Enzyme recovery from biological wastewater treatment

Enzymes are high value industrial bio-catalysts with extensive applications in a wide range of manufacturing and processing sectors. The catalytic efficiency of enzymes can be several orders higher compared to inorganic chemical catalysts under mild conditions. However, the nutrient medium necessary for biomass culture represents a significant cost to industrial enzyme production. Activated sludge (AS) is a waste product of biological wastewater treatment and consists of microbial biomass that degrades organic matter by producing substantial quantities of hydrolytic enzymes. Therefore, enzyme recovery from AS offers an alternative, potentially viable approach to industrial enzyme production. Enzyme extraction from disrupted AS flocs is technically feasible and has been demonstrated at experimental scale. However, no consistent, optimal approach is available for the enzyme extraction from AS and the production of bio-enzymes from this biomass source can be affected by a range of factors, such as the operational extraction parameters. Moreover, free enzymes in the crude extract exhibit poor storage and operational stability, and are readily inactivated and difficult to recycle and reuse, which limits their large-scale commercial applications. The aim of this study is to develop a robust technology for enzyme recovery from AS and to explore the potential applications of recovered enzymes. A protocol for harvesting crude enzyme extracts from AS, by using sonication treatment to disrupt AS flocs, was set up; the impact of the sonication operational parameters and sludge sampling location on the enzyme extraction efficiency was investigated. A carrier-free, immobilised enzyme product, cross-linked enzyme aggregates (CLEA), was produced from the crude AS enzyme extract for the first time; the CLEA technique essentially combines purification and stabilisation of crude AS enzyme in a single step, and avoids introducing a large amount of inert carrier into the enzyme product. The AS CLEA contained a variety of hydrolytic enzymes and demonstrated high potential to be used for bioconversion of complex organic substrates.Open Acces

Statistical Models to Predict Popularity of News Articles on Social Networks

Social networks have changed the way that we obtain information. Content creators and, specifically news article authors, have in interest in predicting the popularity of content, in terms of the number of shares, likes, and comments across various social media platforms. In this thesis, I employ several statistical learning methods for prediction. Both regression-based and classification-based methods are compared according to their predictive ability, using a database from the UCI Machine Learning Repository

Learning Adaptable Risk-Sensitive Policies to Coordinate in Multi-Agent General-Sum Games

In general-sum games, the interaction of self-interested learning agents commonly leads to socially worse outcomes, such as defect-defect in the iterated stag hunt (ISH). Previous works address this challenge by sharing rewards or shaping their opponents' learning process, which require too strong assumptions. In this paper, we demonstrate that agents trained to optimize expected returns are more likely to choose a safe action that leads to guaranteed but lower rewards. However, there typically exists a risky action that leads to higher rewards in the long run only if agents cooperate, e.g., cooperate-cooperate in ISH. To overcome this, we propose using action value distribution to characterize the decision's risk and corresponding potential payoffs. Specifically, we present Adaptable Risk-Sensitive Policy (ARSP). ARSP learns the distributions over agent's return and estimates a dynamic risk-seeking bonus to discover risky coordination strategies. Furthermore, to avoid overfitting training opponents, ARSP learns an auxiliary opponent modeling task to infer opponents' types and dynamically alter corresponding strategies during execution. Empirically, agents trained via ARSP can achieve stable coordination during training without accessing opponent's rewards or learning process, and can adapt to non-cooperative opponents during execution. To the best of our knowledge, it is the first method to learn coordination strategies between agents both in iterated prisoner's dilemma (IPD) and iterated stag hunt (ISH) without shaping opponents or rewards, and can adapt to opponents with distinct strategies during execution. Furthermore, we show that ARSP can be scaled to high-dimensional settings.Comment: arXiv admin note: substantial text overlap with arXiv:2205.1585

Analysing Online Platform Users’ Attitudes Toward Internet of Things

Internet of Things (IoT) is an increasingly important technology. Understanding the attitudes toward IoT may provide insights into the future development and management of IoT and the management of online platforms. In this paper, we examine online platform users’ attitudes toward IoT, by analysing Twitter data. We analyse the backgrounds of Twitter users associated with different attitudes, including the frequency of using Twitter and the geographical location of posts (i.e., called “tweets”). The research findings suggest that most tweets reflect positive attitudes toward IoT and concentrate on information technologies. Some users expressed concerns with security and privacy issues. Most Twitter users surveyed come from coastal areas of the USA

Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning

Decision support is a probabilistic and quantitative method designed for modeling problems in situations with ambiguity. Computer technology can be employed to provide clinical decision support and treatment recommendations. The problem of natural language applications is that they lack formality and the interpretation is not consistent. Conversely, ontologies can capture the intended meaning and specify modeling primitives. Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision support system (DSS). The proposed DSS uses Case-Based Reasoning (CBR) to consider disease manifestations and provides physicians with treatment solutions from similar previous cases for reference. The proposed DSS supports natural language processing (NLP) queries. The DSS obtained 84.63% accuracy in disease classification with the help of the ontology

Mechanisms of DNA Sensing on Graphene Oxide

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Analytical Chemistry copyright © American Chemical Society after peer review and technical editing by publisher. To access the final edited and published work see Liu, B., Sun, Z., Zhang, X., & Liu, J. (2013). Mechanisms of DNA Sensing on Graphene Oxide. Analytical Chemistry, 85(16), 7987–7993. https://doi.org/10.1021/ac401845pAdsorption of a fluorophore-labeled DNA probe by graphene oxide (GO) produces a sensor that gives fluorescence enhancement in the presence of its complementary DNA (cDNA). While many important analytical applications have been demonstrated, it remains unclear how DNA hybridization takes place in the presence of GO, hindering further rational improvement of sensor design. For the first time, we report a set of experimental evidence to reveal a new mechanism involving nonspecific probe displacement followed by hybridization in the solution phase. In addition, we show quantitatively that only a small portion of the added cDNA molecules undergo hybridization while most are adsorbed by GO to play the displacement role. Therefore, it is possible to improve signaling by raising the hybridization efficiency. A key innovation herein is using probes and cDNA with a significant difference in their adsorption energy by GO. This study offers important mechanistic insights into the GO/DNA system. At the same time, it provides simple experimental methods to study the biomolecular reaction dynamics and mechanism on a surface, which may be applied for many other biosensor systems.University of Waterloo || Canadian Foundation for Innovation || Natural Sciences and Engineering Research Council || Ontario Ministry of Research and Innovation |

Subgroup analysis for the functional linear model

Classical functional linear regression models the relationship between a scalar response and a functional covariate, where the coefficient function is assumed to be identical for all subjects. In this paper, the classical model is extended to allow heterogeneous coefficient functions across different subgroups of subjects. The greatest challenge is that the subgroup structure is usually unknown to us. To this end, we develop a penalization-based approach which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and coefficient functions within each subgroup. An effective computational algorithm is derived. We also establish the oracle properties and estimation consistency. Extensive numerical simulations demonstrate its superiority compared to several competing methods. The analysis of an air quality dataset leads to interesting findings and improved predictions.Comment: 24 pages, 9 figure