Identification of Structural and Molecular Signatures Mediating Adaptive Changes in the Mouse Kidney in Response to Pregnancy

Pregnancy is characterized by adaptations in the function of several maternal body systems that ensure the development of the fetus whilst maintaining health of the mother. The renal system is responsible for water and electrolyte balance, as well as waste removal. Thus, it is imperative that structural and functional changes occur in the kidney during pregnancy. However, our knowledge of the precise morphological and molecular mechanisms occurring in the kidney during pregnancy is still very limited. Here, we investigated the changes occurring in the mouse kidney during pregnancy by performing an integrated analysis involving histology, gene and protein expression assays, mass spectrometry profiling and bioinformatics. Data from non-pregnant and pregnant mice were used to identify critical signalling pathways mediating changes in the maternal kidneys. We observed an expansion of renal medulla due to proliferation and infiltration of interstitial cellular constituents, as well as alterations in the activity of key cellular signalling pathways (e.g., AKT, AMPK and MAPKs) and genes involved in cell growth/metabolism (e.g., Cdc6, Foxm1 and Rb1) in the kidneys during pregnancy. We also generated plasma and urine proteomic profiles, identifying unique proteins in pregnancy. These proteins could be used to monitor and study potential mechanisms of renal adaptations during pregnancy and disease.     

Optimization of the material flow in a manufacturing plant by use of artificial bee colony algorithm

To survive in today’s competitive global market, companies must perform strategic changes in order to increase productivity, eliminating wasted materials, time, and effort. This study will examine how to optimize the time and effort required to supply raw material to different production lines in a manufacturing plant in Juarez, Mexico by minimizing the distance an operator must travel to distribute material from a warehouse to a set of different production lines with corresponding demand. The core focus of this study is similar to that of the Vehicle Routing Problem in that it is treated as a combinatorial optimization problem. The artificial bee colony algorithm is applied in order to find the optimal distribution of material with the aim of establishing a standard time for this duty by examining how this is applied in a local manufacturing plant. Results show that using this approach may be convenient to set standard times in the selected company.     

Criticality and threat analysis on utility tunnels for planning security policies of utilities in urban underground space

A utility tunnel is defined as an underground structure containing one or more utilities, permitting the installation, maintenance and removal of the systems without the necessity of making street cuts or excavations. These underground facilities contain all essential utilities serving large urban areas collected together in a tunnel; therefore they are an inviting target for sabotage or vandalism. This paper proposes an expert system combining color-coded scales, Delphi and AHP methods to analyze criticality and threats on utility tunnels to support planning of security policies for utilities in urban subsurface.     

Control of attendance applied in higher education through mobile NFC technologies

This paper presents an improved attendance control system, required for continuous evaluation which has become compulsory following the Bologna Process. It provides a solution based on NFC technology and is based on a real project developed and pilot tested at the “Universidad Pontificia de Salamanca, Campus Madrid”.     

SWTRACK: An intelligent model for cargo tracking based on off-the-shelf mobile devices

Logistics represent the biggest cost for transportation companies. Companies are investing in monitoring and tracking systems aiming at improving services, reducing costs and ensuring the safety in cargos transports. This article presents an intelligent model to obtain the position of vehicles, using off-the-shelf mobile devices named SWTRACK. The proposal allows distributors companies to track loads and to have real-time information about their whereabouts during a travel. SWTRACK presents an automatic travel management to initialize and finish travels without user interaction. Furthermore, it provides a mechanism to monitor detours in planned routes and to send alarms notification through mobile devices. To build that mechanism, we studied the Geofence concept and existing techniques to prepare two solutions based on continuous monitoring. Those solutions were compared and discussed. We also conducted a model evaluation using off-the-shelf mobile devices. Moreover, we provide an impact analysis regarding GPS precision and battery power consumption. In the end, we show that the proposed model generates a reliable system for tracking vehicles.     

Constraint-wrench analysis of robotic manipulators

The constraint-wrench analysis of mechanisms, with focus on parallel robots, is the subject of this paper. Although the method proposed here can be generalized for parallel robots with multiple-loop kinematic chains, here, single-loop chains are targeted. To this end, a novel representation of the constraints imposed by the kinematic pairs is introduced. With this representation, the constraint matrix of a mechanism is readily derived. For the calculation of the constraint wrenches, by means of the constraint matrix and based on the Newton–Euler formulation, a new procedure is introduced. As a case study, the constraint wrench analysis of the McGill Schönflies Motion Generator (SMG), while undergoing a test cycle adopted by the industry, is conducted.     

A differential inclusion approach for modeling and analysis of dynamical systems under uncertainty

In this paper we deal with the application of differential inclusions to modeling nonlinear dynamical systems under uncertainty in parameters. In this case, differential inclusions seem to be better suited to modeling practical situations under uncertainty and imprecision than formulations by means of fuzzy differential equations. We develop a practical algorithm to approximate the reachable sets of a class of nonlinear differential inclusion, which eludes the computational problems of a previous set-valued version of the Heun’s method. Our algorithm is based on a complete discretization (time and state space) of the differential inclusion and it suits hardware features, handling the memory used by the method in a controlled fashion during all iterations. As a case of study, we formulate a differential inclusion to model an epidemic outbreak of dengue fever under Cuban conditions. The model takes into account interaction of human and mosquito populations as well as vertical transmission in the mosquito population. It is studied from the theoretical point of view to apply the Practical Algorithm. Also, we estimate the temporal evolution of the different human and mosquito populations given by the model in the Dengue 3 epidemic in Havana 2001, through the computation of the reachable sets using the Practical Algorithm.     

Mixture and non-mixture cure fraction models based on the generalized modified Weibull distribution with an application to gastric cancer data

The cure fraction models are usually used to model lifetime time data with long-term survivors. In the present article, we introduce a Bayesian analysis of the four-parameter generalized modified Weibull (GMW) distribution in presence of cure fraction, censored data and covariates. In order to include the proportion of “cured” patients, mixture and non-mixture formulation models are considered. To demonstrate the ability of using this model in the analysis of real data, we consider an application to data from patients with gastric adenocarcinoma. Inferences are obtained by using MCMC (Markov Chain Monte Carlo) methods.     

STATISTICAL LINGUISTIC LAWS IN ECOLOGICAL MODELS

This paper gives the theoretical setup so that an ecological model, as a particular mathematical model, can be considered a text written in a formal language (mathematics), and therefore, statistical linguistic laws can be applied to obtain information parameters in different semantic levels of the same model. The statistical laws will be useful to: a) compare semantic levels, submodels, and different models mutually; b) prove that information temperature parameter is an indirect measure of meaning: the significance or semantic component of information, opposed to significant, or comprehension, on the part of the observer (modeller) of the model text. We will apply these ideas in two practical examples.