Nonstationary Discrete Convolution Kernel for Multimodal Process Monitoring.

Data-driven process monitoring has benefited from the development and application of kernel transformations, especially when various types of nonlinearity exist in the data. However, when dealing with the multimodality behavior that is frequently observed in the process operations, the most widely used radial basis function (RBF) kernel has limitations in describing process data collected from multiple normal operating modes. In this article, we highlight this limitation via a synthesized example.

Multi-criteria manufacturability indices for ranking high-concentration monoclonal antibody formulations.

The need for high-concentration formulations for subcutaneous delivery of therapeutic monoclonal antibodies (mAbs) can present manufacturability challenges for the final ultrafiltration/diafiltration (UF/DF) step. Viscosity levels and the propensity to aggregate are key considerations for high-concentration formulations. This work presents novel frameworks for deriving a set of manufacturability indices related to viscosity and thermostability to rank high-concentration mAb formulation conditions in terms of their ease of manufacture.

Data mining for rapid prediction of facility fit and debottlenecking of biomanufacturing facilities.

Higher titre processes can pose facility fit challenges in legacy biopharmaceutical purification suites with capacities originally matched to lower titre processes. Bottlenecks caused by mismatches in equipment sizes, combined with process fluctuations upon scale-up, can result in discarding expensive product. This paper describes a data mining decisional tool for rapid prediction of facility fit issues and debottlenecking of biomanufacturing facilities exposed to batch-to-batch variability and higher titres.

Integration of stochastic simulation with multivariate analysis: short-term facility fit prediction.

This article describes a decision-support tool to help pinpoint the potential root causes of sub-optimal short-term facility fit issues in biopharmaceutical facilities. This was achieved by creating a tool that integrated stochastic simulation with advanced multivariate statistical analysis. Process fluctuations in product titers in cell culture, step yields, and chromatography eluate volumes were mimicked using Monte Carlo simulation data derived using a stochastic discrete-event simulation model.

Principal component score modeling for the rapid description of chromatographic separations.

This paper describes the use of Principal Component Analysis (PCA) as a tool for modeling chromatographic separations. PCA is an analytical technique developed to extract key information out of large data sets and to develop relationships and correlations. The basis of the proposed model is the use of PCA to correlate experimental chromatographic data across different process variables or scales.

Principal component analysis of nonlinear chromatography.

Principal component analysis (PCA) has been used for the modeling of nonlinear chromatography under overload conditions. A 10-fold range of crude erythromycin samples were loaded onto columns with different stationary-phase chemistries (2 polystyrene, 1 methacrylate) in direct proportion to the bed volumes. The elution profiles indicated slightly concave isotherms for the polystyrene supports and a convex Langmuirian isotherm for the methacrylic support used.

Near-infrared spectroscopy for bioprocess monitoring and control.

This article describes the calibration of a spectroscopic scanning instrument for the measurement of selected contaminants in a complex biological process stream. Its use is for the monitoring of a process in which contaminants are to be removed selectively by flocculation from yeast cell homogenate. The main contaminants are cell debris, protein, and RNA.

Closed-loop control of fed-batch cultures of recombinant Escherichia coli using on-line HPLC.

This article describes a fully automated system for the on-line monitoring and closed-loop control of a fed-batch fermentation of recombinant Escherichia coli, and presents two case studies of its used in limiting production of unwanted byproducts such as acetic in fed-batch fermentations. The system had two components. The first components, on-line monitoring, comprised an aseptic sampling device, a microcentrifuge, and HPLC System.

A visual programming environment for bioprocess control.

The paper introduces the use of a visual programming environment (LabVIEW) to program custom control functions for bioprocess research. The time taken for a bioprocess scientist to program new functions compared well with typical times expected for experienced programmers using conventional languages. Experienced LabVIEW programmers will develop applications significantly faster.

Analysis of noise and bias in fermentation oxygen uptake rate data.

The calculation of many derived fermentation variables such as the respiratory quotient (RQ) and mass transfer coefficient (K(L)a) requires the differences between the molar percentages of oxygen and carbon dioxide in the fermentor inlet and exit gas, called the %OUR and %CER. Noise and bias in %CER data is of order that in the exit gas carbon dioxide analysis. However, the relative amount of noise in the %OUR is one to two orders of magnitude greater than the noise in the raw oxygen analyses because the %OUR is calculated as a small difference between two large quantities.

An evolutionary analysis of psychological pain following human (Homo sapiens) rape: IV. The effect of the nature of the sexual assault.

Mental pain is hypothesized to manifest an adaptation for analyzing and coping with social problems that would have reduced inclusive fitness in human (Homo sapiens) evolutionary history. We examined this hypothesis in the psychological pain of 790 rape victims. Reproductive-aged and postreproductive-aged victims were more likely than prereproductive-aged victims to have experienced vaginal intercourse and to have had sperm present in the reproductive tract.