Pharmacogenetics Biomarkers and Their Specific Role in Neoadjuvant Chemoradiotherapy Treatments: An Exploratory Study on Rectal Cancer Patients

Background: Pathological complete response (pCR) to neoadjuvant chemoradiotherapy (CRT) in locally advanced rectal cancer (LARC) is still ascribed to a minority of patients. A pathway based-approach could highlight the predictive role of germline single nucleotide polymorphisms (SNPs). The primary aim of this study was to define new predictive biomarkers considering treatment specificities. Secondary aim was to determine new potential predictive biomarkers independent from radiotherapy (RT) dosage and cotreatment with oxaliplatin. Methods: Thirty germ-line SNPs in twenty-one genes were selected according to a pathway-based approach. Genetic analyses were performed on 280 LARC patients who underwent fluoropyrimidine-based CRT. The potential predictive role of these SNPs in determining pathological tumor response was tested in Group 1 (94 patients undergoing also oxaliplatin), Group 2 (73 patients treated with high RT dosage), Group 3 (113 patients treated with standard RT dosage), and in the pooled population (280 patients). Results: Nine new predictive biomarkers were identified in the three groups. The most promising one was rs3136228-MSH6 (p = 0.004) arising from Group 3. In the pooled population, rs1801133-MTHFR showed only a trend (p = 0.073). Conclusion: This exploratory study highlighted new potential predictive biomarkers of neoadjuvant CRT and underlined the importance to strictly define treatment peculiarities in pharmacogenetic analyses.     

Morphology modulation of gas‐foamed,micrometric, hollow polystyrene particles

In this article, we reports the effects of the processing conditions on the morphological and hollow attributes of polystyrene micrometric hollow particles produced by the use of a recently developed technique based on the gas foaming of spherical, dense particles. By modulating the foaming temperature and saturation pressure, we produced hollow particles with different attributes in terms of hollow dimensions, eccentricity, and open–close features. The results from these small systems were compared, and we found agreement with what is typically observed in bulk polymeric foaming, for example, an increase in the foaming efficiency with saturation pressure and the nonmonotonic effect of temperature. Furthermore, we observed an increase in the hollow number when using nucleating agents with respect to the neat polymer and when using nitrogen with respect to carbon dioxide as the blowing agent. The effects of particle manipulation before foaming to achieve hollow elongated or distorted particles are also reported. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 44236.     

Development of an undergraduate course – Internet-based instrumentation and control

The objective, strategy, and implementation details of a new undergraduate course, Internet-based Instrumentation and Control, are presented. The course has a companion laboratory that is supported by the National Science Foundation and industry. The combination is offered to senior-level undergraduate engineering students interested in sensing, instrumentation, control, and web programming that want to learn more about the integration of these technologies for solving real-world engineering problems. The course will also be offered to gifted high school seniors with similar interests and can serve as a vehicle to attract them to engineering disciplines. Preliminary assessment of the first offering of the course is encouraging and has shown that the course has achieved success in helping students understand concepts and master basic technologies for developing Internet-based automatic systems.     

A semantic approach to remove incoherent items from a user profile and improve the accuracy of a recommender system

Recommender systems usually suggest items by exploiting all the previous interactions of the users with a system (e.g., in order to decide the movies to recommend to a user, all the movies she previously purchased are considered). This canonical approach sometimes could lead to wrong results due to several factors, such as a change in user preferences over time, or the use of her account by third parties. This kind of incoherence in the user profiles defines a lower bound on the error the recommender systems may achieve when they generate suggestions for a user, an aspect known in literature as magic barrier. This paper proposes a novel dynamic coherence-based approach to define the user profile used in the recommendation process. The main aim is to identify and remove, from the previously evaluated items, those not semantically adherent to the others, in order to make a user profile as close as possible to the user’s real preferences, solving the aforementioned problems. Moreover, reshaping the user profile in such a way leads to great advantages in terms of computational complexity, since the number of items considered during the recommendation process is highly reduced. The performed experiments show the effectiveness of our approach to remove the incoherent items from a user profile, increasing the recommendation accuracy.     

Personality theories facilitate integrating the five principles and deducing hypotheses for testing.

Comments on the original article "A New Big Five: Fundamental Principles for an Integrative Science of Personality," by Dan P. McAdams and Jennifer L. Pals (see record 2006-03947-002). In presenting their view of personality science, McAdams and Pals (April 2006) elaborated the importance of five principles for building an integrated science of personality. These principles are stances on evolution and human nature, dispositional signatures, characteristic adaptations, life narratives, and the differential role of culture. Their main emphasis involved differentiating these principles and indicating that they are all relevant to understanding personality. The discussion by McAdams and Pals certainly illuminates the various aspects of personality, but it also cries out for some greater, more systematic integration of the five principles into particular kinds of personality. It is not yet possible, in their approach, to identify different types of personality orientation and to evaluate the relative effectiveness of these orientations. As presented, their approach may be considered a start but hardly a finish. Here, the current author suggests that the metatheory of personality theories that he has proposed (Maddi, 1969/1996) could accelerate the needed integration of the five proposed principles. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

SCIDDICA-SS3: a new version of cellular automata model for simulating fast moving landslides

Cellular Automata (CA) are discrete and parallel computational models useful for simulating dynamic systems that evolve on the basis on local interactions. Some natural events, such as some types of landslides, fall into this type of phenomena and lend themselves well to be simulated with this approach. This paper describes the latest version of the SCIDDICA CA family models, specifically developed to simulate debris-flows type landslides. The latest model of the family, named SCIDDICA-SS₃, inherits all the features of its predecessor, SCIDDICA-SS₂, with the addition of a particular strategy to manage momentum. The introduction of the latter permits a better approximation of inertial effects that characterize some rapid debris flows. First simulations attempts of real landslides with SCIDDICA-SS₃ have produced quite satisfactory results, comparable with the previous model.     

Robust ordinal regression in preference learning and ranking

Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking.     

Selection of a representative value function in robust multiple criteria sorting

We introduce the concept of a representative value function in robust ordinal regression applied to multiple criteria sorting problems. The proposed approach can be seen as an extension of UTADIS^GMS, a new multiple criteria sorting method that aims at assigning actions to p pre-defined and ordered classes. The preference information supplied by the decision maker (DM) is composed of desired assignments of some reference actions to one or several contiguous classes—they are called assignment examples. The robust ordinal regression builds a set of general additive value functions compatible with the assignment examples and results in two assignments: necessary and possible. The necessary assignment specifies the range of classes to which the action can be assigned considering all compatible value functions simultaneously. The possible assignment specifies, in turn, the range of classes to which the action can be assigned considering any compatible value function individually. In this paper, we propose a way of selecting a representative value function among the set of compatible ones. We identify a few targets which build on results of the robust ordinal regression and could be attained by a representative value function. They concern enhancement of differences between possible assignments of two actions. In this way, the selected function highlights the most stable part of the robust sorting, and can be perceived as representative in the sense of robustness preoccupation. We envisage two possible uses of the representative value function in decision support systems. The first one is an explicit exhibition of the function along with the results of the UTADIS^GMS method, in order to help the DM to understand the robust sorting. The other is an autonomous use, in order to supply the DM with sorting obtained by an example-based procedure driven by the chosen function. Three case studies illustrating the use of a representative value function in real-world decision problems are presented. One of those studies is devoted to the comparison of the introduced concept of representativeness with alternative procedures for determining a single value function, which we adapted to sorting problems, because they were originally proposed for ranking problems.     

Spatio-temporal hotspots and application on a disease analysis case via GIS

Hotspot analysis is a spatial analysis that uses cluster techniques for determining areas with elevated concentrations of localized events. We use the consolidated Extended Fuzzy C-Means algorithm to determine the hotspot areas on the map as circles, moreover the advantages of this technique are the linear computational complexity, the robustness to noise and outliers, the automatic determination of the optimal number C of clusters (in the classical FCM algorithm C is chosen a priori). Furthermore it prevents the problem of shifting the clusters with low density area of data points in areas with higher density of such points. We apply this method to study the spatio-temporal variations of the hotspot areas by testing this process on a specific disease problem, precisely we have clusterized 5,000 point-events correspondent to cases of brain cancer detected in the state of New Mexico from 1973 to 1991. We also show that the same results are obtained by using the Extended Gustafson–Kessel algorithm which gives elliptical clusters. We have implemented both algorithms in a Geographic Information System environment. Thus we establish the areas which seem not interested from the incidence of the disease and those areas in which the phenomenon appears to be temporarily attenuated either increased or constant or quite disappeared.