Kidney injury and cell therapy: Preclinical study

The aim of this study is to show histological and immunofluorescence analysis of renal parenchyma of agoutis affected by gentamicin‐induced renal disease after the infusion of bone marrow mononuclear cells (BMMC) stained with Hoechst®. Nine agouti's males were divided into three groups: Test group (TG): renal disease by gentamicin induced (n = 3), cell therapy group (CTG): renal disease by gentamicin induced and BMMC infusion (n = 3), and control group (CG): nonrenal disease and BMMC infusion (n = 3). TG and CTG were submitted to the protocol of renal disease induction using weekly application of gentamicin sulfate for 4 months. CG and CTG received a 1 × 108 BMMC stained with Hoechst and were euthanized for kidney examination 21 days after BMMC injection and samples were collected for histology and immunofluorescence analysis. Histological analysis demonstrated typical interstitial lesions in kidney similarly to human disease, as tubular necrosis, glomerular destruction, atrophy tubular, fibrotic areas, and collagen deposition. We conclude that histological analysis suggest a positive application of agouti's as a model for a gentamicin inducing of kidney disease, beyond the immunofluorescence analysis suggest a significant migration of BMMC to sites of renal injury in CTG. Microsc. Res. Tech., 2012. © 2011 Wiley Periodicals, Inc.     

Influence of photoperiod on carbon dioxide and methane emissions from two pilot-scale stabilization ponds

Greenhouse gas (GHG) emissions (CO(2), CH(4)) from pilot-scale algal and duckweed-based ponds (ABP and DBP) were measured using the static chamber methodology. Daylight and nocturnal variations of GHG and wastewater characteristics (e.g. chemical oxygen demand (COD), pH) were determined via sampling campaigns during midday (12:30-15:30) and midnight (00:30-03:30) periods. The results showed that under daylight conditions in ABP median emissions were -232 mg CO(2) m(-2) d(-1) and 9.9 mg CH(4) m(-2) d(-1), and in DBP median emissions were -1,654.5 mg CO(2) m(-2) d(-1) and 71.4 mg CH(4) m(-2) d(-1), respectively. During nocturnal conditions ABP median emissions were 3,949.9 mg CO(2) m(-2) d(-1), 12.7 mg CH(4) m(-2) d(-1), and DBP median emissions were 5,116 mg CO(2) m(-2) d(-1), 195.2 mg CH(4) m(-2) d(-1), respectively. Once data measured during daylight were averaged together with nocturnal data the median emissions for ABP were 1,566.8 mg CO(2) m(-2) d(-1) and 72.1 mg CH(4) m(-2) d(-1), whilst for DBP they were 3,016.9 mg CO(2) m(-2) d(-) and 178.9 mg CH(4) m(-2) d(-1), respectively. These figures suggest that there were significant differences between CO(2) emissions measured during daylight and nocturnal periods (p < 0.05). This shows a sink-like behaviour for both ABP and DBP in the presence of solar light, which indicates the influence of photosynthesis in CO(2) emissions. On the other hand, the fluxes of CH(4) indicated that DBP and ABP behave as net sources of CH(4) during day and night, although higher emissions were observed from DBP. Overall, according to the compound average (daylight and nocturnal emissions) both ABP and DBP systems might be considered as net sources of GHG.     

Exploring different technological platforms for supporting co-located collaborative games in the classroom

Computer Supported Collaborative Learning is a pedagogical approach that can be used for deploying educational games in the classroom. However, there is no clear understanding as to which technological platforms are better suited for deploying co-located collaborative games, nor the general affordances that are required. In this work we explore two different technological platforms for developing collaborative games in the classroom: one based on augmented reality technology and the other based on multiple-mice technology. In both cases, the same game was introduced to teach electrostatics and the results were compared experimentally using a real class.     

A collaborative platform for cognitive decision making in the Knowledge Society

Two key aspects of the Knowledge Society are the interconnection between the actors involved in the decision making processes and the importance of the human factor, particularly the citizen’s continuous learning and education. This paper presents a new module devoted to knowledge extraction and diffusion that has been incorporated into a previously developed decision making tool concerning the Internet and related with the multicriteria selection of a discrete number of alternatives (PRIOR-Web). Quantitative and qualitative procedures using data and text mining methods have been employed in the extraction of knowledge. Graphical visualisation tools have been incorporated in the diffusion stage of the methodological approach suggested when dealing with decision making in the Knowledge Society. The resulting collaborative platform is being used as the methodological support for the cognitive democracy known as e-cognocracy.     

Strictly positive-definite spike train kernels for point-process divergences

Exploratory tools that are sensitive to arbitrary statistical variations in spike train observations open up the possibility of novel neuroscientific discoveries. Developing such tools, however, is difficult due to the lack of Euclidean structure of the spike train space, and an experimenter usually prefers simpler tools that capture only limited statistical features of the spike train, such as mean spike count or mean firing rate. We explore strictly positive-definite kernels on the space of spike trains to offer both a structural representation of this space and a platform for developing statistical measures that explore features beyond count or rate. We apply these kernels to construct measures of divergence between two point processes and use them for hypothesis testing, that is, to observe if two sets of spike trains originate from the same underlying probability law. Although there exist positive-definite spike train kernels in the literature, we establish that these kernels are not strictly definite and thus do not induce measures of divergence. We discuss the properties of both of these existing nonstrict kernels and the novel strict kernels in terms of their computational complexity, choice of free parameters, and performance on both synthetic and real data through kernel principal component analysis and hypothesis testing.     

CONTEXTUAL LANGUAGE MODELS FOR RANKING ANSWERS TO NATURAL LANGUAGE DEFINITION QUESTIONS

Question–answering systems make good use of knowledge bases (KBs, e.g., Wikipedia) for responding to definition queries. Typically, systems extract relevant facts from articles regarding the question across KBs, and then they are projected into the candidate answers. However, studies have shown that the performance of this kind of method suddenly drops, whenever KBs supply narrow coverage. This work describes a new approach to deal with this problem by constructing context models for scoring candidate answers, which are, more precisely, statistical n‐gram language models inferred from lexicalized dependency paths extracted from Wikipedia abstracts. Unlike state‐of‐the‐art approaches, context models are created by capturing the semantics of candidate answers (e.g., “novel,”“singer,”“coach,” and “city”). This work is extended by investigating the impact on context models of extra linguistic knowledge such as part‐of‐speech tagging and named‐entity recognition. Results showed the effectiveness of context models as n‐gram lexicalized dependency paths and promising context indicators for the presence of definitions in natural language texts.     

FAIRNESS IN RECURRENT AUCTIONS WITH COMPETING MARKETS AND SUPPLY FLUCTUATIONS

Auctions have been used to deal with resource allocation in multiagent environments, especially in service‐oriented electronic markets. In this type of market, resources are perishable and auctions are repeated over time with the same or a very similar set of agents. In this scenario it is advisable to use recurrent auctions: a sequence of auctions of any kind where the result of one auction may influence the following one. Some problems do appear in these situations, as for instance, the bidder drop problem, the asymmetric balance of negotiation power or resource waste, which could cause the market to collapse. Fair mechanisms can be useful to minimize the effects of these problems. With this aim, we have analyzed four previous fair mechanisms under dynamic scenarios and we have proposed a new one that takes into account changes in the supply as well as the presence of alternative marketplaces. We experimentally show how the new mechanism presents a higher average performance under all simulated conditions, resulting in a higher profit for the auctioneer than with the previous ones, and in most cases avoiding the waste of resources.     

Bioinspired sparse spectro-temporal representation of speech for robust classification

In this work, a first approach to a robust phoneme recognition task by means of a biologically inspired feature extraction method is presented. The proposed technique provides an approximation to the speech signal representation at the auditory cortical level. It is based on an optimal dictionary of atoms, estimated from auditory spectrograms, and the Matching Pursuit algorithm to approximate the cortical activations. This provides a sparse coding with intrinsic noise robustness, which can be therefore exploited when using the system in adverse environments. The recognition task consisted in the classification of a set of 5 easily confused English phonemes, in both clean and noisy conditions. Multilayer perceptrons were trained as classifiers and the performance was compared to other classic and robust parameterizations: the auditory spectrogram, a probabilistic optimum filtering on Mel frequency cepstral coefficients and the perceptual linear prediction coefficients. Results showed a significant improvement in the recognition rate of clean and noisy phonemes by the cortical representation over these other parameterizations.     

Asymptotic synchronization of the Colpitts oscillator

In this paper we deal with the observer-based asymptotic synchronization problem for a class of chaotic oscillators. Some results based on a differential algebraic approach are used in order to determine the algebraic observability of unknown variables. The strategy consists of proposing a slave system (observer) which tends to follow asymptotically the master system. The methodology is tested in the real-time asymptotic synchronization of the Colpitts oscillator by means of a proportional reduced order observer (PROO) of free-model type.     

Ontology-based user profile learning

Personal agents gather information about users in a user profile. In this work, we propose a novel ontology-based user profile learning. Particularly, we aim to learn context-enriched user profiles using data mining techniques and ontologies. We are interested in knowing to what extent data mining techniques can be used for user profile generation, and how to utilize ontologies for user profile improvement. The objective is to semantically enrich a user profile with contextual information by using association rules, Bayesian networks and ontologies in order to improve agent performance. At runtime, we learn which the relevant contexts to the user are based on the user’s behavior observation. Then, we represent the relevant contexts learnt as ontology segments. The encouraging experimental results show the usefulness of including semantics into a user profile as well as the advantages of integrating agents and data mining using ontologies.