Microbial evaluation of full-scale wastewater treatment plants by microscopy survey and chemometric analysis

Book of Abstracts of CEB Annual Meeting 2017[Excerpt] Activated sludge (AS) systems, are constituted by living organisms, mainly bacteria (floc-forming and filamentous), protozoa and metazoa. The later play an important role on grazing bacteria, and are known to be dependent on the working operational parameters (incoming effluent, dissolved oxygen, nitrification, hydraulic and sludge retention times, transient phenomena, etc.) and the system itself (conventional activated system – CAS, oxidation ditch – OD, trickling filter – TF, etc.). Floc-forming bacteria, such as aerobic heterotrophic, autotrophic (nitrifying and sulfur-oxidizing), denitrifying, sulfatereducing and phosphate accumulating bacteria (PAO), are the main organisms responsible for pollution reduction in AS systems. On the other hand, the major role played by filamentous bacteria, rests on the establishment of the microbial aggregates structure, a key feature regarding sludge settling ability. It is known that AS systems are prone to be affected by bulking, foaming, pin point flocs and dispersed growth occurrences, causing poor sludge settling abilities and affecting the wastewater treatment plant (WWTP) performance. In fact, an excess of filamentous bacteria, resulting in filamentous bulking or foaming events, or a shortage, resulting in dispersed growth or pinpoint flocs formation, leads to settling problems in the secondary clarifier. Furthermore, it is possible to establish a close correlation between the predominance of certain protozoa and metazoa taxa, several AS systems operational and settling problems occurrences. [...]info:eu-repo/semantics/publishedVersio

Detection of node group membership in networks with group overlap

Most networks found in social and biochemical systems have modular structures. An important question prompted by the modularity of these networks is whether nodes can be said to belong to a single group. If they cannot, we would need to consider the role of "overlapping communities." Despite some efforts in this direction, the problem of detecting overlapping groups remains unsolved because there is neither a formal definition of overlapping community, nor an ensemble of networks with which to test the performance of group detection algorithms when nodes can belong to more than one group. Here, we introduce an ensemble of networks with overlapping groups. We then apply three group identification methods--modularity maximization, k-clique percolation, and modularity-landscape surveying--to these networks. We find that the modularity-landscape surveying method is the only one able to detect heterogeneities in node memberships, and that those heterogeneities are only detectable when the overlap is small. Surprisingly, we find that the k-clique percolation method is unable to detect node membership for the overlapping case.Comment: 12 pages, 6 figures. To appear in Euro. Phys. J

The Distribution of the Asymptotic Number of Citations to Sets of Publications by a Researcher or From an Academic Department Are Consistent With a Discrete Lognormal Model

How to quantify the impact of a researcher's or an institution's body of work is a matter of increasing importance to scientists, funding agencies, and hiring committees. The use of bibliometric indicators, such as the h-index or the Journal Impact Factor, have become widespread despite their known limitations. We argue that most existing bibliometric indicators are inconsistent, biased, and, worst of all, susceptible to manipulation. Here, we pursue a principled approach to the development of an indicator to quantify the scientific impact of both individual researchers and research institutions grounded on the functional form of the distribution of the asymptotic number of citations. We validate our approach using the publication records of 1,283 researchers from seven scientific and engineering disciplines and the chemistry departments at the 106 U.S. research institutions classified as "very high research activity". Our approach has three distinct advantages. First, it accurately captures the overall scientific impact of researchers at all career stages, as measured by asymptotic citation counts. Second, unlike other measures, our indicator is resistant to manipulation and rewards publication quality over quantity. Third, our approach captures the time-evolution of the scientific impact of research institutions.Comment: 20 pages, 11 figures, 3 table

Modularity from Fluctuations in Random Graphs and Complex Networks

The mechanisms by which modularity emerges in complex networks are not well understood but recent reports have suggested that modularity may arise from evolutionary selection. We show that finding the modularity of a network is analogous to finding the ground-state energy of a spin system. Moreover, we demonstrate that, due to fluctuations, stochastic network models give rise to modular networks. Specifically, we show both numerically and analytically that random graphs and scale-free networks have modularity. We argue that this fact must be taken into consideration to define statistically-significant modularity in complex networks.Comment: 4 page

Robust Patterns in Food Web Structure

We analyze the properties of seven community food webs from a variety of environments--including freshwater, marine-freshwater interfaces and terrestrial environments. We uncover quantitative unifying patterns that describe the properties of the diverse trophic webs considered and suggest that statistical physics concepts such as scaling and universality may be useful in the description of ecosystems. Specifically, we find that several quantities characterizing these diverse food webs obey functional forms that are universal across the different environments considered. The empirical results are in remarkable agreement with the analytical solution of a recently proposed model for food webs.Comment: 4 pages. Final version to appear in PR

Comment on: Kinetic Roughening in Slow Combustion of Paper

We comment on a recent Letter by Maunuksela et al. [Phys. Rev. Lett. 79, 1515 (1997)].Comment: 1 page, 1 figure, http://polymer.bu.edu/~hmakse/Home.htm

Canalizing Kauffman networks: non-ergodicity and its effect on their critical behavior

Boolean Networks have been used to study numerous phenomena, including gene regulation, neural networks, social interactions, and biological evolution. Here, we propose a general method for determining the critical behavior of Boolean systems built from arbitrary ensembles of Boolean functions. In particular, we solve the critical condition for systems of units operating according to canalizing functions and present strong numerical evidence that our approach correctly predicts the phase transition from order to chaos in such systems.Comment: to be published in PR

Quantitative Image Analysis: a monitoring tool in wastewater treatment

Book of Abstracts of CEB Annual Meeting 2017[Excerpt] Computers are key equipment for the analysis of large amounts of data, for tasks requiring complex computation, and for the extraction of quantitative information, opposite to the qualitative evaluation of human analysis. Today, the automatic analysis of numerical images captured by digital cameras enables to rapidly extract quantitative information. Thus, quantitative image analysis (QIA) can be defined in general terms as the extraction of significant information from images, by means of digital image processing and analysis techniques. In the last twenty years, QIA have gained an unquestionable role in several fields of research worldwide and our lab is considered a pioneer research unit on the development of QIA procedures for biological wastewater treatment processes monitoring. Over the years, the number of QIA studies [1, 2] for aggregated (granules and flocs) biomass and filamentous bacteria characterization has been increasing. It should be noticed, though, that some difficulties may be encountered in QIA procedures related to the suitability of the employed microscopy technique, regarding the intended biological process characterization. [...]info:eu-repo/semantics/publishedVersio