Analyte Data Processing for SSD Lifespan and Throughput
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Solution Overview
Problem
Current methods for identifying nucleic acid sequences using multiple coloring rounds generate large data volumes, leading to high memory and processing requirements, and SSD hard drives face limitations due to limited write cycles, resulting in system failure and reduced sample throughput.
Innovation Solution
A method that assesses and eliminates irrelevant color information in each data point, combining color information from neighboring pixels, and using machine learning models to identify candidate data points, reducing data volume and processing complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all color information from multiple coloring rounds is stored for evaluation, then complete analyte identification is achieved, but data storage requirements and processing time increase significantly
Solution Approach 1:
The patent extracts and eliminates irrelevant color information from data points that do not represent actual analytes. By assessing each data point to determine if it represents a real analyte or background noise, the system removes unnecessary data while preserving the color information needed for accurate analyte identification, thus reducing data volume without compromising measurement precision
Solution Approach 2:
The patent performs preliminary assessment of data points to identify and eliminate irrelevant color information before storage. By pre-processing the data to remove background noise and non-analyte signals, the system reduces the data volume that needs to be stored and processed later, while maintaining complete information about actual analytes for accurate identification
2Reliability
If large data volumes are stored on SSD hard drives, then complete color information is preserved, but the limited write cycles are quickly exhausted leading to system failure
Solution Approach 1:
The patent extracts and eliminates irrelevant color information before storage, reducing the total data volume written to the SSD. By removing background noise and non-analyte data points, the system preserves only the essential color information needed for analyte identification, thereby extending SSD lifespan through reduced write cycles while maintaining data integrity of the stored information
Solution Approach 2:
The patent discards irrelevant color information that does not contribute to analyte identification. By selectively eliminating background noise and non-analyte signals, the system recovers storage capacity and extends SSD operational life, while preserving the critical color information needed for reliable analyte detection
3Measurement precision
If all color information is processed and evaluated, then comprehensive analyte identification is achieved, but computing time and processing effort increase significantly
Solution Approach 1:
The patent extracts and eliminates irrelevant color information before the main processing and evaluation stages. By removing background noise and non-analyte data points in advance, the system reduces the amount of data that requires computationally intensive processing, thereby significantly decreasing processing time while maintaining the complete color information needed for accurate analyte identification of actual targets
Solution Approach 2:
The patent performs preliminary assessment and elimination of irrelevant data points before the main evaluation process. By pre-filtering the data to remove background noise and non-analyte signals, the system reduces the computational burden of subsequent processing steps, achieving faster analyte identification without compromising the accuracy of color information evaluation for actual analytes
4Productivity
If high sample throughput is achieved by reducing processing time, then productivity increases, but data assessment accuracy may be compromised
Solution Approach 1:
The patent extracts and eliminates irrelevant color information using automated assessment algorithms that quickly identify background noise and non-analyte data points. This pre-processing step reduces data volume before storage and processing, enabling faster sample throughput while maintaining accurate color information for actual analytes, thus preserving data assessment accuracy despite increased productivity
Solution Approach 2:
The patent performs preliminary automated assessment of data points to eliminate irrelevant information before main processing. By using efficient algorithms to pre-filter background noise and non-analyte signals, the system enables rapid data reduction that increases sample throughput without compromising the accuracy of subsequent analyte identification, maintaining measurement precision while improving productivity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces data storage needs, allowing for efficient and rapid processing of color information, extending SSD lifespan and improving sample throughput by eliminating unnecessary data points and using machine learning for precise identification.
Implementation Method 1
The markers are made up of oligonucleotides and dyes coupled thereto, which are generally fluorescent dyes
Data Source
AI summary
A method for preparing data for identifying analytes by coloring one or more analytes with markers in multiple coloring rounds, the markers in each case being specific for a certain set of analytes, detecting multiple markers using a camera, which for each coloring round generates at least one image that includes multiple pixels to which a color value is assigned in each case as color information, and includes colored signals and uncolored signals, wherein a colored signal is a pixel containing color information of a marker, and an uncolored signal is a pixel containing color information that is not based on a marker. A data point in each case includes one or more contiguous pixels in the images of the multiple coloring rounds that are assigned to the same location in a sample.