Computational Decoding of Array Emission Signals for Parallel Detection
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Solution Overview
Problem
Biological assays face challenges in efficiently detecting a large number of small amounts of different biological, chemical, and/or physical entities due to constraints on array design, material usage, density, and instrumentation complexity, leading to issues like increased scanning time, noise, and reduced detection specificity.
Innovation Solution
A method involving exposure to electromagnetic radiation, pixel information acquisition using light sensing devices, and categorical classification of emission signals to detect components of arrays, reducing scanning time and noise while improving resolution and detection specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of objects to be detected is increased, then the detection capacity is improved, but the scanning time increases
Solution Approach 1:
The detection system is segmented into multiple independent detection channels, each capable of simultaneously detecting different objects. This allows parallel processing of multiple objects without sequential scanning, thereby reducing total scanning time while maintaining high detection capacity.
Solution Approach 2:
The system transitions from sequential scanning in one dimension to parallel detection across multiple dimensions simultaneously. By using multiple detection channels and spatial arrangements, the system achieves concurrent detection of multiple objects, eliminating the time penalty associated with increased object quantity.
2Quantity of substance
If the density of objects on substrate is increased, then the detection capacity is improved, but noise levels increase
Solution Approach 1:
Each detection channel is optimized with local quality control, including localized background subtraction and individual signal calibration. This allows high-density object placement while maintaining low noise levels through localized signal processing that compensates for nearby objects and reduces interference.
Solution Approach 2:
The system introduces intermediary processing steps including signal filtering, background correction, and computational decoding algorithms that act as mediators between raw signals and final detections. These intermediaries separate true signals from noise even in high-density configurations where objects are closely spaced.
3Measurement precision
If the complexity of instrumentation is increased, then the detection specificity is improved, but the device complexity increases
Solution Approach 1:
The detection system employs universal detection channels that can identify multiple object types through a single instrumentation platform. By using multi-functional detection mechanisms and unified processing algorithms, the system achieves high detection specificity across diverse objects without proportionally increasing instrumentation complexity.
Solution Approach 2:
The system replaces complex mechanical separation and identification mechanisms with computational decoding methods. Instead of using physically distinct detection paths for each object type, the system uses algorithmic classification of signals, thereby maintaining high specificity while reducing mechanical complexity.
4Measurement precision
If the amount of material used for each object is increased, then the detection sensitivity is improved, but the material constraints are worsened
Solution Approach 1:
The system uses signal copying and amplification techniques where the detection signal is replicated across multiple detection channels and processed computationally. This allows sensitive detection of small amounts of material through signal multiplication and correlation analysis, maintaining sensitivity without requiring proportionally more material.
Solution Approach 2:
The system changes detection parameters including signal processing algorithms, detection thresholds, and analysis methods to optimize sensitivity for small material amounts. By adapting computational parameters and detection thresholds, the system maintains high sensitivity despite reduced material quantity, balancing detection capability with material constraints.
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
The method achieves parallel imaging without moving parts, reduces noise, enhances resolution, decreases crosstalk, and improves detection sensitivity and specificity by accurately identifying emission signals.
Implementation Method 1
exposing the array of biological, chemical, or physical entities to electromagnetic radiation sufficient to excite the array, thereby producing an emission signal of the array
Implementation Method 2
using one or more light sensing devices, acquiring a plurality of pixel information of the emission signal of the array
Data Source
AI summary
The present disclosure provides systems and methods for detecting components of an array of biological, chemical, or physical entities. In an aspect, the present disclosure provides a method for detecting an array of biological, chemical, or physical entities, comprising: (a) using one or more light sensing devices, acquiring pixel information from sites in an array, wherein the sites comprise biological, chemical, or physical entities that produce light; (b) processing the pixel information to identify a set of regions of interest (ROIs) corresponding to the sites in the array that produce the light; (c) classifying the pixel information for the ROIs into a categorical classification from among a plurality of distinct categorical classifications, thereby producing a plurality of pixel classifications; and (d) identifying one or more components of the array of biological, chemical, or physical entities based at least in part on the plurality of pixel classifications.