Coumarin Differentially Affects the Morphology of Different Root Types of Maize Seedlings

The effects of coumarin on the length, diameter, and branching density of different root types in maize seedlings (Zea mays L. cv. Cecilia) were investigated. The maize root system represents a useful model for morphological studies, as it consists of radicle, seminal, and nodal roots whose origin and development are quite different. Maize seedlings were grown in a hydroponic culture for 6 days, and then coumarin (at concentrations of 0, 25, 100, and 400 microM) was added to the nutrient solution. Coumarin inhibited root length, but effects differed depending on the root type. C 1/2 values, representing the coumarin concentration causing a 50% inhibition of the root length, were calculated by nonlinear regression. Six, 1, and 0.25 mM coumarin were sufficient to reduce the radicle, seminal, and nodal root lengths by 50%, respectively. At the highest coumarin concentration, the subapical root zone showed swelling. The degree recorded by average diameter was higher in nodal roots than in seminal and radicle roots. Furthermore, coumarin decreased the number of lateral roots and branching density more in the seminal than in the radicle roots. These results suggest the following order of sensitivity to coumarin: nodal > seminal > radicle roots. The observed spatial effects of coumarin could be ecologically significant, since taprooted species could benefit at the disadvantage of fibrous-rooted species and could modify community composition.     

Prognostic and Theranostic Applications of Positron Emission Tomography for a Personalized Approach to Metastatic Castration-Resistant Prostate Cancer

Metastatic castration-resistant prostate cancer (mCRPC) represents a condition of progressive disease in spite of androgen deprivation therapy (ADT), with a broad spectrum of manifestations ranging from no symptoms to severe debilitation due to bone or visceral metastatization. The management of mCRPC has been profoundly modified by introducing novel therapeutic tools such as antiandrogen drugs (i.e., abiraterone acetate and enzalutamide), immunotherapy through sipuleucel-T, and targeted alpha therapy (TAT). This variety of approaches calls for unmet need of biomarkers suitable for patients’ pre-treatment selection and prognostic stratification. In this scenario, imaging with positron emission computed tomography (PET/CT) presents great and still unexplored potential to detect specific molecular and metabolic signatures, some of whom, such as the prostate specific membrane antigen (PSMA), can also be exploited as therapeutic targets, thus combining diagnosis and therapy in the so-called “theranostic” approach. In this review, we performed a web-based and desktop literature research to investigate the prognostic and theranostic potential of several PET imaging probes, such as ¹⁸F-FDG, ¹⁸F-choline and ⁶⁸Ga-PSMA-11, also covering the emerging tracers still in a pre-clinical phase (e.g., PARP-inhibitors’ analogs and the radioligands binding to gastrin releasing peptide receptors/GRPR), highlighting their potential for defining personalized care pathways in mCRPC.     

Bio-inspired algorithm for outliers detection

An essential activity to obtain valuable information to identify, for example, intrusions, faults, system failures, etc, is outliers detection. This paper proposes a bio-inspired algorithm able to detect anomaly data in distributed systems. Each data object is associated with a mobile agent that follows the well-known bio-inspired algorithm of flocking. The agents are randomly disseminated onto a virtual space where they move autonomously in order to form one or more flocks. Through a tailored similarity function, the agents associated with similar objects join in the same flock, whereas, the agents associated with dissimilar objects do not join in any flock. The objects associated with isolated agents or associated with agents grouped into flock with a number of entities lower than a given threshold, represent the outliers. Experimental results on synthetic and real data sets confirm the validity of the approach.     

Sensitivity Analysis of Coupled Hydro-Economic Models: Quantifying Climate Change Uncertainty for Decision-Making

The paper assessed the sensitivity of an integrated hydro-economic model, to provide a quantitative range of uncertainty in the impacts of climate change on water balance components and water use in the agricultural sector of Apulia region located in a semi-arid Mediterranean climate area in southern Italy. Results show that the impacts of climate change are expressed in the future by an increase in the net irrigation requirements (NIRs) of all crops. Total cultivated land is reduced by 8.5 % in the future, and the percentage of irrigated land decreases from 31 to 22 % of total agricultural land. Reduction in the irrigated land, together with the variation in the cropping pattern and the adoption of the different irrigation techniques, led to a decrease in water demand for irrigation across the entire region. The sensitivity analysis shows that the groundwater recharge has the lowest correlation to climatic parameters. Results are addressed to the scientific community and decision makers to support the design of adequate adaptation policies for efficient water management under the severe drought conditions that are likely to occur in the region according to climate change projections.     

CUD@SAT: SAT solving on GPUs

The parallel computing power offered by graphic processing units (GPUs) has been recently exploited to support general purpose applications – by exploiting the availability of general API and the single-instruction multiple-thread-style parallelism present in several classes of problems (e.g. numerical simulations and matrix manipulations) – where relatively simple computations need to be applied to all items in large sets of data. This paper investigates the use of GPUs in parallelising a class of search problems, where the combinatorial nature leads to large parallel tasks and relatively less natural symmetries. Specifically, the investigation focuses on the well-known satisfiability testing (SAT) problem and on the use of the NVIDIA compute unified device architecture, one of the most popular platforms for GPU computing. The paper explores ways to identify strong sources of GPU-style parallelism from SAT solving. The paper describes experiments with different design choices and evaluates the results. The outcomes demonstrate the potential for this approach, leading to one order of magnitude of speedup using a simple NVIDIA platform.     

Constructive negation and constraint logic programming with sets

The aim of this paper is to extend theConstructive Negation technique to the case ofCLP(SεT), a Constraint Logic Programming (CLP) language based on hereditarily (and hybrid) finite sets. The challenging aspects of the problem originate from the fact that the structure on whichCLP(SεT) is based is notadmissible closed, and this does not allow to reuse the results presented in the literature concerning the relationships betweenCLP and constructive negation. We propose a new constraint satisfaction algorithm, capable of correctly handling constructive negation for large classes ofCLP(SεT) programs; we also provide a syntactic characterization of such classes of programs. The resulting algorithm provides a novel constraint simplification procedure to handle constructive negation, suitable to theories where unification admits multiple most general unifiers. We also show, using a general result, that it is impossible to construct an interpreter forCLP(SεT) with constructive negation which is guaranteed to work for any arbitrary program; we identify classes of programs for which the implementation of the constructive negation technique is feasible. Agostino Dovier, Ph.D.: He is a researcher in the Department of Science and Technology at the University of Verona, Italy. He obtained his master degree in Computer Science from the University of Udine, Italy, in 1991 and his Ph.D. in Computer Science from the University of Pisa, Italy, in 1996. His research interests are in Programming Languages and Constraints over complex domains, such as Sets and Multisets. He has published over 20 research papers in International Journals and Conferences. He is teaching a course entitled “Special Languages and Techniques for Programming” at the University of Verona. Enrico Pontelli, Ph.D.: He is an Assistant Professor in the Department of Computer Science at the New Mexico State University. He obtained his Laurea degree from the University of Udine (Italy) in 1991, his Master degree from the University of Houston in 1992, and his Ph.D. degree from New Mexico State University in 1997. His research interests are in Programming Languages, Parallel Processing, and Constraint Programming. He has published over 50 papers and served on the program committees of different conferences. He is presently the Associate Director of the Laboratory for Logic, Databases, and Advanced Programming. Gianfranco Rossi, Ph.D.: He received his degree in Computer Science from the University of Pisa in 1979. From 1981 to 1983 he was employed at Intecs Co. System House in Pisa. From November 1983 to February 1989 he was a researcher at the Dipartimento di Informatica of the University of Turin. Since March 1989 he is an Associate Professor of Computer Science, currently with the University of Parma. He is the author of several papers dealing mainly with programming languages, in particular logic programming languages and Prolog, and extended unification algorithms. His current research interests are (logic) programming languages with sets and set unification algorithms.     

A suite of abstract domains for static analysis of string values

Strings are widely used in modern programming languages in various scenarios. For instance, strings are used to build up Structured Query Language (SQL) queries that are then executed. Malformed strings may lead to subtle bugs, as well as non‐sanitized strings may raise security issues in an application. For these reasons, the application of static analysis to compute safety properties over string values at compile time is particularly appealing. In this article, we propose a generic approach for the static analysis of string values based on abstract interpretation. In particular, we design a suite of abstract semantics for strings, where each abstract domain tracks a different kind of information. We discuss the trade‐off between efficiency and accuracy when using such domains to catch the properties of interest. In this way, the analysis can be tuned at different levels of precision and efficiency, and it can address specific properties.Copyright © 2013 John Wiley & Sons, Ltd.