The Effect of Proximity, Explicitness, and Representation of Basic Science Information on Student Clinical Problem-Solving

Problem: Medical and veterinary students memorize facts but then have difficulty applying those facts in clinical problem solving. Cognitive engineering research suggests that the inability of medical and veterinary students to infer concepts from facts may be due in part to specific features of how information is represented and organized in educational materials. First, physical separation of pieces of information may increase the cognitive load on the student. Second, information that is necessary but not explicitly stated may also contribute to the student’s cognitive load. Finally, the types of representations – textual or graphical – may also support or hinder the student’s learning process. This may explain why students have difficulty applying biomedical facts in clinical problem solving. Purpose: To test the hypothesis that three specific aspects of expository text – the patial distance between the facts needed to infer a rule, the explicitness of information, and the format of representation – affected the ability of students to solve clinical problems. Setting: The study was conducted in the parasitology laboratory of a college of veterinary medicine in Texas. Sample: The study subjects were a convenience sample consisting of 132 second-year veterinary students who matriculated in 2007. The age of this class upon admission ranged from 20-52, and the gender makeup of this class consisted of approximately 75% females and 25% males. Results: No statistically significant difference in student ability to solve clinical problems was found when relevant facts were placed in proximity, nor when an explicit rule was stated. Further, no statistically significant difference in student ability to solve clinical problems was found when students were given different representations of material, including tables and concept maps. Findings: The findings from this study indicate that the three properties investigated – proximity, explicitness, and representation – had no statistically significant effect on student learning as it relates to clinical problem-solving ability. However, ad hoc observations as well as findings from other researchers suggest that the subjects were probably using rote learning techniques such as memorization, and therefore were not attempting to infer relationships from the factual material in the interventions, unless they were specifically prompted to look for patterns. A serendipitous finding unrelated to the study hypothesis was that those subjects who correctly answered questions regarding functional (non-morphologic) properties, such as mode of transmission and intermediate host, at the family taxonomic level were significantly more likely to correctly answer clinical case scenarios than were subjects who did not correctly answer questions regarding functional properties. These findings suggest a strong relationship (p < .001) between well-organized knowledge of taxonomic functional properties and clinical problem solving ability. Recommendations: Further study should be undertaken investigating the relationship between knowledge of functional taxonomic properties and clinical problem solving ability. In addition, the effect of prompting students to look for patterns in instructional material, followed by the effect of factors that affect cognitive load such as proximity, explicitness, and representation, should be explored.

NOVEL THERAPEUTIC TARGETS IDENTIFIED BY HIGH-THROUGHPUT TECHNOLOGIES FOR TRIPLE-NEGATIVE BREAST CANCER

Triple-negative breast cancers (TNBC) are characterized by the lack of or reduced expression of the estrogen and progesterone receptors, and normal expression of the human epidermal growth factor receptor 2. The lack of a well-characterized target for treatment leaves only systemic chemotherapy as the mainstay of treatment. Approximately 60-70% of patients are chemosensitive, while the remaining majority does not respond. Targeted therapies that take advantage of the unique molecular perturbations found in triple-negative breast cancer are needed. The genes that are frequently amplified or overexpressed represent potential therapeutic targets for triple-negative breast cancer. The purpose of this study was to identify and validate novel therapeutic targets for triple-negative breast cancers. 681 genes showed consistent and highly significant overexpression in TNBC compared to receptor-positive cancers in 2 data sets. For two genes, 3 of the 4 siRNAs showed preferential growth inhibition in TNBC cells. These two genes were the low density lipoprotein receptor-related protein 8 (LRP8) and very low-density lipoprotein receptor (VLDLR). Exposure to their cognate ligands, reelin and apolipoprotein E isoform 4 (ApoE4), stimulated the growth of TNBC cells in vitro. Suppression of the expression of either LRP8 or VLDLR or exposure to RAP (an inhibitor of ligand binding to LRP8 and VLDLR) abolished this ligand-induced proliferation. High-throughput protein and metabolic arrays revealed that ApoE4 stimulation rescued TNBC cells from serum-starvation induced up-regulation of genes involved in lipid biosynthesis, increased protein expression of oncogenes involved in the MAPK/ERK and DNA repair pathways, and reduced the serum-starvation induction of biochemicals involved in oxidative stress response and glycolytic metabolism. shLRP8 MDA-MB-231 xenografts had reduced tumor volume, in comparison to parental and shCON xenografts. These results indicate that LRP8-APOE signaling confers survival advantages to TNBC tumors under reduced nutrient conditions and during cellular environmental stress. We revealed that the LRP8-APOE receptor-ligand system is overexpressed in human TNBC. We also demonstrated that this receptor system mediates a strong growth promoting and survival function in TNBC cells in vitro and helps to sustain the growth of MDA-MD-231 xenografts. We propose that inhibitors of LRP8-APOE signaling may be clinically useful therapeutic agents for triple-negative breast cancer.

Model results of ocean physical field of the reference state and the targets in NetCDF format

In a feasibility study, the potential of proxy data for the temperature and salinity during the Last Glacial Maximum (LGM, about 19 000 to 23 000 years before present) in constraining the strength of the Atlantic meridional overturning circulation (AMOC) with a general ocean circulation model was explored. The proxy data were simulated by drawing data from four different model simulations at the ocean sediment core locations of the Multiproxy Approach for the Reconstruction of the Glacial Ocean surface (MARGO) project, and perturbing these data with realistic noise estimates. The results suggest that our method has the potential to provide estimates of the past strength of the AMOC even from sparse data, but in general, paleo-sea-surface temperature data without additional prior knowledge about the ocean state during the LGM is not adequate to constrain the model. On the one hand, additional data in the deep-ocean and salinity data are shown to be highly important in estimating the LGM circulation. On the other hand, increasing the amount of surface data alone does not appear to be enough for better estimates. Finally, better initial guesses to start the state estimation procedure would greatly improve the performance of the method. Indeed, with a sufficiently good first guess, just the sea-surface temperature data from the MARGO project promise to be sufficient for reliable estimates of the strength of the AMOC.

A Mathematical Representation of "Excitement" in Games from the Viewpoint of a Neutral Audience

Researchers have long believed the concept of "excitement" in games to be subjective and difficult to measure. This paper presents the development of a mathematically computable index that measures this concept from the viewpoint of an audience. One of the key aspects of the index is the differential of the probability of "winning" before and after one specific "play" in a given game. If the probability of winning becomes very positive or negative by that play, then the audience will feel the game to be "exciting." The index makes a large contribution to the study of games and enables researchers to compare and analyze the "excitement" of various games. It may be applied to many fields especially the area of welfare economics, ranging from allocative efficiency to axioms of justice and equity.

A Mathematical Representation of "Excitement" in Games: A Contribution to the Theory of Game Systems

Researchers have long believed the concept of "excitement" in games to be subjective and difficult to measure. This paper presents the development of a mathematically computable index that measures the concept from the viewpoint of an audience and from that of a player. One of the key aspects of the index is the differential of the probability of "winning" before and after one specific "play" in a given game. The index makes a large contribution to the study of games and enables researchers to compare and analyze the “excitement” of various games. It may be applied in many fields, especially the area of welfare economics, and applications may range from those related to allocative efficiency to axioms of justice and equity.

Project management competence analysis in rural communities through territorial representation: application to Aymara women communities in Puno (Peru)

The acquisition of technical, contextual and behavioral competences is a prerequisite for sustainable development and strengthening of rural communities. Territorial display of the status of these skills helps to design the necessary learning, so its inclusion in planning processes is useful for decision making. The article discusses the application of visual representation of competences in a rural development project with Aymara women communities in Peru. The results show an improvement of transparency and dialogue, resulting in a more successful project management and strengthening of social organization.

Neuromorphic Detection of Vowel Representation Spaces

In this paper a layered architecture to spot and characterize vowel segments in running speech is presented. The detection process is based on neuromorphic principles, as is the use of Hebbian units in layers to implement lateral inhibition, band probability estimation and mutual exclusion. Results are presented showing how the association between the acoustic set of patterns and the phonologic set of symbols may be created. Possible applications of this methodology are to be found in speech event spotting, in the study of pathological voice and in speaker biometric characterization, among others.

Symmetry Issues in Directly Irradiated Targets

In direct drive Inertial Confinement Fusion (ICF), the typical laser beam to laser beam angle is around 30o. This fact makes the study of the irradiation symmetry agenuine 3D problem. In this paper we use the three dimensional version of the MULTI hydrocode to assess the symmetry of such ICF implosions. More specifically, we study a shock-ignition proposal for the Laser-M´egajoule facility (LMJ) in which two of the equatorial beam cones are used to implode and pre compress a spherical capsule (the “reference” capsule of HiPER project) made of 0.59 mg of pure Deuterium-Tritium mixture. The symmetry of this scheme is analysed and optimized to get a design inside the operating limits of LMJ. The studied configuration has been found essentially axial-symmetric, so that the use of 2D hydrocodes would be appropriate for this specific situation.

Optimizing Functional Network Representation of Multivariate Time Series

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

Using modular neural networks to model self-consciousness and self-representation for artificial entities

Self-consciousness implies not only self or group recognition, but also real knowledge of one’s own identity. Self-consciousness is only possible if an individual is intelligent enough to formulate an abstract self-representation. Moreover, it necessarily entails the capability of referencing and using this elf-representation in connection with other cognitive features, such as inference, and the anticipation of the consequences of both one’s own and other individuals’ acts. In this paper, a cognitive architecture for self-consciousness is proposed. This cognitive architecture includes several modules: abstraction, self-representation, other individuals'representation, decision and action modules. It includes a learning process of self-representation by direct (self-experience based) and observational learning (based on the observation of other individuals). For model implementation a new approach is taken using Modular Artificial Neural Networks (MANN). For model testing, a virtual environment has been implemented. This virtual environment can be described as a holonic system or holarchy, meaning that it is composed of autonomous entities that behave both as a whole and as part of a greater whole. The system is composed of a certain number of holons interacting. These holons are equipped with cognitive features, such as sensory perception, and a simplified model of personality and self-representation. We explain holons’ cognitive architecture that enables dynamic self-representation. We analyse the effect of holon interaction, focusing on the evolution of the holon’s abstract self-representation. Finally, the results are explained and analysed and conclusions drawn.

Ignition conditions for inertial confinement fusion targets with a nuclear spin-polarized DT fuel

The nuclear fusion cross-section is modified when the spins of the interacting nuclei are polarized. In the case of deuterium?tritium it has been theoretically predicted that the nuclear fusion cross-section could be increased by a factor d = 1.5 if all the nuclei were polarized. In inertial confinement fusion this would result in a modification of the required ignition conditions. Using numerical simulations it is found that the required hot-spot temperature and areal density can both be reduced by about 15% for a fully polarized nuclear fuel. Moreover, numerical simulations of a directly driven capsule show that the required laser power and energy to achieve a high gain scale as d-0.6 and d-0.4 respectively, while the maximum achievable energy gain scales as d0.9.

The Phase Space as a New Representation of the Dynamical Behaviour of Temperature and Enthalpy in a Reefer monitored with a Multidistributed Sensors Network

The study of temperature gradients in cold stores and containers is a critical issue in the food industry for the quality assurance of products during transport, as well as forminimizing losses. The objective of this work is to develop a new methodology of data analysis based on phase space graphs of temperature and enthalpy, collected by means of multidistributed, low cost and autonomous wireless sensors and loggers. A transoceanic refrigerated transport of lemons in a reefer container ship from Montevideo (Uruguay) to Cartagena (Spain) was monitored with a network of 39 semi-passive TurboTag RFID loggers and 13 i-button loggers. Transport included intermodal transit from transoceanic to short shipping vessels and a truck trip. Data analysis is carried out using qualitative phase diagrams computed on the basis of Takens?Ruelle reconstruction of attractors. Fruit stress is quantified in terms of the phase diagram area which characterizes the cyclic behaviour of temperature. Areas within the enthalpy phase diagram computed for the short sea shipping transport were 5 times higher than those computed for the long sea shipping, with coefficients of variation above 100% for both periods. This new methodology for data analysis highlights the significant heterogeneity of thermohygrometric conditions at different locations in the container.

Methodology to assess EU Biofuel Policies: The CAPRI Approach

This report is based on the outcome of a study carried out by the European Commission's Joint Research Centre - Institute for Prospective Technological Studies (JRC-IPTS, Spain) in cooperation with EuroCARE (Bonn, Germany). The report provides a detailed description of the methodology developed to assess the implications of the European Renewable Energy Directive on the agricultural sector, with an explicit focus on regional effects of biofuel targets in the EU. For the analysis, the spatial agricultural sector model CAPRI has been extended to include a global representation of biofuel markets (with endogenous supply, demand and trade flows for biofuels and biofuel feedstocks) while keeping the focus on regional impacts in the EU. The model is capable to simulate the impacts of EU biofuel policies on food production and prices, the potential use of by-products in the feed chain, the increasing pressure on marginal and idle land and the share of imported biofuels (self-sufficiency indicators). CAPRI is now able to jointly assess biofuel and agricultural policies, including policy instruments defined at the Member State level. The CAPRI biofuel module allows for a detailed analysis of most relevant biofuel support instruments like consumer tax exemptions, quota obligations, import tariffs and other trade measures. Additionally, the model allows for analysing scenarios regarding technical progress in 2nd generation technologies for biofuels.

Inverse bremsstrahlung absorption in spherical laser targets

Inverse bremsstrahlung has been incorporated into an analytical model of the expanding corona of a laser-irradiated spherical target. Absorption decreases slowly with increasing intensity, in agreement with some numerical simulations, and contrary to estimates from simple models in use up to now, which are optimistic at low values of intensity and very pessimistic at high values. Present results agree well with experimental data from many laboratories; substantial absorption is found up to moderate intensities,say below IOl5 W cm-2 for 1.06 pm light. Anomalous absorption, wher, included in the analysis, leaves practically unaffected the ablation pressure and mass ablation rate, for given absorbed intensity. Universal results are given in dimensionless fom.

Nonuniform irradiation of laser targets

Smoothing of plasma ablated from a laser target under weakly nonuniform irradiation is discussed. Conduction is assumed restricted to a quasisteady layer enclosing the critical surface (large pellet or focal spot, and long, low-intensity, short-wavelength pulse). Light refraction can make the ablated plasma unstable.