Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach

International audienceBackground: Bacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

Orbiting quarantine facility. The Antaeus report

A mission plan for the Orbiting Quarantine Facility (OQF) is presented. Coverage includes system overview, quarantine and protocol, the laboratory, support systems, cost analysis and possible additional uses of the OQF

Assessment and optimization of environmental systems using data analysis and simulation.

For most environmental systems, specifically wastewater treatment plants and aquifers, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to assess over-arching trends and quantify temporal operational performance. The objective of this research study was to provide an effective means for comprehensive temporal evaluation of environmental systems. The proposed methodology used several multivariate data analyses and statistical techniques to present an assessment framework for the water quality monitoring programs as well as optimization of treatment plants and aquifer systems. The developed procedure considered the combination of statistical and data analysis algorithms including correlation techniques, factor analysis and principal component analysis, and multivariate stepwise regression analysis. Those methodologies were used to develop a series of independent indexes to quantify the composition of wastewater and groundwater. Also, by developing a stepwise data analysis approach, a baseline was introduced to discover the key operational parameters which significantly affect the performance of environmental systems. Moreover, a comprehensive approach was introduced to develop numerical models for forecasting key operational and quality parameters which can be used for future simulation and scenario analysis practices. The developed methodology and frameworks were successfully applied to four case studies which include three wastewater treatment plants and an aquifer system. In the first case study, the aforementioned approach was applied to the Floyds Fork water quality treatment center in Louisville, KY. The objective of this case study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. The study presented a multivariate statistical and data analyses of the wastewater physicochemical parameters to provide a baseline for temporal assessment of the treatment plant. Fifteen quality and quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9% were achieved for the testing dataset. The presented techniques and procedures in this case study provide an assessment framework for the wastewater treatment monitoring programs. The second case study focused on assessing methane production of a novel combined system for treatment of high strength organic wastewater. The studied pilot plant comprised Rotating Biological Contactor (RBC) process under anaerobic condition, in conjunction with Moving Bed Biofilm Reactor (MBBR) as the combining aerobic process. Various operational parameters were tested to maximize the Chemical Oxygen Demand (COD) removal performance and methane gas production from treating high strength synthetic wastewater. The identified optimal parameters included hydraulic retention time, organic loading rate, and disk rotational speed; equal to 5 days, 7 rpm, and 2 kg COD/m3/d, respectively. Under these conditions, the combined system achieved high removal efficiency (98% from influent COD of 10,000 mg/L) with additional benefit of methane production (116.60 L/d from a 46-liter AnRBC reactor). The obtained results from conducting this case study confirmed the effectiveness of integrated hybrid system in achieving both high removal efficiency and methane production. Thus, this system was recommended for treating high strength organic wastewater. The third case study focused on assessing the feasibility of using a contact stabilization process for secondary treatment of refinery wastewater through a step by step analysis. the studied pilot plant comprised contact-stabilization activated sludge process in conjunction with clarification reactor. Various operational parameters were tested to minimize excessive sludge production and maximize system removal performance from treating petroleum refinery wastewater. The mixed liquor dissolved oxygen (DO) and the rate of activated return sludge (RS) were selected as key operational parameters. The results indicated that the system had an optimum performance under applied aeration of 3.7 mg oxygen per liter of mixed liquor and 46% return sludge. This operational combination resulted in COD removal efficiency of 78% with daily biomass production of 1.42 kg/day. Considering the results from this case study, the contact stabilization activated sludge process was suggested as an effective alternative for secondary treatment of wastewater from petroleum refineries. The last case study combined probabilistic and deterministic approaches for assessing aquifer’s water quality. The probabilistic approach used multivariate statistical analysis to classify the groundwater’s physiochemical characteristics. Building upon the obtained results, the deterministic approach used hydrochemistry analyses for a more comprehensive assessment of groundwater suitability for different applications. For this purpose, a large geologic basin, under arid weather conditions, was evaluated. The ultimate objective was to identify: 1) groundwater classification scheme, 2) processes governing the groundwater chemistry, 3) hydrochemical characteristics of groundwater, and 4) suitability of the groundwater for drinking and agricultural purposes. Considering the results from multivariate statistical analysis, chloride salts dissolution was identified within the aquifer. Further application of the deterministic approach revealed degradation of groundwater quality throughout the basin, possibly due to the saltwater intrusion. By developing the water quality index and a multi-hazard risk assessment methodology, the suitability of groundwater for human consumption and irrigation purposes were assessed. The combined consideration of deterministic and probabilistic approaches provided an effective means for comprehensive evaluation of groundwater quality across different aquifers or within one. The presented procedures and methodologies in this research study provide environmental analysts and governmental decision makers with a comprehensive tool to evaluate current and future quality conditions within any given wastewater treatment plants and/or aquifer systems

Aquaculture systems modeling: an introduction with emphasis on warmwater aquaculture

An introduction to modeling is presented. The basic concepts of systems and models and various types of models and their use in research and in management are described. Guidelines for modeling aquaculture systems are presented: empirical models for the analysis of multivariate datasets and theoretical models based on knowledge of the various processes underlying a system. Examples of two modeling approaches to the production of Nile tilapia (Oreochromis niloticus) in ricefields are given in an appendix.Aquaculture systems, Warm-water aquaculture, Modelling

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Efficiency of Hydrogen Peroxide and Fenton Reagent for Polycyclic Aromatic Hydrocarbon Degradation in Contaminated Soil: Insights from Experimental and Predictive Modeling

\ua9 2024 by the authors.This study investigates the degradation kinetics of polycyclic aromatic hydrocarbons (PAHs) in contaminated soil using hydrogen peroxide (H2O2) and the Fenton process (H2O2/Fe2+). The effect of oxidant concentration and the Fenton molar ratio on PAH decomposition efficiency is examined. Results reveal that increasing H2O2 concentration above 25 mmol/samples leads to a slight increase in the rate constants for both first- and second-order reactions. The Fenton process demonstrates higher efficiency in PAH degradation compared to H2O2 alone, achieving decomposition yields ranging from 84.7% to 99.9%. pH evolution during the oxidation process influences PAH degradation, with alkaline conditions favoring lower elimination rates. Fourier-transform infrared (FTIR) spectroscopy analysis indicates significant elimination of PAHs after treatment, with both oxidants showing comparable efficacy in complete hydrocarbon degradation. The mechanisms of PAH degradation by H2O2 and the Fenton process involve hydroxyl radical formation, with the latter exhibiting greater efficiency due to Fe2+ catalysis. Gaussian process regression (GPR) modeling accurately predicts reduced concentration, with optimized ARD-Exponential kernel function demonstrating superior performance. The Improved Grey Wolf Optimizer algorithm facilitates optimization of reaction conditions, yielding a high degree of agreement between experimental and predicted values. A MATLAB 2022b interface is developed for efficient optimization and prediction of C/C0, a critical parameter in PAH degradation studies. This integrated approach offers insights into optimizing the efficiency of oxidant-based PAH remediation techniques, with potential applications in contaminated soil remediation

Reliability analysis for subsea pipeline cathodic protection systems /

Subsea pipelines, as the main transportation means for oil and gas produced offshore, are a key element of the production system. Cathodic protection systems (CPS) are used in combination with surface coatings to protect the pipeline from external corrosion. Although cases of pipeline failure due to external corrosion remain rare, such failures can have catastrophic effects in terms of human lives, environment degradation and financial losses. The offshore industry was led to the use of risk analysis techniques subsequent to major disasters, such as Piper Alpha and Alexander Kjelland. These accidents made the development and use of risk analysis techniques of highly significant interest, and reliability analysis is presently becoming a more important management tool in that field for determining reliability of components such as pipelines, subsea valves and offshore structures. This research is based on an analysis of subsea pipeline cathodic protection systems and on a model of the electrochemical potentials at the pipeline surface. This potential model uses finite element modelling techniques, and integrates probabilistic modules for taking into account uncertainties on input parameters. Uncertainties are used to calculate standard deviations on the potential values. Based on the potentials and potential variances obtained, several parameters characteristic of the cathodic protection system reliability, such as probability of failure and time to failure, are calculated. The model developed proved suitable for simulating any pipeline, under any environmental and operational conditions. It was used as a reliability prediction tool, and to assess the effects of some parameters on the cathodic protection system reliability

Bayesian Network Modeling and Inference in Plant Gene Networks And Analysis of Sequencing and Imaging Data

Scientific and technological advancements over the years have made curing, preventing or managing all diseases, a goal that seems to be within reach. The approach to manipulating biological systems is multifaceted. This dissertation focuses on two problems that pose fundamental challenges in developing methods to control biological systems: the first is to model complex interactions in biological systems; the second is faithful representation and analysis of biological data obtained from scientific equipments. The first part of this dissertation is a discussion on modeling and inference in gene networks, and Bayesian inference. Then we describe the application of Bayesian network modeling to represent interactions among genes, and integrating gene expression data in order to identify potential points of intervention in the gene network. We conclude with a summary of evolving directions for modeling gene interactions. The second topic this dissertation focuses on is taming biological data to obtain actionable insights. We introduce the challenges in representation and analysis of high throughput sequencing data and proceeds to describe the analysis of imaging data in the dynamic environment of cancer cells. Then we discuss tackling the problem of analyzing high throughput RNA sequencing data in order to pinpoint genes that exhibit different behaviors under monitored experimental conditions. Then we address the interesting problem of deciphering and quantifying gene-level activity from epifluorescent imaging data