Spin Electronics: From Concentrated to Diluted Magnetic Semiconductors and Beyond

This presentation will review physical properties, relevant to spintronics, of concentrated magnetic semiconductors. Examples from the open literature and current research utilizing thin film EuS will be used as illustrations. New research on potential spin injection materials and spin detection will also be described. This latter work, carried out at MARTECH, the Center for Materials Research and Technology, demonstrates that carriers from the half-metal CrO₂ may be injected across an insulator without loss of (100%) spin polarization. It also features the development of a Hall gradiometer capable of detecting as few as 10⁵ spins.     

Learning Structure from Data and Its Application to Ozone Prediction

In this paper we propose an algorithm for structure learning in predictive expert systems based on a probabilistic network representation. The idea is to have the simplest structure (minimum number of links) with acceptable predictive capability. The algorithm starts by building a tree structure based on measuring mutual information between pairs of variables, and then it adds links as necessary to obtain certain predictive performance. We have applied this method for ozone prediction in México City, where the ozone level is used as a global indicator for the air quality in different parts of the city. It is important to predict the ozone level a day, or at least several hours in advance, to reduce the health hazards and industrial losses that occur when the ozone reaches emergency levels. We obtained as a first approximation a tree-structured dependency model for predicting ozone in one part of the city. We observe that even with only three parameters, its estimations are acceptable.A causal network representation and the structure learning techniques produced some very interesting results for the ozone prediction problem. Firstly, we got some insight into the dependence structure of the phenomena. Secondly, we got an indication of which are the important and not so important variables for ozone forecasting. Taking this into account, the measurement and computational costs for ozone prediction could be reduced. And thirdly, we have obtained satisfactory short term ozone predictions based on a small set of the most important parameters.     

Determination of Heat of Adsorption on Clean Solid Surfaces

One of the basic parameters characterizing the bond between a chemisorbed atom (or molecule) and the surface is its dissociation energy. It is difficult, however, to determine the bond energy and therefore it becomes necessary to turn to some other quantity, which is sufficiently close to the bond energy and which can be determined without much difficulty. Such a quantity is the heat of adsorption. Heat of adsorption is defined as the difference between the total energy of the adsorption system prior to and after the adsorption process, i.e., it equals the sum of all energy changes connected with adsorption. Thus the experimentally determined value for the heat of adsorption is obviously closely related to the bond energy of the chemisorbed particle, though this relation is not quite simple [1,2].     

5-Fluorouracil Treatment of CT26 Colon Cancer Is Compromised by Combined Therapy with IMMODIN

Due to the physiological complexity of the tumour, a single drug therapeutic strategy may not be sufficient for effective treatment. Emerging evidence suggests that combination strategies may be important to achieve more efficient tumour responses. Different immunomodulators are frequently tested to reverse the situation for the purpose of improving immune response and minimizing chemotherapy side effects. Immodin (IM) represents an attractive alternative to complement chemotherapy, which can be used to enhance the immune system after disturbances resulting from the side effects of chemotherapy. In the presented study, a model of CT26 tumor-bearing mice was used to investigate the effect of single IM or its combination with 5-fluorouracil (5-FU) on colon cancer cells. Our results highlight that the beneficial role of IM claimed in previous studies cannot be generalised to all chemotherapeutic drugs, as 5-FU toxicity was not increased. On the contrary, the chemotherapeutic anti-cancer efficacy of 5-FU was greatly compromised when combined with IM. Indeed, the combined treatment was significantly less effective regarding the tumour growth and animal survival, most probably due to the increased number of tumour-associated macrophages, and increased 5-FU cytotoxic effect related to kidneys and the liver.     

Digitoxin Affects Metabolism,ROS Production and Proliferation in Pancreatic Cancer Cells Differently Depending on the Cell Phenotype

Digitoxin has repeatedly shown to have negative effects on cancer cell viability; however, the actual mechanism is still unknown. In this study, we investigated the effects of digitoxin (1–100 nM) in four pancreatic cancer cell lines, BxPC-3, CFPAC-1, Panc-1, and AsPC-1. The cell lines differ in their KRAS/BRAF mutational status and primary tumor or metastasis origin. We could detect differences in the basal rates of cell proliferation, glycolysis, and ROS production, giving the cell lines different phenotypes. Digitoxin treatment induced apoptosis in all four cell lines, but to different degrees. Cells derived from primary tumors (Panc-1 and BxPC-3) were highly proliferating with a high proportion of cells in the S/G2 phase, and were more sensitive to digitoxin treatment than the cell lines derived from metastases (CFPAC-1 and AsPC-1), with a high proportion of cells in G0/G1. In addition, the effects of digitoxin on the rate of glycolysis, ROS production, and proliferation were dependent on the basal metabolism and origin of the cells. The KRAS downstream signaling pathways were not altered by digitoxin treatment, thus the effects exerted by digitoxin were probably disconnected from these signaling pathways. We conclude that digitoxin is a promising treatment in highly proliferating pancreatic tumors.     

Collective intelligence as mechanism of medical diagnosis: The iPixel approach

Collective intelligence (CI) is an active field of research, which capitalizes the knowledge of human collectives in order to create, to innovate and to invent. There are two important mechanisms to implement CI: recommender and reputation systems. Recommender systems are used to provide filtered information from a large amount of elements. The recommendations are intended to provide interesting elements to users. Recommendation systems can be developed using different techniques and algorithms where the selection of these techniques depends on the area in which they will be applied. This work presents iPixel Recommender Engine, which is focused on the medical field. iPixel Recommendation Engine supports the process of differential diagnosis by recommending mammographic evaluations. Each mammogram is collectively tagged by the users’ community with a semantic sense; this feature allows iPixel acquires collective knowledge. iPixel can associate more than one feature with each mammogram. This work also presents a qualitative evaluation, where the basic features that a recommendation system should have in the medical field were obtained. Finally, a comparison was carried out with other similar recommender systems in order to know the Pixel advantages.     

‘Long autonomy or long delay?’ The importance of domain in opinion mining

Nowadays, people do not only navigate the web, but they also contribute contents to the Internet. Among other things, they write their thoughts and opinions in review sites, forums, social networks, blogs and other websites. These opinions constitute a valuable resource for businesses, governments and consumers. In the last years, some researchers have proposed opinion extraction systems, mostly domain-independent ones, to automatically extract structured representations of opinions contained in those texts. In this work, we tackle this task in a domain-oriented approach, defining a set of domain-specific resources which capture valuable knowledge about how people express opinions on a given domain. These resources are automatically induced from a set of annotated documents. Some experiments were carried out on three different domains (user-generated reviews of headphones, hotels and cars), comparing our approach to other state-of-the-art, domain-independent techniques. The results confirm the importance of the domain in order to build accurate opinion extraction systems. Some experiments on the influence of the dataset size and an example of aggregation and visualization of the extracted opinions are also shown.     

A semantic role labelling-based framework for learning ontologies from Spanish documents

Currently, most of the information available in the Web is adapted primarily for human consumption, but there is so much information that can no longer be processed by a person in a reasonable time, either in digital or physical formats. To solve this problem, the idea of the Semantic Web arose. The Semantic Web deals with adding machine-readable information to Web pages. Ontologies represent a very important element of this web, as they provide a valid and robust structure to represent knowledge based on concepts, relations, axioms, etc. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semiautomatic methods to learn ontologies. In this sense, this paper proposes a new ontology learning methodology based on semantic role labeling from digital Spanish documents. The method makes it possible to represent multiple semantic relations specially taxonomic and partonomic ones in the standardized OWL 2.0. A set of experiments has been performed with the approach implemented in educational domain that show promising results.