MMAF ADC EIA Kit

Name: MMAF ADC EIA Kit
SKU: DEIABL313
Rating: 5 (1 reviews)

Regulatory status: For research use only, not for use in diagnostic procedures.

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COA

SDS

Datasheet

Sample

tissue cell cultures, serum

Species Reactivity

Human, Rat, Mouse, Primate

Intended Use

This test kit is intended for use in the quantitative determination of antibody-MMAF-conjugate level in test sample. It is useful for preclinical and clinical pharmacology study of MMAF Antibody Drug Conjugate (ADC).
- Samples from tissue/cell culture and serum samples from human, rat, mouse, primate, etc. can be used directly with this kit.
- Both humanized monoclonal antibody based MMAF-ADC and mouse monoclonal antibody based MMAF-ADC can be measured with this kit.

Contents of Kit

1. Anti-MMAF Antibody Coated Microplate
2. HRP Conjugated MMAF
3. ELISA Wash Concentrate
4. ELISA HRP Substrate
5. ELISA Stop Solution
6. Antibody-MMAF Conjugated Zero Calibrator

Storage

Unopened kit: Store at 2-8°C. Do not use the kit beyond the expiration date.

Performance Characteristics

High Dose "hook" effect
This assay has showed that it didn't have any high dose "hook" effect for MMAF ADC levels up to 1,000 μg/mL.

Precision

The intra-assay precision was validated by measuring three calibrators (L3, L4 and L7) in six replicate determinations. The CV% is 5.5%, 4.9% and 6.1%.

Sensitivity

The analytical sensitivity (LLOD) of this MMAF ADC EIA as determined by the 2 times standard deviation below the mean of B0 on 12 duplicate determinations of zero standard (B0) is approximately 0.019 μg/mL.

Standard Curve

Citations

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Datasheet

Certificate of Analysis

Safety Data Sheet

Customer Reviews

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Multi-sensor Track Fusion via Multiple-Model Adaptive Filter

PROCEEDINGS OF THE 48TH IEEE CONFERENCE ON DECISION AND CONTROL, 2009 HELD JOINTLY WITH THE 2009 28TH CHINESE CONTROL CONFERENCE (CDC/CCC 2009)

Authors: Fong, Li-Wei

Abstract

A Multiple-Model Adaptive Filter (MMAF) is developed for use in multi-sensor track fusion systems for target tracking. The architecture of hierarchical fusion consists of several local processors and a global processor. Each local processor collects measurement data from a sensor and then using Kalman filter performs tracking function. The global processor utilizes the MMAF which consists of Information Matrix Filter (IMF) with two levels of common process noise and a decision logic switch to aggregate the outputs of local processors. The switch is designed by adopting the modified probabilistic neural network to compute the probability of each IMF for providing the switching capability to respond target dynamics. The resulting filter has better tracking performance than each individual IMF. Simulation results are included to demonstrate the effectiveness of proposed algorithm.

A Decentralized Multiple Model Adaptive Filtering for Discrete-Time Stochastic Systems

JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME

Authors: Watanabe, Keigo

Abstract

A decentralized multiple model adaptive filter (MMAF) is proposed for linear discrete-time stochastic systems. The structure of decentralized multiple model studied here is based on introducing a global hypothesis for the global model and a local hypothesis for the local model, where it is assumed that the former hypothesis includes the latter one as a partial element. Algorithms for the decentralized MMAFs in unsteady and steady-state are derived using recent results in decentralized Kalman filtering. The results can be applied in designing a system for sensor failure detection and identification (FDI). An example is included to illustrate the characteristics of such a FDI system for the estimation of lateral dynamics of the hydrofoil boat.