Article
Ecology
Jeangelis Silva Santos, Adriano Ribeiro de Mendonca, Fabio Guimara Goncalves, Gilson Fernandes da Silva, Andre Quintao de Almeida, Samuel de Padua Chaves e Carvalho, Jeferson Pereira Martins Silva, Rachel Clemente Carvalho, Evandro Ferreira da Silva, Marcelo Otone Aguiar
Summary: This study aimed to use Landsat data to predict and project eucalyptus forest growth and yield, as well as analyze forest growth dynamics. The estimation was performed using artificial neural network (ANN) and random forest (RF) algorithms. The results showed that the proposed methodology can reduce the sampling intensity of traditional continuous forest inventories without significant loss of accuracy, thus achieving cost reduction.
ECOLOGICAL INFORMATICS
(2023)
Article
Chemistry, Physical
Poggio Fraccari Eduardo, Care Damian, Marino Fernando
Summary: Due to the renewed interest in the Water Gas Shift reaction, a large amount of information has been generated. As traditional methods are insufficient to handle this information, a deep learning model is used for exploring and making useful predictions of catalyst performance. This study introduces some novel features, including reducibility, crystal size, and catalyst cost. The Principal Component Analysis shows that the chosen features are not redundant, and the suggested novel features strongly influence the most important components. A Random Forest Regressor is optimized and trained to determine feature importance. An Artificial Neural Network is then employed after Grid Search optimization. The model is trained with different sizes of datasets to evaluate its effect on prediction accuracy.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2023)
Article
Environmental Sciences
Vinicius Luiz Pacheco, Lucimara Bragagnolo, Francisco Dalla Rosa, Antonio Thome
Summary: This study compares the optimization of specific urease activity (SUA) and calcium carbonate (CaCO3) using microbially induced calcite precipitation (MICP) with three machine learning algorithms: random forest regressor, artificial neural networks (ANNs), and multivariate linear regression. The random forest-based algorithm performs the best with improved r(2) values of 0.9381 and 0.9463.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Review
Energy & Fuels
Bin Liang, Jiang Liu, Junyu You, Jin Jia, Yi Pan, Hoonyoung Jeong
Summary: Accurate prediction of hydrocarbon production is crucial for the oil and gas industry, but due to the complexity of underground formations and flow mechanisms, it is difficult to achieve. The emergence of machine learning methodologies offers new opportunities for hydrocarbon production forecasting using production data.
Article
Agriculture, Dairy & Animal Science
Gustavo A. Quintana-Ospina, Maria C. Alfaro-Wisaquillo, Edgar O. Oviedo-Rondon, Juan R. Ruiz-Ramirez, Luis C. Bernal-Arango, Gustavo D. Martinez-Bernal
Summary: This study evaluated the impact of temperature, relative humidity, thermal humidity index, management, and farm-associated factors on the performance of broilers raised under commercial tropical conditions. The results showed that temperature significantly affected the weight, weight gain, feed conversion ratio, and mortality of the broilers. Farm-associated factors also had an impact on the performance of the broilers.
Article
Environmental Sciences
Joanna A. Kaminska, Joanna Kajewska-Szkudlarek
Summary: The polluted air in urban areas poses a significant health risk. Effective modelling of pollutant concentrations and identifying influencing factors can provide advance information for planning and implementing measures to reduce pollution. Two modelling approaches, based on the previous hour's NOx concentration (C&RT models) and meteorological factors, traffic flow, and past NOx and NO2 concentrations (CA models) were described. Three machine learning methods were applied to each approach: artificial neural network (ANN), random forest (RF), and support vector regression (SVR). The best fits were achieved using ANN and RF models (MAPE values of 18.3-18.5%). SVR models had poorer fits (MAPE of 23.4% for C&RT and 29.3% for CA). The choice between the two approaches depends on the intended use of the forecast.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Genetics & Heredity
Yijie He, Lin Cong, Qinfei He, Nianping Feng, Yun Wu
Summary: This study used an expression profile dataset to identify Alzheimer's disease (AD)-related genes through weighted co-expression network and differential expression analysis. An effective AD diagnostic model was established using an artificial neural network.
FRONTIERS IN GENETICS
(2022)
Article
Environmental Studies
Masoud Zolfaghari Nia, Mostafa Moradi, Gholamhosein Moradi, Ruhollah Taghizadeh-Mehrjardi
Summary: Spatial variability of soil properties is critical for soil resource planning, management, and exploitation. Different digital soil mapping models were used to estimate soil physicochemical properties in Maroon riparian forests and agricultural lands. The random forest model provided the best estimation for pH, nitrogen, potassium, and bulk density, while the cubist regression tree was more accurate for organic carbon, C:N ratio, phosphorous, and clay. Artificial neural networks showed the best results for calcium carbonate, sand, and silt contents. Geospatial information such as terrain and climate parameters, as well as satellite images, can be effectively used for soil property mapping. Specific machine learning models should be used for each soil property to ensure highly accurate maps.
Article
Agriculture, Multidisciplinary
Liyuan Zhang, Wenting Han, Yaxiao Niu, Jose L. Chavez, Guomin Shao, Huihui Zhang
Summary: The study showed that UAV vegetation indices and multiple linear regression approach can be effective in estimating maize water status, especially at the field scale. The different responses of canopy structure and chlorophyll concentration to water stress may significantly influence the sensitivity of chlorophyll and structure VIs to water stress.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Chemistry, Applied
Hossein Salami, Matthew A. McDonald, Andreas S. Bommarius, Ronald W. Rousseau, Martha A. Grover
Summary: This study introduces an image-based technique for online detection of trace amounts of undesired solid phases in crystal slurries, which can improve product purity, decrease batch rejection, and increase process performance. By utilizing a convolutional neural network to analyze in situ microscope images, the technique shows greater generality and sensitivity in identifying different crystal classes.
ORGANIC PROCESS RESEARCH & DEVELOPMENT
(2021)
Article
Thermodynamics
Jun Young Kim, Dongjae Kim, Zezhong John Li, Claudio Dariva, Yankai Cao, Naoko Ellis
Summary: Machine learning was used to predict the yield of biomass gasification, and the results showed that Random Forest and Artificial Neural Network had high prediction accuracy. The Monte Carlo filtering method was used to forecast the desired products and identify significant variables for optimization.
Article
Mathematics
Moises Ramos-Martinez, Carlos Alberto Torres-Cantero, Gerardo Ortiz-Torres, Felipe D. J. Sorcia-Vazquez, Himer Avila-George, Ricardo Eliu Lozoya-Ponce, Rodolfo A. Vargas-Mendez, Erasmo M. Renteria-Vargas, Jesse Y. Rumbo-Morales
Summary: This paper introduces a new approach to controlling Pressure Swing Adsorption (PSA) using a neural network controller based on a Model Predictive Control (MPC) process. The approach shows great performance and robustness against disturbances in experimental validation.
Article
Energy & Fuels
Chukwudi Paul Obite, Angela Chukwu, Desmond Chekwube Bartholomew, Ugochinyere Ihuoma Nwosu, Gladys Ezenwanyi Esiaba
Summary: The production and exportation of petroleum has a dominant role in Nigeria's economy, making it heavily reliant on the oil sector. Machine learning models, particularly the ANN model, have been found to outperform traditional statistical methods in modeling crude oil production in Nigeria, indicating the potential for more accurate forecasting and planning in the future.
Article
Chemistry, Physical
Costel Anton, Silvia Curteanu, Catalin Lisa, Florin Leon
Summary: This study aims to evaluate the impact of introducing new auxiliary materials on exhaust emissions during industrial brick manufacturing using machine learning methods. A database of 121 brick batches was built for predictions, with feed-forward neural networks and kNN identified as the best models. An optimization procedure was developed to determine optimal parameters for minimizing gas emissions. The results show that the methodology can help users choose convenient values to reduce emissions with energy potential.
Article
Environmental Sciences
Yuan Xue, Chao Qin, Baosheng Wu, Dan Li, Xudong Fu
Summary: This research proposes an improved random forest algorithm and an automated extraction method for extracting geomorphic information and widths of mountain rivers from satellite images. The results show a high accuracy and effective elimination of disturbances using this method.
Article
Clinical Neurology
Marco Solmi, Andres Estrade, Trevor Thompson, Agorastos Agorastos, Joaquim Radua, Samuele Cortese, Elena Dragioti, Friedrich Leisch, Davy Vancampfort, Lau Caspar Thygesen, Harald Aschauer, Monika Schloegelhofer, Elena Akimova, Andres Schneeberger, Christian G. Huber, Gregor Hasler, Philippe Conus, Kim Q. Do Cuenod, Roland von Kanel, Gonzalo Arrondo, Paolo Fusar-Poli, Philip Gorwood, Pierre-Michel Llorca, Marie-Odile Krebs, Elisabetta Scanferla, Taishiro Kishimoto, Golam Rabbani, Karolina Skonieczna-Zydecka, Paolo Brambilla, Angela Favaro, Akihiro Takamiya, Leonardo Zoccante, Marco Colizzi, Julie Bourgin, Karol Kaminski, Maryam Moghadasin, Soraya Seedat, Evan Matthews, John Wells, Emilia Vassilopoulou, Ary Gadelha, Kuan-Pin Su, Jun Soo Kwon, Minah Kim, Tae Young Lee, Oleg Papsuev, Denisa Mankova, Andrea Boscutti, Cristiano Gerunda, Diego Saccon, Elena Righi, Francesco Monaco, Giovanni Croatto, Guido Cereda, Jacopo Demurtas, Natascia Brondino, Nicola Veronese, Paolo Enrico, Pierluigi Politi, Valentina Ciappolino, Andrea Pfennig, Andreas Bechdolf, Andreas Meyer-Lindenberg, Kai G. Kahl, Katharina Domschke, Michael Bauer, Nikolaos Koutsouleris, Sibylle Winter, Stefan Borgwardt, Istvan Bitter, Judit Balazs, Pal Czobor, Zsolt Unoka, Dimitris Mavridis, Konstantinos Tsamakis, Vasilios P. Bozikas, Chavit Tunvirachaisakul, Michael Maes, Teerayuth Rungnirundorn, Thitiporn Supasitthumrong, Ariful Haque, Andre R. Brunoni, Carlos Gustavo Costardi, Felipe Barreto Schuch, Guilherme Polanczyk, Jhoanne Merlyn Luiz, Lais Fonseca, Luana Aparicio, Samira S. Valvassori, Merete Nordentoft, Per Vendsborg, Sofie Have Hoffmann, Jihed Sehli, Norman Sartorius, Sabina Heuss, Daniel Guinart, Jane Hamilton, John Kane, Jose Rubio, Michael Sand, Ai Koyanagi, Aleix Solanes, Alvaro Andreu-Bernabeu, Antonia San Jose Caceres, Celso Arango, Covadonga M. Diaz-Caneja, Diego Hidalgo-Mazzei, Eduard Vieta, Javier Gonzalez-Penas, Lydia Fortea, Mara Parellada, Miquel A. Fullana, Norma Verdolini, Eva Farkova, Karolina Janku, Mark Millan, Mihaela Honciuc, Anna Moniuszko-Malinowska, Igor Loniewski, Jerzy Samochowiec, Lukasz Kiszkiel, Maria Marlicz, Pawel Sowa, Wojciech Marlicz, Georgina Spies, Brendon Stubbs, Joseph Firth, Sarah Sullivan, Asli Enez Darcin, Hatice Aksu, Nesrin Dilbaz, Onur Noyan, Momoko Kitazawa, Shunya Kurokawa, Yuki Tazawa, Alejandro Anselmi, Cecilia Cracco, Ana Ines Machado, Natalia Estrade, Diego De Leo, Jackie Curtis, Michael Berk, Philip Ward, Scott Teasdale, Simon Rosenbaum, Wolfgang Marx, Adrian Vasile Horodnic, Liviu Oprea, Ovidiu Alexinschi, Petru Ifteni, Serban Turliuc, Tudor Ciuhodaru, Alexandra Bolos, Valentin Matei, Dorien H. Nieman, Iris Sommer, Jim van Os, Therese van Amelsvoort, Ching-Fang Sun, Ta-wei Guu, Can Jiao, Jieting Zhang, Jialin Fan, Liye Zou, Xin Yu, Xinli Chi, Philippe de Timary, Ruud van Winke, Bernardo Ng, Edilberto Pena, Ramon Arellano, Raquel Roman, Thelma Sanchez, Larisa Movina, Pedro Morgado, Sofia Brissos, Oleg Aizberg, Anna Mosina, Damir Krinitski, James Mugisha, Dena Sadeghi-Bahmani, Masoud Sadeghi, Samira Hadi, Serge Brand, Antonia Errazuriz, Nicolas Crossley, Dragana Ignjatovic Ristic, Carlos Lopez-Jaramillo, Dimitris Efthymiou, Praveenlal Kuttichira, Roy Abraham Kallivayalil, Afzal Javed, Muhammad Iqbal Afridi, Bawo James, Omonefe Joy Seb-Akahomen, Jess Fiedorowicz, Andre F. Carvalho, Jeff Daskalakis, Lakshmi N. Yatham, Lin Yang, Tarek Okasha, Aicha Dahdouh, Bjorn Gerdle, Jari Tiihonen, Jae Il Shin, Jinhee Lee, Ahmed Mhalla, Lotfi Gaha, Takoua Brahim, Kuanysh Altynbekov, Nikolay Negay, Saltanat Nurmagambetova, Yasser Abu Jamei, Mark Weiser, Christoph U. Correll
Summary: The COH-FIT project is an international study aiming to investigate the effects of the COVID-19 pandemic on physical and mental health. Through multiple waves of data collection, the project seeks to identify high-risk groups and provide evidence for interventions and policy-making.
JOURNAL OF AFFECTIVE DISORDERS
(2022)
Article
Engineering, Civil
Simona Jokubauskaite, Reinhard Hoessinger, Sergio Jara-Diaz, Stefanie Peer, Alyssa Schneebaum, Basil Schmid, Florian Aschauer, Regine Gerike, Kay W. Axhausen, Friedrich Leisch
Summary: This study presents the estimation of the value of travel time savings (VTTS) by considering the value of liberation time and time assigned to travel. A novel treatment of domestic labor time is introduced to improve the estimation framework. The results show a reduced gender gap in the value of leisure and suggest prioritizing investments in reducing travel time for public transport.
Article
Clinical Neurology
Marco Solmi, Andres Estrade, Trevor Thompson, Agorastos Agorastos, Joaquim Radua, Samuele Cortese, Elena Dragioti, Friedrich Leisch, Davy Vancampfort, Lau Caspar Thygesen, Harald Aschauer, Monika Schloegelhofer, Elena Akimova, Andres Schneeberger, Christian G. Huber, Gregor Hasler, Philippe Conus, Kim Q. Do Cuenod, Roland von Kanel, Gonzalo Arrondo, Paolo Fusar-Poli, Philip Gorwood, Pierre-Michel Llorca, Marie-Odile Krebs, Elisabetta Scanferla, Taishiro Kishimoto, Golam Rabbani, Karolina Skonieczna-Zydecka, Paolo Brambilla, Angela Favaro, Akihiro Takamiya, Leonardo Zoccante, Marco Colizzi, Julie Bourgin, Karol Kaminski, Maryam Moghadasin, Soraya Seedat, Evan Matthews, John Wells, Emilia Vassilopoulou, Ary Gadelha, Kuan-Pin Su, Jun Soo Kwon, Minah Kim, Tae Young Lee, Oleg Papsuev, Denisa Mankova, Andrea Boscutti, Cristiano Gerunda, Diego Saccon, Elena Righi, Francesco Monaco, Giovanni Croatto, Guido Cereda, Jacopo Demurtas, Natascia Brondino, Nicola Veronese, Paolo Enrico, Pierluigi Politi, Valentina Ciappolino, Andrea Pfennig, Andreas Bechdolf, Andreas Meyer-Lindenberg, Kai G. Kahl, Katharina Domschke, Michael Bauer, Nikolaos Koutsouleris, Sibylle Winter, Stefan Borgwardt, Istvan Bitter, Judit Balazs, Pal Czobor, Zsolt Unoka, Dimitris Mavridis, Konstantinos Tsamakis, Vasilios P. Bozikas, Chavit Tunvirachaisakul, Michael Maes, Teerayuth Rungnirundorn, Thitiporn Supasitthumrong, Ariful Haque, Andre R. Brunoni, Carlos Gustavo Costardi, Felipe Barreto Schuch, Guilherme Polanczyk, Jhoanne Merlyn Luiz, Lais Fonseca, Luana Aparicio, Samira S. Valvassori, Merete Nordentoft, Per Vendsborg, Sofie Have Hoffmann, Jihed Sehli, Norman Sartorius, Sabina Heuss, Daniel Guinart, Jane Hamilton, John Kane, Jose Rubio, Michael Sand, Ai Koyanagi, Aleix Solanes, Alvaro Andreu-Bernabeu, Antonia San Jose Caceres, Celso Arango, Covadonga M. Diaz-Caneja, Diego Hidalgo-Mazzei, Eduard Vieta, Javier Gonzalez-Penas, Lydia Fortea, Mara Parellada, Miquel A. Fullana, Norma Verdolini, Eva Farkova, Karolina Janku, Mark Millan, Mihaela Honciuc, Anna Moniuszko-Malinowska, Igor Loniewski, Jerzy Samochowiec, Lukasz Kiszkiel, Maria Marlicz, Pawel Sowa, Wojciech Marlicz, Georgina Spies, Brendon Stubbs, Joseph Firth, Sarah Sullivan, Asli Enez Darcin, Hatice Aksu, Nesrin Dilbaz, Onur Noyan, Momoko Kitazawa, Shunya Kurokawa, Yuki Tazawa, Alejandro Anselmi, Cecilia Cracco, Ana Ines Machado, Natalia Estrade, Diego De Leo, Jackie Curtis, Michael Berk, Philip Ward, Scott Teasdale, Simon Rosenbaum, Wolfgang Marx, Adrian Vasile Horodnic, Liviu Oprea, Ovidiu Alexinschi, Petru Ifteni, Serban Turliuc, Tudor Ciuhodaru, Alexandra Bolos, Valentin Matei, Dorien H. Nieman, Iris Sommer, Jim van Os, Therese van Amelsvoort, Ching-Fang Sun, Ta-Wei Guu, Can Jiao, Jieting Zhang, Jialin Fan, Liye Zou, Xin Yu, Xinli Chi, Philippe de Timary, Ruud van Winke, Bernardo Ng, Edilberto Pena, Ramon Arellano, Raquel Roman, Thelma Sanchez, Larisa Movina, Pedro Morgado, Sofia Brissos, Oleg Aizberg, Anna Mosina, Damir Krinitski, James Mugisha, Dena Sadeghi-Bahmani, Masoud Sadeghi, Samira Hadi, Serge Brand, Antonia Errazuriz, Nicolas Crossley, Dragana Ignjatovic Ristic, Carlos Lopez-Jaramillo, Dimitris Efthymiou, Praveenlal Kuttichira, Roy Abraham Kallivayalil, Afzal Javed, Muhammad Iqbal Afridi, Bawo James, Omonefe Joy Seb-Akahomen, Jess Fiedorowicz, Andre F. Carvalho, Jeff Daskalakis, Lakshmi N. Yatham, Lin Yang, Tarek Okasha, Aicha Dahdouh, Bjorn Gerdle, Jari Tiihonen, Jae Il Shin, Jinhee Lee, Ahmed Mhalla, Lotfi Gaha, Takoua Brahim, Kuanysh Altynbekov, Nikolay Negay, Saltanat Nurmagambetova, Yasser Abu Jamei, Mark Weiser, Christoph U. Correll
Summary: The COH-FIT project aims to measure the impact of the COVID-19 pandemic on the mental health and quality of life of children and families, generating international data to inform interventions and policy-making. Through assessing multiple factors, the project provides a comprehensive understanding to guide future responses to global health crises.
JOURNAL OF AFFECTIVE DISORDERS
(2022)
Article
Statistics & Probability
Muhammad Atif, Muhammad Shafiq, Friedrich Leisch
Summary: This paper presents a comprehensive study of algorithms related to tracing the evolution of clusters over time in cumulative datasets. The importance of clustering in dynamic data streams and the monitoring of clustering solutions over time are highlighted. The implementation of the MONIC algorithm in R-software is used to demonstrate the applications of monitoring changes in clustering solutions with real-life datasets.
JOURNAL OF APPLIED STATISTICS
(2023)
Article
Chemistry, Analytical
Qihao Jiang, Sohan Seth, Theresa Scharl, Tim Schroeder, Alois Jungbauer, Simone Dimartino
Summary: The study proposes the use of machine learning as a complementary tool for predicting column performance. By applying the extreme gradient boosting algorithm to a large dataset, the machine learning model accurately predicts the packing quality of pre-packed columns, with backbone and functional mode having the strongest influence. The results demonstrate the potential of machine learning in guiding column optimization efforts.
JOURNAL OF SEPARATION SCIENCE
(2022)
Article
Environmental Sciences
Ramona M. Cech, Suzanne Jovanovic, Susan Kegley, Koen Hertoge, Friedrich Leisch, Johann G. Zaller
Summary: The use of herbicides in Austria has decreased, but potential toxic risks to non-target organisms and humans still exist. The shift towards more acutely toxic and persistent active ingredients has led to increased toxic loads on honeybees, earthworms, and birds.
ENVIRONMENTAL SCIENCES EUROPE
(2022)
Article
Biochemistry & Molecular Biology
Christoph Koeppl, Nico Lingg, Andreas Fischer, Christina Kroess, Julian Loibl, Wolfgang Buchinger, Rainer Schneider, Alois Jungbauer, Gerald Striedner, Monika Cserjan-Puschmann
Summary: Fusion protein technologies are widely used in biopharmaceutical applications, but they may have negative consequences such as reduced product yield and unwanted changes in expression levels. In this study, we evaluated different N-terminal fusion tag combinations to find an ideal design for a generic fusion tag. The CASPON-tag, a combination of a negatively charged peptide tag, affinity tags, and a caspase-2 cleavage site, showed good performance in enhancing soluble expression and could be efficiently removed.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Food Science & Technology
Natalia Danielewicz, Wenyue Dai, Francesca Rosato, Michael E. Webb, Gerald Striedner, Winfried Roemer, W. Bruce Turnbull, Juergen Mairhofer
Summary: Non-toxic derivatives of the cholera toxin have wide applications in neuroscience as neuronal tracers, vaccine components, and drug-delivery vehicles. However, their production is often inconsistent and requires extensive fine-tuning. In this study, the researchers expanded the molecular toolbox of the Escherichia coli expression system to improve the production of a non-toxic derivative of the cholera toxin. They achieved an 11-fold increase in production capability and successfully validated the biological activity of the derivative.
Article
Agronomy
Ramona Cech, Friedrich Leisch, Johann G. Zaller
Summary: The production of synthetic pesticides in agriculture is often overlooked when it comes to greenhouse gas emissions. However, a study analyzing pesticide sales data in Austria from 2000 to 2019 found that pesticide use and associated greenhouse gas emissions have been increasing, especially in pesticide-intensive crops. In intensive apple production, pesticide-related emissions accounted for 51% of total emissions, while in viticulture it accounted for 37% and in sugar beets it accounted for 12%. This highlights the significant contribution of pesticide production and application to greenhouse gas emissions in the agricultural sector.
Article
Biochemistry & Molecular Biology
Andreas Weber, Martin Gibisch, Daniel Tyrakowski, Monika Cserjan-Puschmann, Jose L. Toca-Herrera, Gerald Striedner
Summary: In this study, the mechanical properties of Escherichia coli (E. coli) cells with different physiological states were investigated using atomic force microscopy. It was found that the peptide-producing cells were softer and had a lower Young's modulus compared to the non-producing cells, and this difference increased over time. This study provides the first evidence that the metabolic burden and carbon limitation significantly influence the physical properties (mechanical properties) of cells in bioreactors.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Engineering, Chemical
Matthias Medl, Vignesh Rajamanickam, Gerald Striedner, Joseph Newton
Summary: Optical density (OD) is a crucial parameter in fermentation, providing valuable information about cell density and process state. However, measuring OD requires sampling of the fermentation broth, which is challenging for high-throughput-microbioreactor systems. A soft sensor based on artificial neural networks was developed to estimate OD in real-time, achieving high accuracy (>95%) even without informative process variables. The study also demonstrated the scaling capabilities of the soft sensor with different strains, contributing to accelerated biopharmaceutical process development.
Article
Biotechnology & Applied Microbiology
Florian Mayer, Benedikt Haslinger, Anton Shpylovyi, Andreas Weber, Ursula Windberger, Bernd Albrecht, Rainer Hahn, Monika Cserjan-Puschmann, Gerald Striedner
Summary: This study investigates the impact of scale-related process heterogeneities on properties of high-cell-density fermentation broths. The findings show that the altered agglomeration tendency of Escherichia coli cells after scale-down cultivations can affect fermentation broth properties relevant for primary recovery. Considering scale-up effects in upstream processing reflected in downstream processing will help optimize the entire bioproduction chain in the future.
JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY
(2023)
Article
Biotechnology & Applied Microbiology
Alexander Jurjevec, Cecile Brocard, Gerald Striedner, Monika Cserjan-Puschmann, Rainer Hahn
Summary: We developed a method to extract recombinant proteins from Escherichia coli (E. coli) cytosol using a polycationic polymer polyethyleneimine (PEI). Compared to high-pressure homogenization, our extraction method provides higher purity of extracts. Addition of PEI to the cells leads to flocculation and gradual diffusion of recombinant proteins out of the PEI/cell network. The chosen PEI molecule (molecular weight and structure) is crucial for protein extraction, and the method can be applied to resuspended cells or fermentation broths at higher PEI concentration, greatly facilitating downstream processing steps.
JOURNAL OF BIOTECHNOLOGY
(2023)