A Novel Approach for Production of Highly Active Baker`s Yeast from Fodder Yeast, a Byproduct from Ethanol Production Industry

2001 ◽  
Vol 1 (7) ◽  
pp. 614-620 ◽  
Author(s):  
M. Fadel . ◽  
M.S. Foda .
Keyword(s):  
Ethanol Production ◽  
Highly Active ◽  
Novel Approach ◽  
Fodder Yeast ◽  
Production Industry

scholarly journals Economic Effects of Biofuel Production on States and Rural Communities

2006 ◽  
Vol 38 (2) ◽  
pp. 377-387 ◽  
Author(s):  
Joe L. Parcell ◽  
Patrick Westhoff
Keyword(s):  
Rural Communities ◽  
Ethanol Production ◽  
Economic Effects ◽  
Biofuel Production ◽  
Local Taxes ◽  
Current Production ◽  
Corn Prices ◽  
On Farm ◽  
Production Industry ◽  
Incentive Payments

This study summarizes research on farm-, local-, regional-, and macro-level economic effects of ethanol production. Given current production levels, the ethanol production industry annually employees approximately 3,500 workers, pays out nearly $132 million in worker salaries, generates over $110 million in local taxes, and takes in some $2 billion in government incentive payments. Projections for a 60 million gallon per year ethanol plant indicate an annual increase in corn usage of 21 million bushels, a one-time capitalization of $75 million, an increase in local corn prices of between $0.06/bushel and $0.12/bushel, a 54 direct and a 210 indirect jobs created, an increase in local tax revenues of $1.2 million, a decrease in federal commodity program outlays of $30 million, and an increase in ethanol production incentives (federal only) of around $30.5 million.

scholarly journals HPO2GO: prediction of human phenotype ontology term associations for proteins using cross ontology annotation co-occurrences

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5298 ◽  
Author(s):  
Tunca Doğan
Keyword(s):  
Genetic Diseases ◽  
Predictive Performance ◽  
Ontology Term ◽  
Human Phenotype ◽  
Go Annotation ◽  
Functional Changes ◽  
Biological Ontologies ◽  
Highly Active ◽  
Novel Approach ◽  
Quantitative Basis

Analysing the relationships between biomolecules and the genetic diseases is a highly active area of research, where the aim is to identify the genes and their products that cause a particular disease due to functional changes originated from mutations. Biological ontologies are frequently employed in these studies, which provides researchers with extensive opportunities for knowledge discovery through computational data analysis. In this study, a novel approach is proposed for the identification of relationships between biomedical entities by automatically mapping phenotypic abnormality defining HPO terms with biomolecular function defining GO terms, where each association indicates the occurrence of the abnormality due to the loss of the biomolecular function expressed by the corresponding GO term. The proposed HPO2GO mappings were extracted by calculating the frequency of the co-annotations of the terms on the same genes/proteins, using already existing curated HPO and GO annotation sets. This was followed by the filtering of the unreliable mappings that could be observed due to chance, by statistical resampling of the co-occurrence similarity distributions. Furthermore, the biological relevance of the finalized mappings were discussed over selected cases, using the literature. The resulting HPO2GO mappings can be employed in different settings to predict and to analyse novel gene/protein—ontology term—disease relations. As an application of the proposed approach, HPO term—protein associations (i.e., HPO2protein) were predicted. In order to test the predictive performance of the method on a quantitative basis, and to compare it with the state-of-the-art, CAFA2 challenge HPO prediction target protein set was employed. The results of the benchmark indicated the potential of the proposed approach, as HPO2GO performance was among the best (Fmax = 0.35). The automated cross ontology mapping approach developed in this work may be extended to other ontologies as well, to identify unexplored relation patterns at the systemic level. The datasets, results and the source code of HPO2GO are available for download at: https://github.com/cansyl/HPO2GO.

scholarly journals Traditional and new raw materials for spirit beverage production

2021 ◽  
Vol 17 ◽  
pp. 70-78
Author(s):  
Szymon Ługowoj ◽  
Maria Balcerek
Keyword(s):  
Ethanol Production ◽  
Raw Materials ◽  
The Other ◽  
Raw Material ◽  
Fast Growing ◽  
Growth Requirements ◽  
Other Hand ◽  
Materials Used ◽  
Production Industry

The ethanol production industry is a fast growing branch of the economy in many countries, and there is a rich tradition of spirit beverage production of many unique drinks such as Polish vodka and Starka or Irish and Scotch whisk(e)y, all of which have unique organoleptic features. This variety is possible thanks to different raw materials used for production such as rye, barley or corn and potatoes, as well as technological solutions developed over the generations of manufacturing. Rye deserves a closer look due to its low growth requirements and many different uses as well as its long tradition of cultivation, especially in Poland. On the other hand, manufacturers are currently interested in using new, original raw materials for the production of so-called craft alcohols. Buckwheat is an example of a raw material that can be successfully used in the production of original spirits.

Towards a Novel Approach for Enterprise Knowledge Capitalization Utilizing an Ontology and Collaborative Decision-Making

2016 ◽  
Vol 8 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Fatima Zohra Benkaddour ◽  
Noria Taghezout ◽  
Bouabdellah Ascar
Keyword(s):  
Decision Making ◽  
Decision Making Process ◽  
Case Based Reasoning ◽  
Collaborative Decision Making ◽  
Novel Approach ◽  
New Ideas ◽  
Collaborative Platform ◽  
Collaborative Decision ◽  
Production Industry ◽  
Reasoning Engine

In this paper, the authors describe the development of a Decision Support System (DSS) in the spunlace nonwoven production industry. The suggested DSS utilizes domain ontology and a collaborative platform that allows operators to share and exchange experiences in the industrial diagnosis in order to have new ideas and useful information for collaborative decision-making. One of the main aspects addressed in the decision-making process was the knowledge management of the most frequently breakdowns of machines as the card, aquajet etc. This paper introduces the architecture of the system, including several modules such as, Reasoning engine and Similarity module, etc. The decision-making is reinforced by a case-based reasoning to recommend solutions where previously solved cases (problem) are compared to recently encountered ones using the same ontology to define similarity between cases. Some experiments have been conducted in INOTIS enterprise to indicate the efficiency of the proposed system.

scholarly journals A novel approach for using silica nanoparticles in a proppant pack to fixate coal fines

2020 ◽  
Vol 60 (1) ◽  
pp. 88 ◽  
Author(s):  
Faisal Ur Rahman Awan ◽  
Alireza Keshavarz ◽  
Hamed Akhondzadeh ◽  
Sarmad Al-Anssari ◽  
Stefan Iglauer
Keyword(s):  
Silica Nanoparticles ◽  
Active Surface ◽  
High Specific Surface Area ◽  
Gas Reservoirs ◽  
Particle Counter ◽  
Pump Failure ◽  
Highly Active ◽  
Novel Approach ◽  
Mechanical Pump ◽  
Coal Fines

Hydraulic fracturing operations in coal seam gas reservoirs are highly prone to release coal fines. Coal fines inevitably cause mechanical pump failure and permeability damage as a result of their hydrophobicity, aggregation in the system and pore-throat blockage. One approach to affix these coal fines at their source, and to retard generation, is to introduce a nanoparticle-treated proppant pack. Thus, this research explores coal fines retention (known as adsorption) in a proppant pack using nanoparticles. In the study, the electrolytic environment, pH, flow rate, temperature and pressure were kept constant, while the variables were concentration of silica nanoparticles (0–0.1 wt%) and coal fines concentration (0.1–1 wt%). The objective was to identify silica nano-formulations that effectively fixate coal fine dispersions. Subsequently, the coal suspensions flowed through a glass-bead proppant pack treated with and without nanoparticles, and were then analysed via a particle counter. The quantitative results from particle counter analysis showed that the proppant pack with nanoparticle treatment strongly affected the fixation ability of coal fines. The proppant pack without nanoparticle treatment showed up to 30% adsorption and flowed through the proppant untreated, while proppant pack treated with nanoparticles showed up to 74% adsorption; hence, more exceptional affixation ability to the coal fines. Further, the results indicated that the zeta-potential of silica nanoparticles at higher salinity became unstable, i.e. approximately –20 mV; this low value helped the proppant pack treated with nanoparticles to attach coal fines to it. The ability of nanoparticles to adsorb coal fines is due to their highly active surface, and high specific surface area.

scholarly journals Predicting muscle activation patterns from motion and anatomy: modelling the skull of Sphenodon (Diapsida: Rhynchocephalia)

2009 ◽  
Vol 7 (42) ◽  
pp. 153-160 ◽  
Author(s):  
Neil Curtis ◽  
Marc E. H. Jones ◽  
Susan E. Evans ◽  
JunFen Shi ◽  
Paul O'Higgins ◽  
...  
Keyword(s):  
Muscle Activity ◽  
Muscle Activation ◽  
Element Analysis ◽  
Activation Patterns ◽  
Muscle Activation Patterns ◽  
Highly Active ◽  
Novel Approach ◽  
Food Size ◽  
Muscle Groups ◽  
Musculoskeletal Systems

The relationship between skull shape and the forces generated during feeding is currently under widespread scrutiny and increasingly involves the use of computer simulations such as finite element analysis. The computer models used to represent skulls are often based on computed tomography data and thus are structurally accurate; however, correctly representing muscular loading during food reduction remains a major problem. Here, we present a novel approach for predicting the forces and activation patterns of muscles and muscle groups based on their known anatomical orientation (line of action). The work was carried out for the lizard-like reptile Sphenodon (Rhynchocephalia) using a sophisticated computer-based model and multi-body dynamics analysis. The model suggests that specific muscle groups control specific motions, and that during certain times in the bite cycle some muscles are highly active whereas others are inactive. The predictions of muscle activity closely correspond to data previously recorded from live Sphenodon using electromyography. Apparent exceptions can be explained by variations in food resistance, food size, food position and lower jaw motions. This approach shows considerable promise in advancing detailed functional models of food acquisition and reduction, and for use in other musculoskeletal systems where no experimental determination of muscle activity is possible, such as in rare, endangered or extinct species.

scholarly journals HPO2GO: prediction of human phenotype ontology term associations using cross ontology annotation co-occurrences

2018 ◽  
Author(s):  
Tunca Doğan
Keyword(s):  
Genetic Diseases ◽  
Predictive Performance ◽  
Literature Mining ◽  
Ontology Term ◽  
Human Phenotype ◽  
Go Annotation ◽  
Functional Changes ◽  
Biological Ontologies ◽  
Highly Active ◽  
Novel Approach

Analysing the relationships between biomolecules and the genetic diseases is a highly active area of research, where the aim is to identify the genes and their products that cause a particular disease due to functional changes originated from mutations. Biological ontologies are frequently employed in these studies, which provided researchers with extensive opportunities for knowledge discovery through computational data analysis. In this study, a novel approach is proposed for the identification of relationships between biomedical entities by automatically mapping phenotypic abnormality defining HPO terms with biomolecular function defining GO terms, where each association indicates the occurrence of the abnormality due to the loss of the biomolecular function expressed by the corresponding GO term. The proposed HPO2GO mappings were extracted by calculating the frequency of the co-annotations of the terms on the same genes/proteins, using already existing curated HPO and GO annotation sets. This was followed by the filtering of the unreliable mappings that could be observed due to chance, by statistical resampling of the co-occurrence similarity distributions. Furthermore, the biological relevance of the finalized mappings were discussed over selected cases, using the literature. The resulting HPO2GO mappings can be employed in different settings to predict and to analyse novel gene/protein - ontology term - disease relations. As an application of the proposed approach, HPO term - protein associations (i.e., HPO2protein) are predicted. In order to test the predictive performance of the method on a quantitative basis, and to compare it with the state-of-the-art, CAFA2 challenge HPO prediction target protein set was employed. The results of the benchmark indicated the potential of the proposed approach, as HPO2GO beat all models from 38 participating groups (with Fmax=0.402), by a margin of 12.6% compared to the top performer. It is important to note that, HPO2GO was not proposed to replace, but to complement the conventional approaches used in the field of biomedical relation discovery. The automated cross ontology mapping approach developed in this work can easily be extended to other ontologies as well, to identify unexplored relation patterns at the systemic level. The proposed approach will be more effective when combined with powerful techniques such as text/literature mining. The datasets, results and the source code of HPO2GO are available for download at: https://github.com/cansyl/HPO2GO.

A novel approach for the improvement of ethanol fermentation by Saccharomyces cerevisiae

2010 ◽  
Vol 56 (6) ◽  
pp. 495-500 ◽  
Author(s):  
Lihua Hou ◽  
Xiaohong Cao ◽  
Chunling Wang
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Ethanol Production ◽  
Genome Engineering ◽  
Ethanol Conversion ◽  
Mitotic Chromosomes ◽  
Gene Encoding ◽  
Very High Gravity ◽  
Novel Approach ◽  
Very High Gravity Fermentation ◽  
Tetraploid Strain

Fermentation properties under the control of multiple genes are difficult to alter with traditional methods in Saccharomyces cerevisiae . Here, a novel genome engineering approach is developed to improve ethanol production in very high gravity fermentation with 300 g/L glucose as the carbon source. This strategy involved constructing aneuploid strains on the base of tetraploid cells. The tetraploid strain was constructed by using the plasmid YCplac33-GHK, which harbored the HO gene encoding the site-specific Ho endonucleases. The aneuploid strain, WT4-M, was selected and screened after the tetraploid cells were treated with methyl benzimidazole-2-yl-carbamate to induce loss of mitotic chromosomes. It was found that aneuploid strain WT4-M not only exhibited an increase in ethanol production and osmotic and thermal tolerance, but also an improvement in the sugar–ethanol conversion rate. Notably, WT4-M provided up to 9.8% improvement in ethanol production compared with the control strain. The results demonstrated that the strategy of aneuploidy was valuable for creating yeast strains with better fermentation characteristics.

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