Energy Deposition around Swift Carbon-Ion Tracks in Liquid Water

Energetic carbon ions are promising projectiles used for cancer radiotherapy. A thorough knowledge of how the energy of these ions is deposited in biological media (mainly composed of liquid water) is required. This can be attained by means of detailed computer simulations, both macroscopically (relevant for appropriately delivering the dose) and at the nanoscale (important for determining the inflicted radiobiological damage). The energy lost per unit path length (i.e., the so-called stopping power) of carbon ions is here theoretically calculated within the dielectric formalism from the excitation spectrum of liquid water obtained from two complementary approaches (one relying on an optical-data model and the other exclusively on ab initio calculations). In addition, the energy carried at the nanometre scale by the generated secondary electrons around the ion’s path is simulated by means of a detailed Monte Carlo code. For this purpose, we use the ion and electron cross sections calculated by means of state-of-the art approaches suited to take into account the condensed-phase nature of the liquid water target. As a result of these simulations, the radial dose around the ion’s path is obtained, as well as the distributions of clustered events in nanometric volumes similar to the dimensions of DNA convolutions, contributing to the biological damage for carbon ions in a wide energy range, covering from the plateau to the maximum of the Bragg peak.     

3′5-Dimaleamylbenzoic Acid Attenuates Bleomycin-Induced Pulmonary Fibrosis in Mice

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by parenchymal scarring, leading progressively to alveolar architecture distortion, respiratory failure, and eventually death. Currently, there is no effective treatment for IPF. Previously, 3′5-dimaleamylbenzoic acid (3′5-DMBA), a maleimide, demonstrated pro-apoptotic, anti-inflammatory, and anti-cancer properties; however, its potential therapeutic effects on IPF have not been addressed. Bleomycin (BLM) 100 U/kg was administered to CD1 mice through an osmotic minipump. After fourteen days of BLM administration, 3′5-DMBA (6 mg/kg or 10 mg/kg) and its vehicle carboxymethylcellulose (CMC) were administered intragastrically every two days until day 26. On day 28, all mice were euthanized. The 3′5-DMBA effect was assessed by histological and immunohistochemical staining, as well as by RT-qPCR. The redox status on lung tissue was evaluated by determining the glutathione content and the GSH/GSSG ratio. 3′5-DMBA treatment re-established typical lung histological features and decreased the expression of BLM-induced fibrotic markers: collagen, α-SMA, and TGF-β1. Furthermore, 3′5-DMBA significantly reduced the expression of genes involved in fibrogenesis. In addition, it decreased reduced glutathione and increased oxidized glutathione content without promoting oxidative damage to lipids, as evidenced by the decrease in the lipid peroxidation marker 4-HNE. Therefore, 3′5-DMBA may be a promising candidate for IPF treatment.     

MEXICA: A computer model of a cognitive account of creative writing

MEXICA is a computer model that produces frameworks for short stories based on the engagement-reflection cognitive account of writing. During engagement MEXICA generates material guided by content and rhetorical constraints, avoiding the use of explicit goals or story-structure information. During reflection the system breaks impasses, evaluates the novelty and interestingness of the story in progress and verifies that coherence requirements are satisfied. In this way, MEXICA complements and extends those models of computerised story-telling based on traditional problem-solving techniques where explicit goals drive the generation of stories. This paper describes the engagement-reflection account of writing, the general characteristics of MEXICA and reports an evaluation of the program.     

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.     

Damp trend Grey Model forecasting method for airline industry

This paper presents a modification of the Grey Model (GM) to forecast routes passenger demand growth in the air transportation industry. Forecast methods like Holt-Winters, autoregressive models, exponential smoothing, neural network, fuzzy logic, GM model calculate very high airlines routes pax growth. For this reason, a modification has been done to the GM model to damp trend calculations as time grows. The simulation results show that the modified GM model reduces the model exponential estimations grow. It allows the GM model to forecast reasonable routes passenger demand for long lead-times forecasts. It makes this model an option to calculate airlines routes pax flow when few data points are available.The United States domestic air transport market data are used to compare the performance of the GM model with the proposed model.     

A cluster-DEE-based strategy to empower protein design

The Medical and Pharmaceutical industries have shown high interest in the precise engineering of protein hormones and enzymes that perform existing functions under a wide range of conditions. Proteins are responsible for the execution of different functions in the cell: catalysis in chemical reactions, transport and storage, regulation and recognition control. Computational Protein Design (CPD) investigates the relationship between 3-D structures of proteins and amino acid sequences and looks for all sequences that will fold into such 3-D structure. Many computational methods and algorithms have been proposed over the last years, but the problem still remains a challenge for Mathematicians, Computer Scientists, Bioinformaticians and Structural Biologists. In this article we present a new method for the protein design problem. Clustering techniques and a Dead-End-Elimination algorithm are combined with a SAT problem representation of the CPD problem in order to design the amino acid sequences. The obtained results illustrate the accuracy of the proposed method, suggesting that integrated Artificial Intelligence techniques are useful tools to solve such an intricate problem.     

Efficient implementation of the implicit error correction nVoD schema

Implicit Error Correction is a near Video-on-Demand (nVoD) schema that trades bandwidth utilization for initial playback delay while potentially allowing an infinite number of users. Additionally, it provides error protection without further bandwidth increase by exploiting the implicit redundancy of nVoD protocols, using linear combinations of the segments transmitted in a given time slot. This paper introduces a new implementation that outperforms the original schema by adding two new mechanisms: redundancy channels and feedback error correction.     

Controller design for a wind farm, considering both power and load aspects

In this paper, a wind farm controller is developed that distributes power references among wind turbines while it reduces their structural loads. The proposed controller is based on a spatially discrete model of the farm, which delivers an approximation of wind speed in the vicinity of each wind turbine. The control algorithm determines the reference signals for each individual wind turbine controller in two scenarios based on low and high wind speed. In low wind speed, the reference signals for rotor speed are adjusted, taking the trade-off between power maximization and load minimization into account. In high wind speed, the power and pitch reference signals are determined while structural loads are minimized. To the best of authors’ knowledge, the proposed dynamical model is a suitable framework for control, since it provides a dynamic structure for behavior of the flow in wind farms. Moreover, the controller has been proven exceptionally useful in solving the problem of both power and load optimization on the basis of this model.     

Characteristic time,developers’ behavior and leapfrogging dynamics of high-rise buildings

This paper suggests a plausible explanation for the spatial evolution of high-rise buildings. Contrary to intuitive expectations the histograms and spatial distribution in cities indicate peculiar and non-continuous patterns. These patterns evolve as a result of developers’ behavior in real-estate markets. One of the critical variables in decision-making of developers is time. Despite obvious differences in land prices within a particular real-estate market, differences in costs and prices are relatively small in comparison to differences in their time incidence. The paper presents a simple search model of land developer that suggests interesting explanation of the spatial sprawl of cities. The central parameter in our model is “characteristic time”, the period of time from the acquisition of initial property rights in the land and until the first return on the investment is realized. The model leads to leapfrogging patterns, particularly during downturn periods and explains the appearance of high-rise buildings in the urban periphery.