is an exemplary diagram of a possible data processing environment provided in which illustrative embodiments may be implemented. It should be appreciated that  FIG. 1  is only exemplary and is not intended to assert or imply any limitation with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made. 
     Referring to  FIG. 1 , network data processing system  51  is a network of computers in which illustrative embodiments may be implemented. Network data processing system  51  contains network  50 , which is the medium used to provide communication links between various devices and computers connected together within network data processing system  51 . Network  50  may include connections, such as wire, wireless communication links, or fiber optic cables. 
     In the depicted example, a client computer  52 , server computer  54 , and a repository  53  connect to network  50 . In other exemplary embodiments, network data processing system  51  may include additional client computers, storage devices, server computers, and other devices not shown. The client computer  52  includes a set of internal components  800   a  and a set of external components  900   a , further illustrated in  FIG. 6 . The client computer  52  may be, for example, a mobile device, a cell phone, a personal digital assistant, a netbook, a laptop computer, a tablet computer, a desktop computer, a sequencing machine or any other type of computing device. 
     Client computer  52  may contain an interface  104 . The interface can be, for example, a command line interface, a graphical user interface (GUI), or a web user interface (WUI). The interface may be used, for example for viewing an uncompressed sequence from a repository or an entire genome from a repository. The interface may also accept an input regarding a rank of at least two identified characteristics, to display a hierarchy of the inputted identified characteristics that is created, and/or to display matched reference genomes. 
     In the depicted example, server computer  54  provides information, such as boot files, operating system images, and applications to client computer  52 . Server computer  54  can compute the information locally or extract the information from other computers on network  50 . Server computer  54  includes a set of internal components  800   b  and a set of external components  900   b  illustrated in  FIG. 6 . 
     Program code, reference genomes, and programs such as a sequence to reference genome compare program  67 , a genome creator program  66 , and/or a characteristic hierarchy program  68  may be stored on at least one of one or more computer-readable tangible storage devices  830  shown in  FIG. 6 , on at least one of one or more portable computer-readable tangible storage devices  936  as shown in  FIG. 6 , or repository  53  connected to network  50 , or downloaded to a data processing system or other device for use. For example, program code, reference genomes, and programs such as a sequence to reference genome compare program  67 , characteristic hierarchy program  68  and/or a genome creator program  66  may be stored on at least one of one or more tangible storage devices  830  on server computer  54  and downloaded to client computer  52  over network  50  for use on client computer  52 . Alternatively, server computer  54  can be a web server, and the program code, reference genomes, and programs such as a sequence to reference genome compare program  67 , characteristic hierarchy program  68  and/or a genome creator program  66  may be stored on at least one of the one or more tangible storage devices  830  on server computer  54  and accessed on client computer  52 . Sequence to reference genome compare program  67 , characteristic hierarchy program  68  and/or genome creator program  66  can be accessed on client computer  52  through interface  104 . In other exemplary embodiments, the program code, reference genomes, and programs such as sequence to reference genome compare program  67 , characteristic hierarchy program  68  and genome creator program  66  may be stored on at least one of one or more computer-readable tangible storage devices  830  on client computer  52  or distributed between two or more servers. 
       FIGS. 2-3  show a flowchart of a method of minimizing the surprisal data by comparing a sequence to a hierarchy filter pattern. 
     In a first step, characteristics of at least one genetic sequence of an organism are read and identified from a repository at a source (step  202 ), for example in repository  53  by the characteristic hierarchy program  68  as shown in  FIG. 1 . The characteristics may be, but are not limited to, facts regarding a medical history of the organism, demographics of the organism, diagnosed illnesses, and other such characteristics or identifying indicators. The uncompressed genetic sequence of an organism may be a DNA sequence, an RNA sequence, or a nucleotide sequence and may represent a sequence or a genome of an organism. The organism may be a fungus, microorganism, human, animal or plant. 
     An input of rank of at least two identified characteristics of a sequence of an organism is received from a user (step  204 ), for example through interface  104 . The rank provides the relative value, weight or importance of identified specific characteristics. From the inputted rank of at least two identified characteristics, a hierarchy of identified characteristics is generated (step  206 ), for example by the characteristic hierarchy program  68 . 
     The hierarchy of identified characteristics generated may be defined as a matter of order, with the order being between the identified characteristics, which are classified in different nested categories, or an ordered series of identified characteristics in which each terms is superior relative to a specific set of identified characteristics. The hierarchy can be: a simple linear hierarchy, a branching network of subcategories, and/or a nested hierarchy of categories. 
     For example, a hierarchy with a branching network of subcategories may have a primary category of diabetes mellitus and secondary categories of Type 1 [juvenile type] and Type 2 [adult onset]. It should be noted that the two types under the second category are mutually exclusive. 
     An example of a nested hierarchy of categories may have diabetes mellitus Type 1 [juvenile type] as a primary category and secondary categories of: diabetes with renal manifestations, diabetes with ophthalmic manifestations, diabetes with neurological manifestations, and diabetes with peripheral circulatory disorders. Note that a patient could have from zero to all of the secondary categories. The secondary categories could have additional inclusive or mutually exclusive categories. For example diabetes with neurological manifestations could have Tertiary Categories of: amyotrophy, gastroparalysis, gastroparesis, mononeuropathy, neurogenic arthropathy, peripheral autonomic neuropathy, and/or polyneuropathy. 
     The hierarchy of identified characteristics is then compared to a repository of reference genomes (step  208 ). A reference genome is a digital nucleic acid sequence database which includes numerous sequences. The sequences of the reference genome do not represent any one specific individual&#39;s genome, but serve as a starting point for broad comparisons across a specific species, since the basic set of genes and genomic regulator regions that control the development and maintenance of the biological structure and processes are all essentially the same within a species. In other words, the reference genome is a representative example of a species&#39; set of genes. As discussed above, specific characteristics of diseases or underlying causes of diseases can and have been attributed to specific genes or nucleotides that are associated with specific reference genomes. 
     If a match (step  210 ) is not present between at least one reference genome in the repository and the hierarchy generated of the identified characteristics, then, the method returns to step  204  of receiving an input of the rank of at least two identified characteristics of a sequence of an organism. 
     The user may set what is considered a match to the hierarchy through the interface, for example interface  104 . For example, the user may set that a match between a reference genome and the hierarchy is only present if a match is found with the hierarch or the hierarch and a neighbor, and so on. Alternatively, a match may be based on a probability threshold. 
     If a match (step  210 ) is present between at least one reference genome in the repository and the hierarchy generated of the identified characteristics, the at least one matched reference genome is stored in a repository (step  212 ). The repository may be repository  53  or a separate repository. 
     The at least one matched reference genome stored in the repository is broken into pieces, some of the pieces being associated with causing or taking part in the identified characteristic. The pieces which are so associated are stored in the repository, and the remainder of the reference genome is discarded (step  214 ). The pieces may be genes, a series of genes, or pathways. 
     The stored pieces are combined together to form a filter pattern associated with the identified characteristics of the generated hierarchy (step  216 ). 
     The filter pattern of pieces of the matched reference genomes is then compared to the corresponding pieces of a sequence of an organism to obtain surprisal data, and the surprisal data and an indication of the matched reference genome used to prepare the filter pattern is stored in a repository (step  218 ), for example using a sequence to reference genome compare program  67 . 
     The surprisal data preferably includes how the reference genome was broken into pieces; a location of the difference within the reference genome pieces, the number of nucleic acid bases that are different, and the actual changed nucleic acid bases. Including the number of bases which are different within the surprisal data that is compressed provides a double check of the method by comparing the actual bases to the reference genome bases to confirm that the bases really are different. 
       FIG. 4  shows a schematic of the comparison of an organism sequence to a piece of a reference genome sequence to obtain surprisal data representing an organism&#39;s genome. For example, the surprisal data that resulted from comparing the organism sequence to the reference genome shown in  FIG. 4  would be surprisal data consisting of: a difference at location  485  of the reference genome; four nucleic acid base differences relative to the reference genome, and the actual bases present in the sequence at the location, for example CAAT (instead of GTTA). 
     If a large amount of surprisal data is present from comparing to a sequence of an organism to obtain surprisal data and the surprisal data and an indication of the matched reference genome used to prepare the filter pattern is stored in a repository in step  218 , and the method then returns to step  204  of receiving an input of rank of at least two identified characteristics of a sequence of an organism is received from a user. This step is carried out to check to see if the surprisal data is valid. Since the filter pattern is attempting to provide a fine tuned amount of surprisal data and if from comparing the filter pattern to the sequence of a organism, a large amount of surprisal data, then the filter pattern is not specific enough and additional or altered input regarding the hierarchy is necessary. 
     If a large amount of surprisal data is not present from comparing a sequence of an organism to obtain surprisal data and the surprisal data and an indication of the matched reference genome used to prepare the filter pattern is stored in a repository in step  218 , and an indication of how and what reference genomes were broken into pieces and the surprisal data is sent to a destination (step  222 ). The surprisal data preferably includes a location of the difference within the reference genome pieces, the number of nucleic acid bases that are different, and the actual changed nucleic acid bases. Including the number of bases which are different within the surprisal data that is compressed, provides a double check of the method by comparing the actual bases to the reference genome bases to confirm that the bases really are different. 
     For example, a user may wish to determine if at least one sequence of an organism yields surprisal data when compared to reference genomes that are associated with type 2 diabetes mellitus, coronary artery disease, but not chronic obstructive pulmonary disease (COPD). A user may therefore assign a rank or weight of 0.6 to the identified characteristic of type 2 diabetes mellitus, a rank or weight of 0.3 to the identified characteristic of coronary artery disease and a rank or weight of 0.1 to COPD. 
     A reference genome that is associated with type 2 diabetes mellitus and not COPD may be considered a match and would provide a narrowed, filtered amount of surprisal data. Another match could be a reference genome that is associated with coronary disease and not COPD. 
     The matched reference genomes would then be broken into pieces, for example genes or pathways of genes associated with the insulin production. Another match may be broken into pieces associated with high blood pressure. 
     A filter pattern is created by combining these pieces of the reference genomes that match. In this example, comparing the sequence of at least one organism to a specific filter pattern from matched reference genomes, maximizes the “common” or “normally expected” sequences of nucleotides that can be compressed out and minimizes the surprisal data, such that the surprisal data that does result from the comparison to both matched reference genomes is increased in relevancy based on the user&#39;s input. 
     The indication of how and what reference genomes were broken into pieces and the surprisal data is received by the destination and stored in a repository (step  224 ). The indicated reference genomes are then retrieved from a repository and broken into piece as indicated in step  224 , for example using a genome creator program  66  and stored in a repository (step  226 ). 
     The pieces of the reference genome are then reassembled into the filter pattern that matches the filter pattern used in step  216 . 
     From the surprisal data, the retrieved reference genomes, and the recreated filter pattern, an entire genome of the organism is re-created by finding a location within at least one reference genome that was indicated as having a difference in the surprisal data and alters the bases of the reference genome to be the bases indicated by the surprisal data (step  230 ), for example by the genome creator program  66 . In the example of  FIG. 5 , based on the surprisal data, a difference is present at location  485 , this location is found in the reference genome and GTTA is changed to be CAAT as indicated by the surprisal data. 
     The surprisal data may be verified by comparing the nucleotides from the genetic sequence of the organism in the surprisal data to the nucleotides in the reference genome at the location. If all of the nucleotides in the surprisal data are different from the nucleotides in the reference genome, the surprisal data is verified. This verification may take place prior to step  222 . 
     Alternatively, the verification may take place simultaneously with step  230  during the creation of the entire genome of an organism by a genome creator program  66 . If some of the nucleotides in the surprisal data are the same as the nucleotides in the reference genome, the surprisal data has an error. 
     It should be noted that in  FIGS. 4 and 5 , only a portion of both the organism sequence and the reference genome are shown for clarity, and the sequences shown are chosen randomly and do not represent a real DNA sequence of any sort. 
       FIG. 6  illustrates internal and external components of client computer  52  and server computer  54  in which illustrative embodiments may be implemented. In  FIG. 6 , client computer  52  and server computer  54  include respective sets of internal components  800   a ,  800   b , and external components  900   a ,  900   b . Each of the sets of internal components  800   a ,  800   b  includes one or more processors  820 , one or more computer-readable RAMs  822  and one or more computer-readable ROMs  824  on one or more buses  826 , and one or more operating systems  828  and one or more computer-readable tangible storage devices  830 . The term “tangible storage device” does not encompass a signal propagation media such as a copper cable, optical fiber or wireless transmission media. The one or more operating systems  828 , sequence to reference genome compare program  67 , characteristic hierarchy program  68  and genome creator program  66  are stored on one or more of the computer-readable tangible storage devices  830  for execution by one or more of the processors  820  via one or more of the RAMs  822  (which typically include cache memory). In the embodiment illustrated in  FIG. 6 , each of the computer-readable tangible storage devices  830  is a magnetic disk storage device of an internal hard drive. Alternatively, each of the computer-readable tangible storage devices  830  is a semiconductor storage device such as ROM  824 , EPROM, flash memory or any other computer-readable tangible storage device that can store a computer program and digital information. 
     Each set of internal components  800   a ,  800   b  also includes a R/W drive or interface  832  to read from and write to one or more portable computer-readable tangible storage devices  936  such as a CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk or semiconductor storage device. Sequence to reference genome compare program  67 , characteristic hierarchy program  68  and genome creator program  66  can be stored on one or more of the portable computer-readable tangible storage devices  936 , read via R/W drive or interface  832  and loaded into hard drive  830 . 
     Each set of internal components  800   a ,  800   b  also includes a network adapter or interface  836  such as a TCP/IP adapter card. Sequence to reference genome compare program  67 , characteristic hierarchy program  68  or genome creator program  66  can be downloaded to client computer  52  and server computer  54  from an external computer via a network (for example, the Internet, a local area network or other, wide area network) and network adapter or interface  836 . From the network adapter or interface  836 , sequence to reference genome compare program  67 , characteristic hierarchy program  68  and genome creator program  66  are loaded into hard drive  830 . The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. 
     Each of the sets of external components  900   a ,  900   b  includes a computer display monitor  920 , a keyboard  930 , and a computer mouse  934 . Each of the sets of internal components  800   a ,  800   b  also includes device drivers  840  to interface to computer display monitor  920 , keyboard  930  and computer mouse  934 . The device drivers  840 , R/W drive or interface  832  and network adapter or interface  836  comprise hardware and software (stored in storage device  830  and/or ROM  824 ). 
     Sequence to reference genome compare program  67 , characteristic hierarchy program  68  and genome creator program  66  can be written in various programming languages including low-level, high-level, object-oriented or non object-oriented languages. Alternatively, the functions of a sequence to reference genome compare program  67 , characteristic hierarchy program  68  and genome creator program  66  can be implemented in whole or in part by computer circuits and other hardware (not shown). 
     Based on the foregoing, a computer system, method and program product have been disclosed for minimizing surprisal data. However, numerous modifications and substitutions can be made without deviating from the scope of the present invention. Therefore, the present invention has been disclosed by way of example and not limitation. 
     As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. 
     Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. 
     A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. 
     Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. 
     Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
     Aspects of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.