Search

The aim of this study was to semi-automate process analytics for the quantification of common impurities in downstream processing such as host cell DNA, host cell proteins and endotoxins using a commercial liquid handling station. By semi-automation, the work load to fully analyze the elution peak of a purification run was reduced by at least 2.41 h. The relative standard deviation of results among different operators over a time span of up to 6 months was at the best reduced by half, e.g. from 13.7 to 7.1% in dsDNA analysis. Automation did not improve the reproducibility of results produced by one operator but released time for data evaluation and interpretation or planning of experiments. Overall, semi-automation of process analytics reduced operator-specific influence on test results. Such robust and reproducible analytics is fundamental to establish process analytical technology and get downstream processing ready for Quality by Design approaches.

Ultrafiltration is a powerful method used in virtually every pharmaceutical bioprocess. Depending on the process stage, the product-to-impurity ratio differs. The impact of impurities on the process depends on various factors. Solely mechanistic models are currently not sufficient to entirely describe these complex interactions. We have established two hybrid models for predicting the flux evolution, the protein rejection factor and two components&rsquo, concentration during crossflow ultrafiltration. The hybrid models were compared to the standard mechanistic modeling approach based on the stagnant film theory. The hybrid models accurately predicted the flux and concentration over a wide range of process parameters and product-to-impurity ratios based on a minimum set of training experiments. Incorporating both components into the modeling approach was essential to yielding precise results. The stagnant film model exhibited larger errors and no predictions regarding the impurity could be made, since it is based on the main product only. Further, the developed hybrid models exhibit excellent interpolation properties and enable both multi-step ahead flux predictions as well as time-resolved impurity forecasts, which is considered to be a critical quality attribute in many bioprocesses. Therefore, the developed hybrid models present the basis for next generation bioprocessing when implemented as soft sensors for real-time monitoring of processes.

Increased efforts are required to prevent further losses to terrestrial biodiversity and the ecosystem services that it provides1,2. Ambitious targets have been proposed, such as reversing the declining trends in biodiversity3; however, just feeding the growing human population will make this a challenge4. Here we use an ensemble of land-use and biodiversity models to assess whether—and how—humanity can reverse the declines in terrestrial biodiversity caused by habitat conversion, which is a major threat to biodiversity5. We show that immediate efforts, consistent with the broader sustainability agenda but of unprecedented ambition and coordination, could enable the provision of food for the growing human population while reversing the global terrestrial biodiversity trends caused by habitat conversion. If we decide to increase the extent of land under conservation management, restore degraded land and generalize landscape-level conservation planning, biodiversity trends from habitat conversion could become positive by the mid-twenty-first century on average across models (confidence interval, 2042–2061), but this was not the case for all models. Food prices could increase and, on average across models, almost half (confidence interval, 34–50%) of the future biodiversity losses could not be avoided. However, additionally tackling the drivers of land-use change could avoid conflict with affordable food provision and reduces the environmental effects of the food-provision system. Through further sustainable intensification and trade, reduced food waste and more plant-based human diets, more than two thirds of future biodiversity losses are avoided and the biodiversity trends from habitat conversion are reversed by 2050 for almost all of the models. Although limiting further loss will remain challenging in several biodiversity-rich regions, and other threats—such as climate change—must be addressed to truly reverse the declines in biodiversity, our results show that ambitious conservation efforts and food system transformation are central to an effective post-2020 biodiversity strategy. To promote the recovery of the currently declining global trends in terrestrial biodiversity, increases in both the extent of land under conservation management and the sustainability of the global food system from farm to fork are required.

An efficient mathematical tool for the design and scaling up of protein chromatography is suggested, in which the model parameters can be determined quickly over a wide operating space without large material investments. The design method is based on mathematical modelling of column dynamics and moment analysis. The accuracy of the dynamic models that are most frequently used for simulations of chromatographic processes is analyzed, and possible errors that can be generated using the moment analysis are indicated. The so-called transport dispersive model was eventually employed for the process simulations. The model was modified to account for the protein dispersion in void volumes of chromatographic systems. The manner of the model calibration was suggested, which was based on a few chromatographic runs and verified over a wide space of the operating parameters, including composition and flow rate of the mobile phase, column dimensions, residence time, and mass loading. The model system for the study was ion-exchange chromatography. The analysis was performed based on the elution profiles of basic fibroblast growth factor 2 and lysozyme, on two different IEX media.

There are currently no spatially explicit, openly accessible data available on forest certification below national level, so understanding the drivers of certification in the past, examining the scope for further certification and using this information for development of future sustainable forest management strategies is challenging. Hence, this paper presents a methodology for the development of a global map of certified forest areas at 1 km resolution in order to satisfy this information need. Validation of the map with certified areas in Russia showed reasonable results, but the lack of openly accessible data requires broadening the strategy for improving the global certification map in the future. Thus, the second aim of the paper is to present an online tool for visualization and interactive improvement of the global forest certification product through collaborative mapping, aiming at a range of stakeholders including third-party certifiers, green NGOs, forestry organizations, decision-makers, scientists and local experts. Such an approach can help to make more accurate information on forest certification available, promote the sharing of data and encourage more transparent and sustainable forest management, i.e. both producers and users can benefit from this online tool.

BACKGROUND Evaluation of optimized upstream conditions is often solely based on titers that do not reflect the downstream process and, consequently, the final purity and yield. This is especially critical for proteins expressed as inclusion bodies (IBs) because the subsequent downstream process is more complex than for soluble proteins. A miniaturized process development platform representing the entire downstream process at a microscale level combined with a multi-fermenter system for bioprocess screening enables fast investigation, using quality by design (QbD) measures, of the quality of feedstock compositions that are altered by upstream conditions. RESULTS We integrated high-throughput methods for an entire process chain, including upstream and downstream processing, for production of a recombinant protein as IBs. The miniaturized process chain consisted of cell disruption, IB harvesting, solubilization, refolding, and chromatographic purification. This enables processing and evaluation of 16 different fermentation conditions within 7 days. Only 2 g of initial biomass was required for evaluation of an individual fermentation. This resulted in a 15-fold reduction of time and a 100-fold reduction of material compared with common bench scale experiments. CONCLUSION This microscale process chain mimics the bench process and is able to differentiate purity variations as low as 2%.

Cell disruption is crucial during recovery of biopharmaceuticals overexpressed in E. coli , which tend to be produced intracellularly as insoluble inclusion bodies. Miniaturized high-throughput systems can accelerate the laborious downstream protocol for such biopharmaceuticals and enable integrated process-development. A fast and robust cell disruption method reflecting the protein and impurity profile of homogenates obtained by large-scale methods is required for such an approach. We established a miniaturized bead mill for parallel mechanical cell disruption at the microscale. Its total protein and impurity release, protein pattern, and particle size distribution were compared to results from microscale enzymatic digestion and referred to laboratory-scale high-pressure homogenization. Bead mill disruption led to equivalent protein and impurity release as well as to the same particle size profile as the large-scale reference. In contrast, lysates obtained by enzymatic digestion contained only 30–47% of overall protein, 17% of dsDNA, and 7–10% of endotoxin compared to those obtained by high-pressure homogenization; also larger debris was present in lysates after enzymatic digestion. The established method is fast, efficient, robust and comparable to current large-scale standards, allowing for parallelization of experiments. Thus, it is the method of choice for rapid integrated process development at the microscale.

A model-based approach for scaling up chromatographic capture step was developed. The purification of human basic fibroblast growth factor protein 2 (FGF2) from an E. coli homogenate on a cation exchange resin was selected as a case study. Non-ideal effects accompanying the capture operation were examined, including: reduction in the protein diffusivity in the presence of the homogenate, competitive adsorption between FGF2 and undefined impurities, and flow behavior in external column volumes. The viscosity of the homogenate was measured as a function of dilution degree and shear stress, and its contribution to the diffusivity reduction was quantified. A dynamic model was formulated which accounted for underlying kinetic and thermodynamic dependencies. The model parameters were determined for a lab scale system using a small 2-mL column. The model was successfully used to scale up the capture operation from the lab scale column to a preparative bench scale column of about 1 L volume.

The land area covered by freely available very high-resolution (VHR) imagery has grown dramatically over recent years, which has considerable relevance for forest observation and monitoring. For example, it is possible to recognize and extract a number of features related to forest type, forest management, degradation and disturbance using VHR imagery. Moreover, time series of medium-to-high-resolution imagery such as MODIS, Landsat or Sentinel has allowed for monitoring of parameters related to forest cover change. Although automatic classification is used regularly to monitor forests using medium-resolution imagery, VHR imagery and changes in web-based technology have opened up new possibilities for the role of visual interpretation in forest observation. Visual interpretation of VHR is typically employed to provide training and/or validation data for other remote sensing-based techniques or to derive statistics directly on forest cover/forest cover change over large regions. Hence, this paper reviews the state of the art in tools designed for visual interpretation of VHR, including Geo-Wiki, LACO-Wiki and Collect Earth as well as issues related to interpretation of VHR imagery and approaches to quality assurance. We have also listed a number of success stories where visual interpretation plays a crucial role, including a global forest mask harmonized with FAO FRA country statistics; estimation of dryland forest area; quantification of deforestation; national reporting to the UNFCCC; and drivers of forest change. ISSN:0169-3298 ISSN:1573-0956

Pre-packed small scale chromatography columns are increasingly used for process development, for determination of design space in bioprocess development, and for post-licence process verifications. The packing quality of 30,000 pre-packed columns delivered to customers over a period 10 years has been analyzed by advanced statistical tools. First, the data were extracted and checked for inconsistencies, and then were tabulated and made ready for statistical processing using the programming language Perl (https://www.perl.org/) and the statistical computing environment R (https://www.r-project.org/). Reduced HETP and asymmetry were plotted over time to obtain a trend of packing quality over 10 years. The obtained data were used as a visualized coefficient of variation analysis (VCVA), a process that has often been applied in other industries such as semiconductor manufacturing. A typical fluctuation of reduced HETP was seen. A Tsunami effect in manufacturing, the effect of propagation of manufacturing deviations leading to out-of-specification products, was not observed with these pre-packed columns. Principal component analysis (PCA) showed that all packing materials cluster. Our data analysis showed that the current commercially available chromatography media used for biopharmaceutical manufacturing can be reproducibly and uniformly packed in polymer-based chromatography columns, which are designed for ready-to-use purposes. Although the number of packed columns has quadrupled over one decade the packing quality has remained stable.

Power input and local energy dissipation are crucial parameters for the engineering characterization of mixing and fluid dynamics at the microscale. Since hydrodynamic stress is solely dependent on the maximum power input, we adapted the clay/polymer method to obtain flock destruction kinetics in six-, 24-, and 96-well microtiter plates on orbital shakers. We also determined the specific power input using calorimetry and found that the power input is at the same order of magnitude for the six- and 96-well plates and the laboratory-scale stirred tank reactor, with 40 to 90 W/m3 (Re' = 180 to 440), 40 to 140 W/m3 (Re' = 320 to 640), and 30 to 50 W/m3 (Re = 4000 to 8500), respectively. All of these values are significantly below 450 to 2100 W/m3 determined for the pilot-scale reactor. The hydrodynamic stress differs significantly between the different formats of MTPs, as the 96-well plates showed very low shear stress on the shaker with a shaking amplitude of 3 mm. Thus, the transfer of mixing conditions from the microtiter plate to small-scale and pilot-scale reactors must be undertaken with care. Our findings, especially the power input determined by the calorimetric method, show that the hydrodynamic conditions in laboratory- and pilot-scale reactors cannot be reached.

Knowledge of the spatial distribution of agricultural abandonment following the collapse of the Soviet Union is highly uncertain. To help improve this situation, we have developed a new map of arable and abandoned land for 2010 at a 10 arc-second resolution. We have fused together existing land cover and land use maps at different temporal and spatial scales for the former Soviet Union (fSU) using a training data set collected from visual interpretation of very high resolution (VHR) imagery. We have also collected an independent validation data set to assess the map accuracy. The overall accuracies of the map by region and country, i.e. Caucasus, Belarus, Kazakhstan, Republic of Moldova, Russian Federation and Ukraine, are 90±2%, 84±2%, 92±1%, 78±3%, 95±1%, 83±2%, respectively. This new product can be used for numerous applications including the modelling of biogeochemical cycles, land-use modelling, the assessment of trade-offs between ecosystem services and land-use potentials (e.g., agricultural production), among others.

Escherichia coli, Saccharomyces cerevisiae, and Pichia pastoris are the standard platforms for biopharmaceutical production with 40% of all between 2010 to 2014 approved protein drugs produced in those microbial hosts. Typically, products overexpressed E. coli and S. cerevisiae remain in the cytosol or are secreted into the periplasm. Consequently, efficient cell disruption is essential for high product recovery during microbial production. Process development platforms at microscale are essential to shorten time to market. While high-pressure homogenization is the industry standard for cell disruption at large scale this method is not practicable for experiments in microscale. This review describes microscale methods for cell disruption at scales as low as 200 µL. Strategies for automation, parallelization and miniaturization, as well as comparability of the results at this scale to high pressure homogenization are considered as those criteria decide which methods are most suited for scale down. Those aspects are discussed in detail for protein overexpression in E. coli and yeast but also the relevance for alternative products and host such as microalgae are taken into account. The authors conclude that bead milling is the best comparable microscale method to large scale high-pressure homogenization and therefore the most suitable technique for automated process development of microbial hosts with the exception of pDNA production.

Regulatory recommendations for quality by design instead of quality by testing raise increasing interest in new sensor technologies. An online monitoring system for downstream processes is developed, which is based on an array of online detectors. Besides standard detectors (UV, pH, and conductivity), our chromatographic workstation is equipped with a fluorescence and a mid-infrared spectrometer, a light scattering, and a refractive index detector. The combination of these sensors enables the prediction of specific protein concentration and various purity attributes, such as high molecular weight impurities, DNA and host cell protein content during the elution phase of a chromatographic antibody capture process. Prediction models solely based on online signals are set up providing real-time predictions. No mechanistic models or information about the chromatographic runs is used. These predictions allow online pooling decisions replacing time- and labor-intensive laboratory measurements. Different process variations, such as changes in the column load or elution buffer, are introduced to test the predictive power of the models. Extrapolation of the models worked well when the column load is changed, whereas model adjustment is necessary when the elution conditions are changed considerably.

Citizens are increasingly becoming involved in data collection, whether for scientific purposes, to carry out micro-tasks, or as part of a gamified, competitive application. In some cases, volunteered data collection overlaps with that of mapping agencies, e.g., the citizen-based mapping of features in OpenStreetMap. LUCAS (Land Use Cover Area frame Sample) is one source of authoritative in-situ data that are collected every three years across EU member countries by trained personnel at a considerable cost to taxpayers. This paper presents a mobile application called FotoQuest Austria, which involves citizens in the crowdsourcing of in-situ land cover and land use data, including at locations of LUCAS sample points in Austria. The results from a campaign run during the summer of 2015 suggest that land cover and land use can be crowdsourced using a simple protocol based on LUCAS. This has implications for remote sensing as this data stream represents a new source of potentially valuable information for the training and validation of land cover maps as well as for area estimation purposes. Although the most detailed and challenging classes were more difficult for untrained citizens to recognize, the agreement between the crowdsourced data and the LUCAS data for basic high level land cover and land use classes in homogeneous areas (ca. 80%) shows clear potential. Recommendations for how to further improve the quality of the crowdsourced data in the context of LUCAS are provided so that this source of data might one day be accurate enough for land cover mapping purposes.

A number of global and regional maps of forest extent are available, but when compared spatially, there are large areas of disagreement. Moreover, there is currently no global forest map that is consistent with forest statistics from FAO (Food and Agriculture Organization of the United Nations). By combining these diverse data sources into a single forest cover product, it is possible to produce a global forest map that is more accurate than the individual input layers and to produce a map that is consistent with FAO statistics. In this paper we applied geographically weighted regression (GWR) to integrate eight different forest products into three global hybrid forest cover maps at a 1 km resolution for the reference year 2000. Input products included global land cover and forest maps at varying resolutions from 30 m to 1 km, mosaics of regional land use/land cover products where available, and the MODIS Vegetation Continuous Fields product. The GWR was trained using crowdsourced data collected via the Geo-Wiki platform and the hybrid maps were then validated using an independent dataset collected via the same system. Three different hybrid maps were produced: two consistent with FAO statistics, one at the country and one at the regional level, and a “best guess” forest cover map that is independent of FAO. Independent validation showed that the “best guess” hybrid product had the best overall accuracy of 93% when compared with the individual input datasets. The global hybrid forest cover maps are available at http://biomass.geo-wiki.org .