Automated Classification of Bioprocess Based on Optimum Compromise Whitening and Clustering

The proposed methodology of technological state classification is based on data smoothing, dimensionality reduction, compromise whitening, and optimum clustering. The novelty of our approach is in the stabile state hypothesis which improves initialization of c-mean algorithm and enables interleaved cross-validation strategy. We also employ the Akaike information criterion to obtain the optimum number of technological states that minimize it, but using as many as possible clusters and components. The general approach is applied to state classification of Pseudomonas putida fed-batch cultivation on octanoic acid

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Adaptive Control of Saccharomyces cerevisiae Yeasts Fed-Batch Cultivations

In this paper, the application of an adaptive algorithm for control of fed-batch bioprocess capable of coping with time-variant process properties in the presence of uncertainties is introduced. The proposed adaptive controller uses Maršík’s heuristic algorithm for adaptation based on control error oscillation rate criterion without the need of a mathematical model of the controlled process or any special test signals. The intended application of the resulting controller was off-gas CO2 concentration control in fed-batch yeast cultivations where the set point has the form of a time-varying concentration profile. The controller has been tested in a series of experimental fed-batch cultivations with D7 Saccharomyces cerevisiae strain, a UV mutant suitable for ergosterol production, in 7‑litre laboratory bioreactor. Obtained results demonstrate good properties of this adaptive controller that can be used without the need for a tedious parameter identification of the complex bioprocess

Data Acquisition Applications

Data acquisition systems have numerous applications. This book has a total of 13 chapters and is divided into three sections: Industrial applications, Medical applications and Scientific experiments. The chapters are written by experts from around the world, while the targeted audience for this book includes professionals who are designers or researchers in the field of data acquisition systems. Faculty members and graduate students could also benefit from the book

DBFZ Jahrestagung 2020 - Bioenergie zwischen Klimapaket und Bioökonomiestrategie: 16./17. September 2020, Leipzig: DBFZ

Im vorliegenden Tagungsreader präsentieren wir Ihnen die für die DBFZ-Jahrestagung 2020 eingereichten Abstracts und Folien zum Nachlesen, einige Livemitschnitte der Vorträge finden Sie unter www.bioenergiekonferenz.de

Proteomics-based identification of potential protein biomarkers for epithelial ovarian cancer

Ph.DDOCTOR OF PHILOSOPH

Unstructured proteins of the malaria parasite Plasmodium falciparum as vaccine candidates

Malaria vaccine research has been battling with persistent challenges, including polymorphisms of vaccine antigens, difficulties with production processes, and limited immune protection against the disease. Intrinsically unstructured proteins (IUPs) are a fairly newly classified group of proteins that have no stable 3D structure and are generally heat-resistant. They usually contain low complexity regions and repetitive sequences, both of which are distinct characteristics of the malaria proteome. Surprisingly, some of the vaccine candidates that have been extensively studied were later reported to have unstructured regions, some of which serve as targets of protective immunity. In keeping with their interesting immunological profiles and their unique properties, which are exceptionally beneficial for vaccine production, malarial IUP antigens may be good vaccine candidates. This PhD project has the following aims:- 1) to develop a synthetic unstructured protein antigen based on the Block 2 region of MSP-1, named the MSP-1 hybrid 2) to characterize a novel vaccine antigen derived from the MSP-3.3 protein, namely an IUP region of PF10_0347 gene product, for its potential as a vaccine candidate 3) to develop a second-generation vaccine by combining the MSP-1 hybrid, with two allelic variants of MSP-2, to overcome antigenic polymorphism and strain-specific immune responses 4) to validate protocols for IUP identification from proteins extracted from the malaria parasite. This study showed that 1) MSP-1 hybrid production was scalable, yielding high protein yields with comparable immunological properties to small-scale production. MSP-1 hybrid was shown to be compatible with different adjuvants, and elicited specific antibodies covering the whole range of Block 2 allelic diversities. 2) A novel antigen, MSP-3.3C, an IUP based on the 3’ region of the PF10_0347 gene, was cloned, expressed and purified. Anti-MSP3.3C antibodies showed very strong parasite growth inhibitory effects in vitro. 3) The MSP-multihybrid antigen was expressed using simple techniques, but only at low levels. It contains epitopes from all three parasite antigen components, and is recognized by specific naturally acquired antibodies. 4) an unconventional 2D gel technique was tested as a method of malaria parasite IUP identification. Plans for further validation of this technique were discussed

Znalostni ridici systemy a jejich vyuziti pri rizeni biotechnologickych procesu.

The theoretical section of the work deals with the general subject of automated bioprocess control. The initial overview of industrially common bioprocess control technologies is followed by a more detailed review and analysis of modern advanced approaches applicable for bioprocess control, here the main attention is paid to the application of knowledge-based control. As the application part of this work is focused on the control of the fed-batch cultivation, the task of fed-batch bioprocess control is examined more throughly. The application section contains a very detailed description of the knowledge-based bioprocess control system BIOGENES for an automated control of the Saccharomyces cerevisiae fed-batch cultivation process. Major emphasis is put here on the characterisation of the supervisory knowledge-based level of BIOGENES, which performs the key tasks of on-line qualitative process data and state classification (metabolic state, process phase, etc.) as well as supervisory process control (setpoint and control strategy selection). The final part of the work is constituted by the experimental section, describing results achieved by the application of the BIOGENES system in a process control of the series of 7 experimental fed-batch cultivations of the microorganism Saccharomyces cerevisiae on a laboratory cultivation unit.Available from STL, Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi