Challenges with development of a pharmacokinetics assay to measure a variably glycosylated fusion protein

Aim: Development of recombinant fusion proteins as drugs poses unique challenges for bioanalysis. This paper describes a case study of a glycosylated fusion protein, where variable glycosylation, matrix interference and high sensitivity needs posed unique challenges. Results: Six different assay configurations, across four different platforms were evaluated for measurement of drug concentrations. Two platforms that achieved the assay requirements were Simoa HD-1 and immune-capture LC–MS/MS-based assay. Conclusion: Both, Simoa HD-1 and the mass spectrometry-based methods were able to detect total drug by providing the adequate matrix tolerance, required sensitivity and detection of all the various glycosylated fusion proteins to support clinical sample analysis. The mass spectrometry-based method was selected due to robustness and ease of method transfer.

A PROPOSAL FOR METHOD TRANSFER BASED ON SUCCESSFUL EXAMPLES OF INTRA-INDUSTRY METHOD TRANSFER

To transfer methods from science to industrial application is an important task of engineering design researchers. However, the way in which this is done leaves still room for improvement. A look beyond the horizon into the intra-industrial transfer of methods can therefore be helpful. Based on general requirements and success factors as well as successful intra-industry transfer examples, this paper proposes the P4I process for the transfer of methods from academy to industry.

Alighting Stop Determination of Unlinked Trips Based on a Two-Layer Stacking Framework

Smart card data of conventional bus passengers are important basic data for many studies such as bus network optimization. As only boarding information is recorded in most cities, alighting stops need to be identified. The classical trip chain method can only detect destinations of passengers who have trip cycles. However, the rest of unlinked trips without destinations are hard to analyze. To improve the accuracy of existing methods for determining alighting stops of unlinked trips, a two-layer stacking-framework-based method is proposed in this work. In the first layer, five methods are used, i.e., high-frequency stop method, stop attraction method, transfer convenience method, land-use type attraction method, and improved group historical set method (I-GHSM). Among them, the last one is presented here to cluster records with similar behavior patterns into a group more accurately. In the second layer, the logistic regression model is selected to get the appropriate weight of each method in the former layer for different datasets, which brings the generalization ability. Taking data from Xiamen BRT Line Kuai 1 as an example, I-GHSM given in the first layer has proved to be necessary and effective. Besides, the two-layer stacking-framework-based method can detect all destinations of unlinked trips with an accuracy of 51.88%, and this accuracy is higher than that of comparison methods, i.e., the two-step algorithms with KNN (k-nearest neighbor), Decision Tree or Random Forest, and a step-by-step method. Results indicate that the framework-based method presented has high accuracy in identifying all alighting stops of unlinked trips.

Rolling Bearing Fault Diagnosis Based on Domain Adaptation and Preferred Feature Selection under Variable Working Conditions

In real industrial scenarios, with the use of conventional machine learning techniques, data-driven diagnosis models have a limitation that it is difficult to achieve the desirable fault diagnosis performance, and the reason is that the training and testing datasets are assumed to have the same feature distributions. To address this problem, a novel bearing fault diagnosis framework based on domain adaptation and preferred feature selection is proposed, in that the model trained by the labeled data collected from a working condition can be applied to diagnose a new but similar target data collected from other working conditions. In this framework, an improved domain adaptation method, transfer component analysis with preserving local manifold structure (TCAPLMS), is proposed to reduce the differences in the data distributions between different domain datasets and, at the same time, take the label information of feature dataset and the local manifold structure of feature data into consideration. Furthermore, preferred feature selection by fault sensitivity and feature correlation (PSFFC) is embedded into this framework for selecting features which are more beneficial to fault pattern recognition and reduce the redundancy of feature set. Finally, vibration datasets collected from two test platforms are used for experimental analysis. The experimental results validate that the proposed method can obviously improve diagnosis accuracy and has significant potential benefits towards actual industrial scenarios.

Modelling of Critical Velocities of the Cardan Mechanism using Transfer Matrix Method

The presented paper focuses to rotating components of mechanical constructions. The problem of the spatial combined bending-gyratory vibration and calculation of the Eigen frequencies is studied. The model of Cardan Mechanism is solved by the transfer matrix method. Transfer matrices were derived for shaft, concentrated mass and elastic bearing. The physical and mechanical properties of each part of the mechanism are hidden in these matrices. A procedure for calculating Eigen frequencies was proposed.