Geometric distance matched spatial transcriptome multi-slice alignment and domain identification system

Abstract

This invention provides a spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching, belonging to the fields of artificial intelligence and bioinformatics. Technical solution: Based on a geometric distance matching algorithm and constructed using an MVC architecture, it includes two core functional modules: multi-slice data alignment and spatial domain identification, as well as three supporting modules: front-end interaction, back-end service, and data processing. By identifying common cell types across multiple slices, Euclidean distance is calculated after standardizing cell spatial coordinates to achieve nearest neighbor matching. A similarity threshold is then used to filter high-matching cell pairs to complete multi-slice alignment. Beneficial effects: The system is compatible with multi-platform data and mainstream formats, provides 2D / 3D visualization interaction functions and result export services, and solves the problems of complex models, high resource consumption, and high usage thresholds in existing technologies. It achieves efficient and accurate multi-slice data alignment and spatial domain identification, and is suitable for multiple research scenarios such as developmental biology and oncology.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence and bioinformatics technology, and relates to a spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching. Background Technology

[0002] Spatial transcriptomics (ST) is a revolutionary breakthrough in the life sciences. It can accurately measure gene expression in the spatial context of tissues and combine gene expression information with tissue spatial structure. This provides a new perspective for studying cellular heterogeneity, tissue structure and its functional relationship, and has greatly promoted research progress in multiple fields such as developmental biology, oncology, and neuroscience.

[0003] In practical research, researchers typically perform spatial transcriptome sequencing on multiple slices from the same tissue or from different time points and under different conditions to obtain a comprehensive spatial gene expression map. However, differences in tissue cutting, inconsistent spatial coordinate systems, and technical noise between different slices make accurate alignment of multi-slice data a key challenge in data integration and analysis. Effective multi-slice spatial alignment not only helps construct three-dimensional spatial expression maps but also helps identify spatial domains and their dynamic changes, revealing deeper insights into the spatial regulatory mechanisms of cells.

[0004] Meanwhile, spatial domain identification, as a key step in ST data analysis, requires precise division of organizational functional regions. However, existing technologies generally have limitations: on the one hand, mainstream multi-slice alignment methods rely on deep learning, graph neural networks, or complex statistical models. These models are not only complex in structure and consume huge amounts of computational resources, making it difficult for ordinary laboratories to configure compatible hardware, but also require cumbersome pre-training and parameter tuning processes, demanding extremely high user expertise. Furthermore, they impose strict restrictions on input data formats and sample annotation information, resulting in poor compatibility, a lack of user-friendly interfaces and intuitive visualization tools, and weak interpretability of results. On the other hand, existing spatial domain identification technologies are inefficient and have poor visualization effects when processing large-scale data, requiring high user operation skills and failing to meet the needs of researchers for rapid and accurate analysis. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching. With geometric distance matching algorithm as the core and combined with MVC (Model-View-Controller) architecture, it achieves efficient alignment and accurate spatial domain identification of multi-slice ST data, while reducing the technical threshold and improving the ease of use and interpretability of data analysis, providing an integrated data processing solution for researchers.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching includes: The data input module is used to receive multiple spatial transcriptome slice data files uploaded by the user; A common cell type identification module is used to automatically identify and filter out common cell types from the multiple slice data; The geometric distance matching module is used to extract the spatial coordinates of the common cell type, standardize the coordinates, calculate the Euclidean distance between cells of the same type in slices, and generate initial matching pairs through the nearest neighbor matching algorithm. The similarity filtering module is used to filter the initial matching pairs according to the similarity threshold set by the user to obtain the final alignment result; The spatial domain identification module is used to divide tissue spatial functional regions based on cell spatial coordinates and gene expression characteristics using clustering algorithms; The results output and visualization module is used to output alignment and spatial domain recognition results and provide 2D / 3D visualization views.

[0007] Furthermore, in the geometric distance matching module, the spatial coordinates are standardized using the Min-Max standardization method, and the standardization formula is: (1) in, For standardized coordinates, Original coordinates , These are the minimum and maximum coordinates for this cell type, respectively.

[0008] Furthermore, in the geometric distance matching module, the formula for calculating the Euclidean distance is: (2) in, , These are the coordinates of two cells in a common cell type between two slices.

[0009] Furthermore, in the similarity filtering module, the formula for calculating the similarity of matching pairs is: (3) in, dist To match the Euclidean distance between two cells in a pair. , These are the minimum and maximum distances for all matching pairs, respectively.

[0010] Furthermore, the system adopts an MVC architecture, including: The front-end interaction module, built on the Streamlit framework, is used to provide a user interface. The backend service module is used to execute core business logic processing; The data processing module is used to parse data files, manage data structures, and generate visualized data.

[0011] Furthermore, the front-end interaction module supports drag-and-drop / browse upload of data files, target cell type filtering, similarity threshold adjustment, control of cell type visibility in 2D view, and adjustment of rotation, scaling, line length, azimuth, and elevation parameters in 3D view.

[0012] Furthermore, the data processing module uses the Scanpy library to parse .h5ad format files and exports the results in .csv or .h5ad format.

[0013] Furthermore, the spatial domain identification module automatically extracts spatial coordinates and gene expression features based on the tissue slice images and corresponding .h5ad files uploaded by the user, and performs density clustering based on Ground Truth annotations to identify spatial functional domains.

[0014] The present invention includes an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the functions of various modules of the system described above.

[0015] The present invention also includes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the functions of the various modules of the system described in any of the preceding claims.

[0016] The beneficial effects of this invention are: Compared with existing technologies, the spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching described in this invention exhibits multiple advantages in the field of spatial transcriptome multi-slice data processing, significantly outperforming existing mainstream methods: (1) Better alignment performance: Based on the core algorithm of geometric distance matching, the multi-slice alignment accuracy is ≥92%, and it can achieve accurate matching in complex scenarios such as dynamic developmental tissues, layered tissues, and diseased tissues, effectively overcoming slice differences and technical noise; (2) More comprehensive functions: It integrates multi-slice alignment, spatial domain recognition, result visualization and export functions, and has cross-platform data compatibility, flexible parameter adjustment and result reuse capabilities, without relying on multiple tool combinations; (3) Wider range of applications: It is compatible with data from multiple platforms such as 10x Visium, Stereo-seq, and Xenium, as well as the mainstream .h5ad format. It can be adapted to various research scenarios such as embryonic development tracking, cerebral cortex analysis, and tumor microenvironment analysis. It shows stable adaptability in both routine tissue and disease tissue data processing. (4) Higher practical value: The algorithm is simple and efficient, with low hardware resource requirements, and can be deployed in ordinary laboratories; the graphical interactive interface lowers the operation threshold and does not require professional programming skills; the analysis efficiency far exceeds that of traditional methods, greatly shortens the research cycle, and supports the connection of results with mainstream bioinformatics tools, providing convenience for subsequent research. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 The system design overall architecture diagram provided by the embodiment of the present invention is shown, which clearly shows the front-end interaction module, back-end service module, data processing module of the MVC architecture and the data flow between the three; Figure 2 The system main interface provided in this embodiment of the invention is shown, presenting the selection entry points and corresponding function descriptions for the two major functional modules, "Data Alignment" and "Spatial Domain Recognition". Figure 3 The data upload interface of the data alignment module provided in this embodiment of the invention is shown, which supports uploading two .h5ad format files by dragging and dropping or browsing. Figure 4 The 2D cell distribution visualization interface provided in the embodiment of the present invention is shown, displaying the spatial distribution and quantity statistics of each cell type in two slices in a bar chart; Figure 5 The 2D view interactive interface provided in the embodiment of the present invention is shown, focusing on the "Brain" core cell type and showing its distribution comparison in two slices; Figure 6 The alignment settings interface provided in this embodiment of the invention is shown, presenting a selectable list of cell types and a similarity threshold adjustment slider ranging from 0.00 to 1.00; Figure 7 The 3D matching interface provided in this embodiment of the invention is shown, which displays the three-dimensional visualization effect of the cell matching results, including view control parameters such as line length, azimuth angle, and elevation angle; Figure 8 The 3D alignment interactive interface provided in the embodiment of the present invention is shown, which separately displays the cross-slice matching relationship of the target cell type "Brain"; Figure 9 The scoring interface provided in this embodiment of the invention is shown, which displays the similarity score distribution of all matching cell pairs in the form of a histogram. Figure 10 The data upload interface for the spatial domain recognition function provided in this embodiment of the invention is shown, which supports the upload of tissue slice images (PNG, JPG, etc.) and .h5ad files; Figure 11 The spatial domain recognition interface provided in the embodiment of the present invention is shown, presenting the spatial domain division results based on Ground Truth and the visual parameter adjustment controls such as background transparency and Spot display size; Figure 12 This paper illustrates a spatial domain identification data download interface provided in an embodiment of the present invention, displaying statistical information such as the number of domains and the total number of spots, as well as a result download button. Figure 13 The results download interface provided in this embodiment of the invention is shown, presenting downloadable .csv format analysis result files and .h5ad format complete data files; Figure 14 This paper presents an example of download analysis result data provided by an embodiment of the present invention, showing structured data including spot ID, coordinate information, ground truth tags, etc. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The following description, in conjunction with the accompanying drawings... Figure 1-14 The spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching is further explained.

[0019] Example 1 The core technical solution of this invention is based on a geometric distance matching algorithm, combined with an MVC (Model-View-Controller) architecture to build a system framework. It includes two core functional modules: multi-slice data alignment and spatial domain recognition, as well as three supporting modules: front-end interaction, back-end service, and data processing, to achieve integrated data processing throughout the entire process.

[0020] System Architecture The system is based on object-oriented design principles and uses the classic MVC (Model-View-Controller) architecture to build its overall framework. By clearly defining the boundaries between the front-end view layer, the back-end controller layer, and the data model layer, it effectively enhances the system's maintainability and scalability. The overall system architecture is referenced... Figure 1 .

[0021] The core design modules of this system are divided into the following three parts: Front-end interaction module: Based on the Streamlit framework, it integrates the development features of HTML and Python to build the user interaction interface. It supports functions such as module selection, data file upload (drag and drop / browse), target cell type filtering, parameter setting (similarity threshold, visualization parameters), and matching result viewing; it also supports 2D view cell type visibility control and 3D view rotation / scaling / connection length / azimuth / elevation angle multi-parameter adjustment.

[0022] Backend service module: Responsible for core business logic processing, including spatial coordinate extraction, cell type matching, geometric distance calculation, similarity scoring, spatial domain clustering, cell type visibility control, 3D view interaction control, etc. All functions are encapsulated through functions for easy maintenance and expansion.

[0023] Data processing module: Uses Scanpy to parse .h5ad files and manage spatial transcriptome data structures, process information such as spatial coordinates, cell types, and ground truth, and realize statistical analysis, storage, and visualization image generation of alignment results and spatial domain identification results. Supports export of result files (.csv / .h5ad format).

[0024] Core Algorithm Geometric distance matching alignment algorithm: First, common cell type identification is performed on the uploaded multi-slice spatial transcriptome data. Cell types common to all slices are automatically extracted and screened as matching candidate sets to ensure the biological rationality of the matching. Then, the two-dimensional spatial coordinates (x, y) of each candidate cell type in different slices are extracted. The Min-Max normalization method is used to process the coordinates to eliminate scale differences between different slices. The normalization formula is: (1) in For standardized coordinates, Original coordinates , These are the minimum and maximum coordinates for the cell type, respectively; for each common cell type, calculate the Euclidean distance between cells of the same type in the slices. The Euclidean distance formula is: (2) In the formula , The coordinates of similar cells in two slices are given. A nearest neighbor matching algorithm is used to find the closest matching pair for each cell, forming an initial matching pair. Finally, the matching pairs are filtered using a user-defined similarity threshold (0.00-1.00). The similarity calculation method is as follows: (3) In the formula , The minimum and maximum distances for matching pairs of this cell type are defined, and cell pairs with similarity ≥ the threshold are retained as alignment results, thus achieving accurate matching and alignment between multiple slices based on spatial structure.

[0025] Spatial domain recognition algorithm: After the user uploads tissue slice images and .h5ad files, the system automatically extracts cell spatial coordinates, gene expression features, and Ground Truth annotations; based on the Ground Truth, density clustering is performed on the cell coordinates and expression features, and functional regions in the tissue structure are delineated by calculating the comprehensive correlation between inter-cell spatial distance and gene expression similarity; relevant parameters such as the number of domains, total number of spots, and average number of spots / domain are automatically counted to generate quantitative statistical results, ensuring the consistency between the recognition results and the actual tissue structure.

[0026] Data processing flow During the data input and preprocessing stages, the system supports users uploading data files in specified formats (the data alignment module uploads two .h5ad files; the spatial domain recognition module uploads tissue slice images (PNG, JPG, JPEG, TIFF, TIF formats) and .h5ad files). The system automatically parses the files, extracts metadata such as spatial coordinates, cell type, and ground truth, and converts the raw data into a structured format (such as Pandas DataFrame) for easier subsequent processing.

[0027] After data upload and successful processing, the data alignment module automatically identifies common cell types in the two slices, allowing users to select cell types for alignment. A similarity threshold slider (range 0.00-1.00) is provided, enabling users to flexibly set matching precision based on data characteristics. The system executes a geometric distance matching algorithm to produce multiple visualization outputs, including a 2D cell spatial distribution map with control over cell type visibility, a rotatable and scalable 3D alignment view with connecting lines showing matching relationships, and a similarity score distribution histogram reflecting matching quality. The spatial domain identification module automatically performs spatial clustering based on Ground Truth, dividing functional regions within the tissue. Users can optimize the visualization by adjusting parameters such as background transparency and Spot display size, ultimately generating spatial domain visualization results. After analysis, the system counts and displays the number of domains, the total number of spots, and the distribution of each domain. It also supports exporting clustering results in .csv format and complete data in .h5ad format to meet users' needs for further analysis and archiving.

[0028] System operating environment and deployment (1) Hardware environment requirements Minimum requirements: CPU 2.0GHz, 8GB RAM, 256GB hard drive space, screen resolution 1600×900; Recommended configuration: CPU clock speed of 2.5GHz or higher, 16GB RAM or higher, 500GB hard disk space or higher, screen resolution of 1920×1080 or higher.

[0029] (2) Software operating environment requirements Windows 11 operating system (64-bit), supports Python 3.8+ environment, and depends on open source libraries such as Scanpy, Streamlit, Pandas, and NumPy.

[0030] (3) Installation and Startup Copy the software installation package to your local path, and enter the command "streamlit run app.py --server.maxUploadSize=5120" in the system terminal to start the software.

[0031] Example 2 Experimental Dataset Description Mouse embryo dataset: from the Stereo-seq platform, containing slice samples from continuous developmental stages from E9.5 to E16.5. Each slice contains 10,000+ single cells, covering primordial structures of multiple organs such as cardiovascular, nervous, and digestive systems. The tissue morphology exhibits large dynamic changes and cell types are diverse, which is used to verify the accuracy of the system in aligning multiple slices during dynamic development.

[0032] The DLPFC dataset consists of human dorsolateral prefrontal cortex data from the 10x Visium platform. It contains 12 consecutive slices that clearly cover the cortical layer (layers 4-6) and white matter (WM) structures. The cellular layer structure is clear and the spatial domain boundaries are well-defined, which is used to verify the accuracy of spatial domain identification.

[0033] All datasets conform to the .h5ad format standard and contain complete spatial coordinates, cell type annotations, and ground truth annotations, providing comprehensive support for functional validation and performance evaluation.

[0034] Example of spatial transcriptome data alignment for multiple slices Mouse embryo slide alignment (dynamic developmental tissue scenario): Select the .h5ad files of mouse embryo slides from E11.5 (critical period of organ primordium formation) and E12.5 (period of organ morphological differentiation), and start the system (e.g., Figure 2 After that, select the "Data Alignment" module and complete the upload of both files by dragging and dropping. Figure 3 As shown.

[0035] Enter the alignment settings interface ( Figure 6 Select 10 common core cell types, set the similarity threshold to 0.50, and click "Show 3D Matching View" to trigger the algorithm. In the 3D view ( Figure 7 By adjusting the connection length to 1.00, azimuth angle to 280°, and elevation angle to 20°, users can observe that the matching connections of similar cells are neat and without crossing or entanglement. In particular, the matching connection in the kidney primordium region (derived from the "urogenital ridge" of E11.5) accurately corresponds to the mesonephric and metanephric structures in early kidney development, which is completely consistent with the known developmental patterns of mouse embryos.

[0036] Simultaneously, specific cell types can be selected via the legend on the right side of the 3D view, or specific cell types can be selected through alignment settings (such as keeping only "Brain") to observe the cross-slice matching relationship of the target organ individually. Figure 8 The matching lines of the "Brain" type cells shown are neat and without crossing or entanglement, consistent with the known developmental patterns of mouse embryos.

[0037] The similarity score distribution histogram on the rating interface is displayed. Figure 9), 82% of the matching pairs had a similarity in the range of 0.70-0.90, of which 57% had a high similarity of ≥0.80, and the alignment accuracy reached 92%.

[0038] The results demonstrate that the system can effectively overcome the spatial differences caused by changes in tissue morphology and organ volume during embryonic development, providing a core tool for constructing a spatiotemporal map of embryonic development and supporting the tracking of the migration and differentiation trajectories of specific cell populations.

[0039] Spatial Domain Recognition Examples We selected tissue slice 151675 from the DLPFC dataset as the research object, chose the "Spatial Domain Recognition" module, and uploaded PNG format tissue slice images (resolution 1920×1080) and the corresponding .h5ad files. Figure 10 The system requires no additional parameter settings, automatically parses files and executes spatial domain clustering algorithms, and divides functional regions based on cell types and gene expression characteristics labeled with Ground Truth.

[0040] Enter the spatial domain recognition visualization interface as follows Figure 11 As shown, when the user adjusts the background image transparency to 0.50, the Spot display size to 60, and the global transparency to 0.70, eight spatial domains with different colors can be clearly observed. The boundaries of each domain are clear, and Layer_1 to Layer_6 are distributed in a continuous layered manner. The WM region is independent and has a clear boundary with the cortical layer, which is consistent with the anatomical structure of the human dorsolateral prefrontal cortex.

[0041] Statistical results show ( Figure 12 The total number of spots was 3592, with an average of 449 spots per domain. The distribution of the number of spots in each domain matched the physiological structural characteristics of the DLPFC. Layer 3 (771 spots) and Layer 5 (732 spots), as the main functional layers of the cortex, had significantly higher numbers of spots than other layers.

[0042] The system supports downloading results, such as... Figure 13 As shown, click "Download Analysis Results (CSV)" to obtain detailed data including spot ID, ground truth label, original coordinates, and aligned coordinates, as shown below. Figure 14 As shown, it can be used for subsequent analysis of tissue development patterns and spatial distribution studies of gene expression; click "Download Full Data (H5AD)" to obtain a complete dataset containing gene expression characteristics, supporting further in-depth analysis.

[0043] In this example, the entire process from data upload to statistical result generation took ≤1.7 minutes, and the core computation time of the clustering algorithm was ≤30 seconds. The eight identified spatial domains correspond precisely to the six cortical layers, white matter regions, and unknown functional regions (26 spots) of the DLPFC. Among them, the unknown functional regions may be transitional areas between the cortex and white matter, providing new targets for subsequent research on the interaction mechanism between the cortex and white matter. The exported data can be used to analyze differentially expressed genes in different spatial domains and reveal the functional specificity of each cortical layer.

[0044] System performance comprehensive evaluation The above examples demonstrate that the system exhibits excellent performance in core performance indicators. In terms of processing efficiency, single-group slice alignment (10,000+ cells) takes ≤2.8 minutes, and spatial domain recognition takes ≤1.7 minutes. Regarding robustness, in DLPFC slice data with added random noise of 10%, the alignment accuracy decreases by only 3%, indicating that the system has strong resistance to technical noise. These results fully demonstrate that the system of this invention possesses accuracy, efficiency, and robustness in multi-slice alignment and spatial domain recognition tasks, making it suitable for various complex research scenarios and possessing significant scientific research value and application prospects.

[0045] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching, characterized in that, include: The data input module is used to receive multiple spatial transcriptome slice data files uploaded by the user; A common cell type identification module is used to automatically identify and filter out common cell types from the multiple slice data; The geometric distance matching module is used to extract the spatial coordinates of the common cell type, standardize the coordinates, calculate the Euclidean distance between cells of the same type in slices, and generate initial matching pairs through the nearest neighbor matching algorithm. The similarity filtering module is used to filter the initial matching pairs according to the similarity threshold set by the user to obtain the final alignment result; The spatial domain identification module is used to divide tissue spatial functional regions based on cell spatial coordinates and gene expression characteristics using clustering algorithms; The results output and visualization module is used to output alignment and spatial domain recognition results and provide 2D / 3D visualization views.

2. The spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching according to claim 1, characterized in that, In the geometric distance matching module, the spatial coordinates are standardized using the Min-Max standardization method. The standardization formula is as follows: (1) in, For standardized coordinates, Original coordinates , These are the minimum and maximum coordinates for this cell type, respectively.

3. The spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching according to claim 2, characterized in that, In the geometric distance matching module, the formula for calculating the Euclidean distance is: (2) in, , These are the coordinates of two cells in a common cell type between two slices.

4. The spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching according to claim 1, characterized in that, In the similarity filtering module, the formula for calculating the similarity of matching pairs is: (3) in, dist To match the Euclidean distance between two cells in a pair. , These are the minimum and maximum distances for all matching pairs, respectively.

5. The spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching according to claim 1, characterized in that, The system adopts an MVC architecture, including: The front-end interaction module, built on the Streamlit framework, is used to provide a user interface. The backend service module is used to execute core business logic processing; The data processing module is used to parse data files, manage data structures, and generate visualized data.

6. The spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching according to claim 5, characterized in that, The front-end interaction module supports drag-and-drop / browse upload of data files, target cell type filtering, similarity threshold adjustment, control of cell type visibility in 2D view, and adjustment of rotation, scaling, line length, azimuth, and elevation parameters in 3D view.

7. The spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching according to claim 5, characterized in that, The data processing module uses the Scanpy library to parse .h5ad format files and exports the results in .csv or .h5ad format.

8. The spatial transcriptome multi-slice alignment and domain identification system based on geometric distance matching according to claim 1, characterized in that, The spatial domain identification module automatically extracts spatial coordinates and gene expression features based on user-uploaded tissue slice images and corresponding .h5ad files, and performs density clustering based on Ground Truth annotations to identify spatial functional domains.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the functions of each module of the system as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the functions of the various modules of the system as described in any one of claims 1-8.

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