Sources and sinks of hydroxyl radicals upon irradiation of natural water samples

Hydroxyl radical formation rates, steady-state concentration, and overall scavenging rate constant were measured by irradiation of surface lake water samples from Piedmont (NW Italy) and nitrate-rich groundwater samples from Moldova (NE Romania). Dissolved organic matter (DOM) was the main source and sink of *OH upon lake water irradiation, with [*OH] being independent of DOM amount. Water oxidation by photoexcited DOM is a likely *OH source in the presence of very low levels of nitrate and dissolved iron. Under different circumstances it is not possible to exclude other processes, e.g., DOM-enhanced photo-Fenton reactions. Under the hypotheses of no interaction and absence of mutual screening of radiation, nitrate would prevail over DOM as *OH source for a NO3-/DOM ratio higher than 3.3 x 10(-5) (mol NO3-) (mg C)(-1), DOM prevailing for lower values. Substantial DOM photolability was observed upon irradiation of nitrate-rich groundwater, mainly due to the elevated *OH generation rate. For the first time to our knowledge, evidence was also obtained of the photoformation of potentially toxic and/or mutagenic nitroaromatic compounds upon irradiation of natural lake water and groundwater samples, proportionally to the nitrate levels.     

Threat Objects Detection in X-ray Images Using an Active Vision Approach

X-ray testing for baggage inspection has been increasingly used at airports, reducing the risk of terrorist crimes and attacks. Nevertheless, this task is still being carried out by human inspectors and with limited technological support. The technology that is being used is not always effective, as it depends mainly on the position of the object of interest, occlusion, and the accumulated experience of the inspector. Due to this problem, we have developed an approach that inspects X-ray images using active vision in order to automatically detect objects that represent a threat. Our method includes three steps: detection of potential threat objects in single views based on the similarity of features and spatial distribution; estimation of the best-next-view using Q-learning; and elimination of false alarms based on multiple view constraints. We tested our algorithm on X-ray images that included handguns and razor blades. In the detection of handguns we registered good results for recall and precision (Re = 67%, Pr = 83%) along with a high performance in the detection of razor blades (Re = 82%, Pr = 100%) taking into consideration 360 inspections in each case. Our results indicate that non-destructive inspection actively using X-ray images, leads to more effective object detection in complex environments, and helps to offset certain levels of occlusion and the internal disorder of baggage.     

Targeting AKT in ER-Positive HER2-Negative Metastatic Breast Cancer: From Molecular Promises to Real Life Pitfalls?

The AKT protein kinase plays a central role in several interconnected molecular pathways involved in growth, apoptosis, angiogenesis, and cell metabolism. It thereby represents a therapeutic target, especially in hormone receptor-positive (HR) breast cancers, where the PI3K/AKT signaling pathway is largely hyperactivated. Moreover, resistance to therapeutic classes, including endocrine therapy, is associated with the constitutive activation of the PI3K/AKT pathway. Improved knowledge on the molecular mechanisms underlying resistance to endocrine therapy has led to the diversification of the therapeutic arsenal, notably with the development of PI3K and mTOR inhibitors, which are currently approved for the treatment of advanced HR-positive breast cancer patients. AKT itself constitutes a novel pharmacological target for which AKT inhibitors have been developed and tested in clinical trials. However, despite its pivotal role in cell survival and anti-apoptotic mechanisms, as well as in endocrine therapy resistance, few drugs have been developed and are available for clinical practice. The scope of the present review is to focus on the pivotal role of AKT in metastatic breast cancer through the analysis of its molecular features and to discuss clinical implications and remaining challenges in the treatment of HR-positive metastatic breast cancer.     

A novel online self-learning system with automatic object detection model for multimedia applications

Multimedia Tools and Applications - This paper proposes a novel online self-learning detection system for different types of objects. It allows users to random select detection target, generating...     

Prediction of Mechanical Properties of Corn and Tortilla Chips by Using Computer Vision

Deep-fat frying is a unit operation which develops unique sensorial attributes in foods. For instance, texture is the principal quality parameter of tortilla and corn chips. On the other hand, computer vision is a useful tool for quality evaluation and prediction of some physical properties in different either raw or processed foods. The objective of this research was to characterize corn and tortilla chips by using computer vision, and to build proper mathematical models which permit to predict mechanical properties of these chips (maximum force, such as hardness, and distance to maximum force, such as toughness) by using chromatic features extracted from their corresponding digital images. Corn and tortilla chips (thickness of 2 mm; diameter of 37 mm) were made from masa of maize and fried at constant oil temperatures of 160, 175, and 190 °C. A high linear correlation (R ² > 0.9400) was obtained between mechanical properties and some image features (Hu, Fourier, and Haralick moments). Cross-validation technique demonstrated the repeatability and good performance (>90%) of the models tested, indicating that can be used to predict the textural properties of the tortilla and corn chips by using selected features extracted from their digital images, without the necessity of measuring them in a texture analyzer.     

Effect of CSN1S1-CSN3 (α(S1)-κ-casein) composite genotype on milk production traits and milk coagulation properties in Mediterranean water buffalo

The aim of this study was to estimate effects of CSN1S1-CSN3 (α_S1-κ-casein) composite genotypes on milk production traits and milk coagulation properties (MCP) in Mediterranean water buffalo. Genotypes at CSN1S1 and CSN3 and coagulation properties [rennet clotting time (RCT), curd firming time (K₂₀), and curd firmness (A₃₀)] were assessed by reversed-phase HPLC and computerized renneting meter analysis, respectively, using single test-day milk samples of 536 animals. Alternative protein variants of α_S1-CN and κ-CN were detected by HPLC, and identification of the corresponding genetic variants was carried out by DNA analysis. Two genetic variants were detected at CSN1S1 (A and B variants) and 2 at CSN3 (X1 and X2 variants). Statistical inference was based on a linear model including the CSN1S1-CSN3 composite genotype effect (7 genotypes), the effects of herd-test-day (8 levels), and a combined days in milk (DIM)-parity class. Composite genotype AB-X2X2 was associated with decreased test-day milk yield [?0.21 standard deviation (SD) units of the trait] relative to genotype BB-X2X2. Genotypes did not affect milk protein content, but genotype AB-X1X1 was associated with increased fat content compared with genotype BB-X2X2 (+0.28 SD units of the trait) and AB-X1X1 (+0.43 SD units of the trait). For RCT, the largest difference (+1.91 min; i.e., 0.61 SD units of the trait) was observed between genotype AA-X1X2 and AB-X1X1. Direction of genotype effects on K₂₀ was consistent with that for RCT. The maximum variation in K₂₀ due to genotype effects (between AA-X1X2 and AB-X1X1 genotypes) was almost 0.9 SD units of the trait. Magnitude of genotype effects was smaller for A₃₀ than for RCT and K₂₀, with a maximum difference of 0.5 SD units of the trait between genotype AA-X1X2 and AA-X1X1. The B allele at CSN1S1 was associated with increased RCT and K₂₀ and with weaker curds compared with allele A. Allele X2 at CSN3 exerted opposite effects on MCP relative to CSN1S1 B. Because of linkage disequilibrium, allele B at CSN1S1 and allele X2 at CSN3 tend to be associated and this likely makes their effects cancel each other. This study indicates a role for casein genes in variation of MCP of buffalo milk. Further studies are necessary to estimate the effects of casein genetic variants on variation of cheese yield.     

Correlations between biological activity and structural properties for two short homologous sequences in thymosin beta4 and gelsolin

Gelsolin and thymosin beta4 appear to be two important actin-associated proteins involved in the regulation of actin polymerization. It has been widely demonstrated that thymosin is the major cellular actin-sequestering factor shifting the polymerization equilibrium of actin towards a monomeric state. At the same time gelsolin, a Ca2+ and inositol phosphate sensitive protein, regulates actin filament length. The interactions of these two proteins with actin are rather complex and require the participation of several complementary peptide sequences. We have identified a common motif, (I, V)EKFD, in the two proteins in the functional sequences so far examined. Gelsolin- and thymosin beta4-related peptides including the common motif were synthesized and their structural and functional properties studied. These two sequences exert a major inhibitory effect on salt-induced actin polymerization. We used circular dichroism and Fourier-transform infrared spectroscopy to show that the two synthetic peptides present some secondary structure in solution. As far as the peptide derived from the thymosin sequence was concerned, alpha-helical structure was induced by trifluoroethanol as observed with the full-length molecule. These experiments underscore the importance of the conformational state of peptide fragments in their biological activities. ELISA and fluorescence measurements have been used to identify the binding regions of these fragments to a C-terminal region (subdomain 1) of the actin sequence. Our results also emphasize the relationship between the propensity of small sequences to form secondary structures and their propensity for biological activity as related to actin interaction and inhibition of actin polymerization.     

Indoor Mobile Robotics at Grima, PUC

This paper describes the main activities and achievements of our research group on Machine Intelligence and Robotics (Grima) at the Computer Science Department, Pontificia Universidad Catolica de Chile (PUC). Since 2002, we have been developing an active research in the area of indoor autonomous social robots. Our main focus has been the cognitive side of Robotics, where we have developed algorithms for autonomous navigation using wheeled robots, scene recognition using vision and 3D range sensors, and social behaviors using Markov Decision Processes, among others. As a distinguishing feature, in our research we have followed a probabilistic approach, deeply rooted in machine learning and Bayesian statistical techniques. Among our main achievements are an increasing list of publications in main Robotics conference and journals, and the consolidation of a research group with more than 25 people among full-time professors, visiting researchers, and graduate students.     

Towards a Type-Theoretical Account of Lexical Semantics

After a quick overview of the field of study known as “Lexical Semantics”, where we advocate the need of accessing additional information besides syntax and Montague-style semantics at the lexical level in order to complete the full analysis of an utterance, we summarize the current formulations of a well-known theory of that field. We then propose and justify our own model of the Generative Lexicon Theory, based upon a variation of classical compositional semantics, and outline its formalization. Additionally, we discuss the theoretical place of informational, knowledge-related data supposed to exist within the lexicon as well as within discourse and other linguistic constructs. The formalization of the structure of natural language utterances around a surface form (phenogrammatics), a deep structure (tectogrammatics) and the meaning thereof as a logical form (semantics) has developed from the original theories of Curry and Montague to form coherent, type-driven models. Most of these new theories rely upon variations of the compositional analysis of the sentence: from pheno to tectogrammatics, and then to semantics. Our contribution to this work aims at giving such a model a means to overcome the problems posed by polysemous lexical units during the semantical analysis of the tectogrammatical form. Building upon an assumed “deep structure”, we formalize parts of Pustejovsky’s Generative Lexicon Theory, linguistically motivated in Pustejovsky (The generative lexicon, MIT Press, Cambridge, MA, 1995), in a pre-processing of the semantics of the sentence. The mechanisms of Lexical Semantics we propose are an additional layer of classical Montague compositional semantics, and, as such, integrate smoothly within such an analysis; we proceed by converting the lexical data to modifiers of the logical form. This treatment of Lexical Semantics furthermore induces us to think that some sort of non-evident background knowledge of the common use of words is necessary to perform a correct semantic analysis of an utterance. This “commonsense metaphysics” would therefore not be strictly confined to pragmatics, as is often assumed.