Biologics manufacturing is increasingly moving toward intensified processes that require novel control strategies in order to achieve higher titers in shorter periods of time compared to traditional ...fed-batch cultures. In order to implement these strategies for intensified processes, continuous process monitoring is often required. To this end, inline Raman spectroscopy was used to develop partial least squares models to monitor changes in residual concentrations of glucose, phenylalanine and methionine during the culture of five different glutamine synthetase piggyBac
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Chinese hamster ovary clones cultured using an intensified high inoculation density fed-batch platform process. Continuous monitoring of residual metabolite concentrations facilitated automated feed-rate adjustment of three supplemental feeds to maintain glucose, phenylalanine, and methionine at desired setpoints, while maintaining other nutrient concentrations at acceptable levels across all clones cultured on the high inoculation density platform process. Furthermore, all clones cultured on this process achieved high viable cell concentrations over the course of culture, indicating no detrimental impacts from the proposed feeding strategy. Finally, the automated control strategy sustained cultures inoculated at high cell densities to achieve product concentrations between 5 and 8.3 g/L over the course of 12 days of culture.
Coastal marsh habitat and its associated vegetation are strongly linked to substrate elevation and local drainage patterns. As such, accurate representations of both the vegetation height and the ...surface elevations are requisite components for systematic analysis and temporal monitoring of the habitat. Topographic Light Detection and Ranging (LIDAR) data can provide high-resolution, high-accuracy elevation measurements of features both aboveground and at the surface. However, because of poor penetration of the laser pulse through the marsh vegetation, bare-earth LIDAR elevations can be markedly less accurate when compared with adjacent upland habitats. Consequently, LIDAR ground-elevation errors (i.e., standard deviation SD and bias) can vary significantly from the standard upland land-cover classes quoted in a typical data provider's quality-assurance report. Custom digital elevation model (DEM) generation techniques and point classification processes can be used to improve estimates of ground elevations in coastal marshes. The simplest of these methods is minimum bin gridding, which extracts the lowest elevation value included within a user-specified search window and assigns that value to the appropriate DEM grid cell. More complex point-to-point classification can be accomplished by enforcing stricter slope limits and increasing the level of smoothing. Despite lowering the spatial resolution of the DEM, the application of these techniques significantly improves the vertical accuracy of the LIDAR-derived bare-earth surfaces. By employing the minimum bin technique to the bare-earth classified LIDAR data, the overall bias in the resultant surface was reduced by 12 cm, and the vertical accuracy was improved by 8 cm when compared with the “as-received” data.
The use of Raman models for glucose and phenylalanine concentrations to provide the signal for a control algorithm to continuously adjust the feed rate of two separate supplemental feeds during the ...fed-batch culture of a CHOK1SV GS-KO® cell line in a platform process was evaluated. Automated feed rate adjustment of the glucose feed using a Raman model for glucose concentration, maintained the glucose concentration within the desired target (average deviation ± 0.49 g/L). Automated feed rate adjustment of the nutrient feed using a Raman model for phenylalanine concentration, maintained phenylalanine concentrations within the target (average deviation ± 29.97 mg/L). The novel use of a Raman model for phenylalanine concentration, combined with a Raman model for glucose concentration, to maintain target glucose and phenylalanine concentrations through feed-rate adjustments, reduced the average cumulative glucose and nutrient feed additions (19% and 27% respectively) compared to manually adjusted cultures. Additionally, the proposed automation strategy led to lower osmolality during culture, maintained the nutrient environment more consistently, and achieved higher harvest product concentration (≈ 20% higher) compared to typical fed-batch process control for the cell line and platform process evaluated. Furthermore, the proposed feeding strategy yielded similar glycosylation and charge variant profiles compared to manually adjusted fed-batch process control. The ability to continuously adjust the feed rate addition of two separate feeds in this manner helps enable a shift away from the current daily offline sampling needed to control fed-batch mammalian cell culture during clinical and commercial manufacturing on platform processes.
•Use of plant cover in remote sensing is complicated due to many ways cover is calculated.•We model vegetation cover from RapidEye imagery using two cover calculation methods.•Cover indicators using ...intercept data from all canopy layers gave lower model RMSE.•Indicator meaning and model performance should influence choice of calculation method.
Accurate and timely spatial predictions of vegetation cover from remote imagery are an important data source for natural resource management. High-quality in situ data are needed to develop and validate these products. Point-intercept sampling techniques are a common method for obtaining quantitative information on vegetation cover that have been widely implemented in a number of local and national monitoring programs. The use of point-intercept data in remote sensing projects, however, is complicated due to differences in how vegetation cover indicators can be calculated. Decisions on whether to use plant intercepts from any canopy layer (i.e., any-hit cover) or only the first plant intercept at each point (i.e., top-hit cover) can result in discrepancies in cover estimates which are used to train remotely-sensed imagery. Our objective in this paper was to explore the theory of point-intercept sampling relative to training and testing remotely-sensed imagery, and to test the strength of relationships between top-hit and any-hit methods of calculating vegetation cover and high-resolution satellite imagery in two study areas managed by the Bureau of Land Management in northwestern Colorado and northeastern California. We modeled top-hit and any-hit percent cover for six vegetation indicators from 5m-resolution RapidEye imagery using beta regression. Model performance was judged using normalized root mean-squared error (RMSE) from a 5-fold cross validation. Any-hit cover estimates were significantly higher (α<0.05) than top-hit cover estimates for forbs and grasses in the White River study area, but only marginally higher in Northern California. Pseudo-R2 values for beta regression models of vegetation cover from RapidEye image information varied from 0.1525 to 0.7732 in White River and 0.2455 to 0.6085 in Northern California, with little pattern to whether any-hit or top-hit indicators produced better model fit. However, normalized RMSE was lower for any-hit cover (indicating better model performance) or minimally higher than top-hit cover for all indicators in each study area. Our results do not support the idea that top-hit cover estimates from point-intercept sampling are the most appropriate for remote sensing applications in arid and semi-arid shrub-steppe environments. In fact, having two sets of different indicators calculated from the same data may cause additional confusion in a situation where there is already considerable debate on how vegetation cover should be measured and used. Ultimately, selection of indicators to use for developing remote sensing classification or predictive models should be based first on the meaning or interpretation of the indicator in the ecosystem of interest, and second on how well the indicator performs in modeling applications.
Remotely sensed imagery at multiple spatial scales is used increasingly in conjunction with field data to estimate rangeland indicators (e.g., vegetation cover) and meet the growing need for ...landscape-scale monitoring and assessment of rangelands. Remote sensing studies that produce rangeland indicators often require intensive and costly field-data collection efforts to produce accurate model predictions. Existing monitoring data, such as those collected by the Bureau of Land Management's Assessment, Inventory, and Monitoring (AIM) program, are potentially useful sources of field data in remote sensing modeling studies. Given their data-hungry nature, common regression tree — based modeling approaches may be inadequate for reliably predicting rangeland indicators with the smaller sample sizes of AIM data than typically used for remote sensing studies. Current literature suggests that Bayesian models, such as Bayesian additive regression trees (BART), may provide a suitable alternative to traditional regression tree — based modeling approaches to overcome the sample size limitation of the AIM data. In this study, we used 182 AIM field plots together with both high (RapidEye) and moderate (Landsat OLI) spatial resolution satellite imagery to predict bare ground and bare soil, total foliar, herbaceous, woody, and shrub cover indicators on rangelands in a 14 625-km2 area of northeastern California. We demonstrate that a BART model performed similarly to other regression tree approaches when field data and high spatial resolution imagery predictions were combined to predict indicator values using the medium spatial resolution Landsat image. The BART models also provided spatially explicit uncertainty estimates, which allow land managers to more carefully evaluate indicator predictions and to identify areas where future field data collection might be most useful. This study demonstrates that existing field data and freely available, remotely sensed imagery can be integrated to produce spatially explicit and continuous surface estimates of rangeland indicators across entire landscapes.
While monospecific antibodies have long been the foundational offering of protein therapeutics, recent advancements in antibody engineering have allowed for the development of far more complex ...antibody structures. Novel molecular format (NMF) proteins, such as bispecific antibodies (BsAbs), are structures capable of multispecific binding, allowing for expanded therapeutic functionality. As demand for NMF proteins continues to rise, biomanufacturers face the challenge of increasing bioreactor process productivity while simultaneously maintaining consistent product quality. This challenge is exacerbated when producing structurally complex proteins with asymmetric modalities, as seen in NMFs. In this study, the impact of a high inoculation density (HID) fed-batch process on the productivity and product quality attributes of two CHO cell lines expressing unique NMFs, a monospecific antibody with an Fc-fusion protein and a bispecific antibody, compared to low inoculation density (LID) platform fed-batch processes was evaluated. It was observed that an intensified platform fed-batch process increased product concentrations by 33 and 109% for the two uniquely structured complex proteins in a shorter culture duration while maintaining similar product quality attributes to traditional fed-batch processes.
The monitoring and control of mammalian cell culture processes are critical for the safe and cost-effective development of biotherapeutics in drug development. Current control using off line sampling ...is infrequent and can miss important metabolic shifts which can impact product quality and consistency. Raman spectroscopy can provide in-line monitoring of multiple metabolites and critical process parameters to allow automation and process improvements. In this research a generic model was developed for monitoring multiple cell lines with a platform Chinese hamster ovary (CHO) cell process. Parameter estimation was then used for the implementation of feedback control. Two nutrient feeds were added based on the culture needs as measured approximately every hour for viable cell and glucose concentrations without operator intervention. This demonstrated comparable, if not improved, control compared to historically adjusting feeds based on daily off-line sampling.
This research evaluated the vertical accuracy of two lidar-derived elevation datasets acquired from two different altitudes over a clay-capped hazardous waste site located on the Savannah River Site ...(SRS), South Carolina, using the same Optech ALTM 2050 lidar sensor and Cessna 337
platform. Both missions provided adequate elevation estimates (low-altitude RMSEz 6 cm; high-altitude RMSEz 14 cm.) A quantitative comparison was performed to determine how decreasing platform altitude and increasing lidar posting density affected the vertical elevation
accuracy. Higher posting densities did not significantly improve the vertical accuracy of lidar-derived elevation data. Conversely, acquiring the lidar-derived elevation data at a lower altitude had a significant influence on the mean vertical error present in the lidar-derived elevation data.
Differences in mean vertical elevation error between the lowand high-altitude lidar data collection missions were primarily due to a systematic underestimation bias present in the high-altitude lidar data.
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