Biochemical,functional, and histochemical effects of essential fatty acid deficiency in rat kidney

The present study was designed to examine the effects of EFA deficiency (EFAD) on biochemical, functional, and structural aspects of the kidney in growing and adult rats fed a normal or EFAD diet for 9 wk after weaning. Food and fluid intake (FI), urine volume, and Na⁺ and K⁺ excretions were measured weekly from weeks 4 to 8 by placing the rats in individual metabolic cages for 24 h. At week 9, Li⁺ and a 5% water load, respectively, were administered at 14 and 1.5 h prior to glomerular and proximal tubular function studies, as assessed by 3-h creatinine (C_Cr) and Li⁺ (C_Li+) clearances. Hematocrit and urine volume; serum and urine [Cr], [Li⁺], [Na⁺], and [K⁺]; and renal FA distribution were also measured. Data [corrected to 100 g/body weight (bw) and presented as means ±SEM] were significant, at P<-0.05. Despite a similar ingestion of solids from weeks 4 to 7 (weeks 7 to 10 of life), the rats on the EFAD diet showed a decreased body weight from week 5. From weeks 4 to 8, Fl and urine volume were similar for both groups, but the Fl increased at week 6 in the EFAD group; 24-h Na⁺ and K⁺ excretions were similar at all weeks, except for an increase in the EFAD group for both ions at week 7. In the EFAD group, C_Cr and C_Li+ decreased by 27 and 56.3%, respectively (385.7±33.4 vs. 280±21.1, and 21.0±2.1 vs. 9.2±1.1 μL/min/100 g; n=9 vs. 10), the latter result suggesting increased proximal reabsorption. The 3-h Na⁺ and K⁺ excretions were similar, but the Li⁺ decreased (0.78±0.06×10⁻² vs. 0.32±0.03×10⁻² μeq/min/100 g) in the EFAD group, giving additional support to the suggestion. Renal structure was normal and similar for both groups, but the EFAD group showed a more prominent proximal tubule brush border, together with heavier periodic acid-Schiff staining in all specimens from weeks 5 to 9. In the EFAD group, FA of the n−9 and n−7 series were higher, but most of the n−6 series were lower as a percentage of total lipids in the medulla and cortex. Medullary levels of 20∶4n−6 were maintained, 22∶4n−6 declined twice, arachidonic acid was maintained, and 20∶5n−3 was lower. The EFAD diet affected glomerular function, proximal tubular structure and function, and FA distribution in the rat kidney.     

Learning Sets of Antecedent-restricted Functional and Multivalued Dependencies with Queries

Identifying dependencies that hold in relational databases is essential to produce good databases designs. In particular, functional and multivalued dependencies are used to obtain relation schemes that satisfy the 4th normal form, a property that is considered satisfactory for most applications. It is known that the class of sets of functional dependencies is learnable in the exact model of learning with queries. Also a subclass of multivalued dependencies, the class of consequent-restricted multivalued dependencies, has been shown to be learnable in this model. In this paper, we present an algorithm that learns a class that contains sets of both functional and multivalued dependencies under some restrictions imposed on the antecedents of dependencies. We also show, as a by-product, an algorithm that learns a non-trivial subclass of 2-quasi Horn formulas, closely related to the class just mentioned.     

Applying model-driven engineering to the development of Rich Internet Applications for Business Intelligence

Business Intelligence (BI) applications have been gradually ported to the Web in search of a global platform for the consumption and publication of data and services. On the Internet, apart from techniques for data/knowledge management, BI Web applications need interfaces with a high level of interoperability (similar to the traditional desktop interfaces) for the visualisation of data/knowledge. In some cases, this has been provided by Rich Internet Applications (RIA). The development of these BI RIAs is a process traditionally performed manually and, given the complexity of the final application, it is a process which might be prone to errors. The application of model-driven engineering techniques can reduce the cost of development and maintenance (in terms of time and resources) of these applications, as they demonstrated by other types of Web applications. In the light of these issues, the paper introduces the S^m4RIA-B methodology, i.e., a model-driven methodology for the development of RIA as BI Web applications. In order to overcome the limitations of RIA regarding knowledge management from the Web, this paper also presents a new RIA platform for BI, called RI@BI, which extends the functionalities of traditional RIAs by means of Semantic Web technologies and B2B techniques. Finally, we evaluate the whole approach on a case study—the development of a social network site for an enterprise project manager.     

Non-Pauli observables for CWS codes

It is known that nonadditive quantum codes can have higher code dimensions than stabilizer codes for the same length and minimum distance. The class of codeword stabilized codes (CWS) provides tools to obtain new nonadditive quantum codes by reducing the problem to finding nonlinear classical codes. In this work, we establish some results on the kind of non-Pauli operators that can be used as observables in the decoding scheme of CWS codes and propose a procedure to obtain those observables.     

Hybrid Nanoscopy of Hybrid Nanomaterials

The combination of complementary techniques to characterize materials at the nanoscale is crucial to gain a more complete picture of their structure, a key step to design and fabricate new materials with improved properties and diverse functions. Here it is shown that correlative atomic force microscopy (AFM) and localization‐based super‐resolution microscopy is a useful tool that provides insight into the structure and emissive properties of fluorescent β‐lactoglobulin (βLG) amyloid‐like fibrils. These hybrid materials are made by functionalization of βLG with organic fluorophores and quantum dots, the latter being relevant for the production of 1D inorganic nanostructures templated by self‐assembling peptides. Simultaneous functionalization of βLG fibers by QD655 and QD525 allows for correlative AFM and two‐color super‐resolution fluorescence imaging of these hybrid materials. These experiments allow the combination of information about the topography and number of filaments that compose a fibril, as well as the emissive properties and nanoscale spatial distribution of the attached fluorophores. This study represents an important step forward in the characterization of multifunctionalized hybrid materials, a key challenge in nanoscience.     

Information fusion for automotive applications – An overview

This article focusses on the fusion of information from various automotive sensors like radar, video, and lidar for enhanced safety and traffic efficiency. Fusion is not restricted to data from sensors onboard the same vehicle but vehicular communication systems allow to propagate and fuse information with sensor data from other vehicles or from the road infrastructure as well. This enables vehicles to perceive information from regions that are hardly accessible otherwise and represents the basis for cooperative driving maneuvers. While the Bayesian framework builds the basis for information fusion, automobile environments are characterized by their a priori unknown topology, i.e., the number, type, and structure of the perceived objects is highly variable. Multi-object detection and tracking methods are a first step to cope with this challenge. Obviously, the existence or non-existence of an object is of paramount importance for safe driving. Such decisions are highly influenced by the association step that assigns sensor measurements to object tracks. Methods that involve multiple sequences of binary assignments are compared with soft-assignment strategies. Finally, fusion based on finite set statistics that (theoretically) avoid an explicit association are discussed.     

A genetic solution based on lexicographical goal programming for a multiobjective job shop with uncertainty

In this work we consider a multiobjective job shop problem with uncertain durations and crisp due dates. Ill-known durations are modelled as fuzzy numbers. We take a fuzzy goal programming approach to propose a generic multiobjective model based on lexicographical minimisation of expected values. To solve the resulting problem, we propose a genetic algorithm searching in the space of possibly active schedules. Experimental results are presented for several problem instances, solved by the GA according to the proposed model, considering three objectives: makespan, tardiness and idleness. The results illustrate the potential of the proposed multiobjective model and genetic algorithm.     

Parallax cues in the design of graphics used in technical education to illustrate complex spatial problems

The explanation of certain 3D concepts is based on 2D drawings. These drawings should contain certain depth cues, such as perspective and overlapping. Until recently, parallax has not been used as a depth cue. Nevertheless, new technologies allow it to be incorporated. This forms the background to our study of the design of interactive educational resources and stereoscopic graphics. The results obtained demonstrate that (1) the use of parallax cues improves the interpretation of the figures and (2) that the assistance they afford is most appreciated by the students with less highly developed spatial perception.     

Differential evolution solution to the SLAM problem

A new solution to the Simultaneous Localization and Modelling problem is presented in this paper. The algorithm is based on the stochastic search for solutions in the state space to the global localization problem by means of a differential evolution algorithm. This non linear evolutive filter, called Evolutive Localization Filter (ELF), searches stochastically along the state space for the best robot pose estimate. The set of pose solutions (the population) focuses on the most likely areas according to the perception and up to date motion information. The population evolves using the log-likelihood of each candidate pose according to the observation and the motion errors derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model.The proposed SLAM algorithm operates in two steps: in the first step the ELF filter is used at local level to re-localize the robot based on the robot odometry, the laser scan at a given position and a local map where only a low number of the last scans have been integrated. In the second step, the aligned laser measures and the corrected robot poses are used to detect whether the robot is revisiting a previously crossed area (i.e., a cycle in the robot trajectory exists). Once a cycle is detected, the Evolutive Localization Filter is used again to estimate the accumulated residual drift in the detected loop and then to re-estimate the robot poses in order to integrate the sensor measures in the global map of the environment.The algorithm has been tested in different environments to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.