Life cycle approach for evaluating sanitation projects - case study: biogas latrine

This paper applies a Life Cycle Assessment framework for the evaluation of water and sanitation projects to a biogas latrine constructed in Uganda. This will be the first time this assessment tool is applied to a sanitation project in the East African Region. While using this tool, one takes into consideration five life stages of a development project and five sustainability factors (socio cultural respect, community participation, political cohesion, economic sustainability, and environmental sustainability). By using this tool during planning, implementation and evaluation of a project, the sustainability of a project can be increased and lessons can be learned and implemented in similar future projects. In this case study the tool was used to evaluate the biogas project and create a starting point to rehabilitate the system

In-silico identification of phenotype-biased functional modules

<p>Abstract</p> <p>Background</p> <p>Phenotypes exhibited by microorganisms can be useful for several purposes, e.g., ethanol as an alternate fuel. Sometimes, the target phenotype maybe required in combination with other phenotypes, in order to be useful, for e.g., an industrial process may require that the organism survive in an anaerobic, alcohol rich environment and be able to feed on both hexose and pentose sugars to produce ethanol. This combination of traits may not be available in any existing organism or if they do exist, the mechanisms involved in the phenotype-expression may not be efficient enough to be useful. Thus, it may be required to genetically modify microorganisms. However, before any genetic modification can take place, it is important to identify the underlying cellular subsystems responsible for the expression of the target phenotype.</p> <p>Results</p> <p>In this paper, we develop a method to identify statistically significant and phenotypically-biased functional modules. The method can compare the organismal network information from hundreds of phenotype expressing and phenotype non-expressing organisms to identify cellular subsystems that are more prone to occur in phenotype-expressing organisms than in phenotype non-expressing organisms. We have provided literature evidence that the phenotype-biased modules identified for phenotypes such as hydrogen production (dark and light fermentation), respiration, gram-positive, gram-negative and motility, are indeed phenotype-related.</p> <p>Conclusion</p> <p>Thus we have proposed a methodology to identify phenotype-biased cellular subsystems. We have shown the effectiveness of our methodology by applying it to several target phenotypes. The code and all supplemental files can be downloaded from (<url>http://freescience.org/cs/phenotype-biased-biclusters/</url>).</p

DENSE: efficient and prior knowledge-driven discovery of phenotype-associated protein functional modules

<p>Abstract</p> <p>Background</p> <p>Identifying cellular subsystems that are involved in the expression of a target phenotype has been a very active research area for the past several years. In this paper, <it>cellular subsystem </it>refers to a group of genes (or proteins) that interact and carry out a common function in the cell. Most studies identify genes associated with a phenotype on the basis of some statistical bias, others have extended these statistical methods to analyze functional modules and biological pathways for phenotype-relatedness. However, a biologist might often have a specific question in mind while performing such analysis and most of the resulting subsystems obtained by the existing methods might be largely irrelevant to the question in hand. Arguably, it would be valuable to incorporate biologist's knowledge about the phenotype into the algorithm. This way, it is anticipated that the resulting subsytems would not only be related to the target phenotype but also contain information that the biologist is likely to be interested in.</p> <p>Results</p> <p>In this paper we introduce a fast and theoretically guranteed method called <it>DENSE </it>(Dense and ENriched Subgraph Enumeration) that can take in as input a biologist's <it>prior </it>knowledge as a set of query proteins and identify all the dense functional modules in a biological network that contain some part of the query vertices. The density (in terms of the number of network egdes) and the enrichment (the number of query proteins in the resulting functional module) can be manipulated via two parameters γ and <it>μ</it>, respectively.</p> <p>Conclusion</p> <p>This algorithm has been applied to the protein functional association network of <it>Clostridium acetobutylicum </it>ATCC 824, a hydrogen producing, acid-tolerant organism. The algorithm was able to verify relationships known to exist in literature and also some previously unknown relationships including those with regulatory and signaling functions. Additionally, we were also able to hypothesize that some uncharacterized proteins are likely associated with the target phenotype. The DENSE code can be downloaded from <url>http://www.freescience.org/cs/DENSE/</url></p

What evidence exists on ecotechnologies for recycling carbon and nutrients from domestic wastewater? a systematic map

Abstract: Background: Eutrophication of the Baltic Sea, and many other water bodies, is partly the result of point-source emissions of nutrients and carbon from wastewater. At the same time, nitrogen and phosphorus planetary boundaries have been breached. There is a need for more efficient resource management, including the recovery and reuse of nutrients and carbon in waste. The aim of this paper is to collate evidence on ecotechnologies intended for use in the wastewater sector globally to facilitate the recovery or reuse of carbon and/or nutrients. Methods: Searches were performed on literature published between 2013 and 2017 and in 5 bibliographic databases, 1 search engine, and 38 specialist websites. Database searches were performed in English. Searches in specialist websites were also performed in Finnish, Polish and Swedish. There was no geographical limitation. Screening was conducted at title and abstract level, and on full texts. Apart from bibliographical information, we extracted information on ecotechnology type, intervention, details of the recovery or reuse, the type of wastewater stream to which the ecotechnology is applied, the study location, type and design. Prior to screening and coding, we conducted consistency checks amongst reviewers. We generated a searchable database of coded studies. Findings were synthesised narratively and visualised in a geographical information system (i.e. an evidence atlas). We identified a series of knowledge gaps and clusters that warrant further research. Results: The search resulted in 4024 records, out of which 413 articles were retained after the screening process. In addition, 35 pre-screened studies from the specialist website searches were added. Together, these 448 articles contained 474 individual studies of 28 types of ecotechnologies. A combination of ecotechnologies (16.7%), followed by microalgae cultivation (14.1%) were the most frequent ecotechnologies in the evidence base. Ecotechnologies for recovery composed 72.6% of the evidence base. The most common wastewater streams for recovery were mixed wastewater and sludge (73.8%). There was a relative lack of studies on recovery from source-separated wastewater. The most common type of recovery was energy (27.3%), followed by simultaneous recovery of nitrogen and phosphorus (22.1%). Reuse of recovered substances was described in 22.8% of the studies. The most common type of reuse was of nitrogen and phosphorus (57.4%), followed by joint reuse of organic carbon, nitrogen and phosphorus (35.2%). Reuse ecotechnologies were mostly focused on the use of wastewater for irrigation or reuse of biosolids, and not on the nutrients that had been extracted through e.g. precipitation of struvite. In 22 studies both recovery and reuse were described. In total, 60 different study countries were reported in the evidence base, and the most common study location was China

NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS

Fundamentals of enviromental engineering/ Mihelcic

xi, 335 hal: ill,tab; 23 cm

Fundamentals of enviromental engineering/ Mihelcic

xi, 335 hal: ill,tab; 23 cm