Surveillance after colorectal cancer surgery

Early diagnosis of local and distant recurrences of colorectal cancer remains difficult and there is no agreement on the effectiveness of follow-up in these patients. The aim of this study is to assess the value of our method of follow-up. We consider 239 patients with colorectal cancer and at least 2 years follow-up following radical resection. A local recurrence appeared in 26 patients (10.9%), a distant metastasis in 41 (17.1%), while in seven (2.9%) local and distant recurrences appeared simultaneously. Local recurrence was detected because of an increase in carcinoembryonic antigen (CEA) level in 15 patients (57.7%), during a scheduled endoscopy in four (15.4%) and because of symptoms in seven (26.9%). In seven patients (26.9%) a radical resection was possible. Distant metastases were detected by CEA levels in 20 patients (48.8%), by ultrasonography (U.S.) in 12 (29.3%) and by chest X-ray in five (12.2%). In 13 of 26 patients with liver metastases a resection was performed. This study shows that few patients benefit from follow-up and only CEA levels and liver U.S. performed intensively between 15 and 36 months after surgery are useful in early detection of recurrences. A modification of the follow-up to the single patient, according to the stage, location and grading of cancer, could improve the results, so lowering the costs of this expensive practice.     

Selective interactions among the multiple connexin proteins expressed in the vertebrate lens: the second extracellular domain is a determinant of compatibility between connexins

Gap junctions are collections of intercellular channels composed of structural proteins called connexins (Cx). We have examined the functional interactions of the three rodent connexins present in the lens, Cx43, Cx46, and Cx50, by expressing them in paired Xenopus oocytes. Homotypic channels containing Cx43, Cx46, or Cx50 all developed high conductance. heterotypic channels composed of Cx46 paired with either Cx43 or Cx50 were also well coupled, whereas Cx50 did not form functional channels with Cx43. We also examined the functional response of homotypic and heterotypic channels to transjunctional voltage and cytoplasmic acidification. We show that all lens connexins exhibited sensitivity to cytoplasmic acidification as well as to voltage, and that voltage-dependent closure of heterotypic channels for a given connexin was dramatically influenced by its partner connexins in the adjacent cell. Based on the observation that Cx43 can discriminate between Cx46 and Cx50, we investigated the molecular determinants that specify compatibility by constructing chimeric connexins from portions of Cx46 and Cx50 and testing them for their ability to form channels with Cx43. When the second extracellular (E2) domain in Cx46 was replaced with the E2 of Cx50, the resulting chimera could no longer form heterotypic channels with Cx43. A reciprocal chimera, where the E2 of Cx46 was inserted into Cx50, acquired the ability to functionally interact with Cx43. Together, these results demonstrate that formation of intercellular channels is a selective process dependent on the identity of the connexins expressed in adjacent cells, and that the second extracellular domain is a determinant of heterotypic compatibility between connexins.     

Effect of mixing leaves with olives on organoleptic quality of oil obtained by centrifugation

Variable quantities of leaves were added to ripe olives prior to extraction to determine their effect on the quality of the resulting olive oil. The addition of 1–3% w/w leaves improved the organoleptic quality. An increase intrans-2-hexenal aldehyde content occurred, giving the pleasant flavor of “freshly cut grass.” This compound was produced by the enzymatic breakdown of 13-L-hydroperoxide of linolenic acid in leaf homogenates.     

Requirement-oriented risk management for incremental software development

In incremental software development (ISD) functionalities are delivered incrementally and requirements keep on evolving across iterations. The requirements evolution involves the addition of new dependencies and conflicts among functional and non-functional requirements along with changes in priorities and dependency weights. This, in turn, demands refactoring the order of development of system components to minimize the impact of these changes. Neglecting the non-functional constraints in the software development process exposes it to risks that may accumulate across several iterations. In this research work, we propose a risk management framework for ISD processes that provides an estimate of risk exposure for the project when functional features are frozen while ignoring the associations with non-functional requirements. Our framework proposes suitable risk reduction strategies that work in tandem with the risk assessment module. We also provide a tool interface for our risk management framework.

     

A single pass algorithm for clustering evolving data streams based on swarm intelligence

Existing density-based data stream clustering algorithms use a two-phase scheme approach consisting of an online phase, in which raw data is processed to gather summary statistics, and an offline phase that generates the clusters by using the summary data. In this article we propose a data stream clustering method based on a multi-agent system that uses a decentralized bottom-up self-organizing strategy to group similar data points. Data points are associated with agents and deployed onto a 2D space, to work simultaneously by applying a heuristic strategy based on a bio-inspired model, known as flocking model. Agents move onto the space for a fixed time and, when they encounter other agents into a predefined visibility range, they can decide to form a flock if they are similar. Flocks can join to form swarms of similar groups. This strategy allows to merge the two phases of density-based approaches and thus to avoid the computing demanding offline cluster computation, since a swarm represents a cluster. Experimental results show that the bio-inspired approach can obtain very good results on real and synthetic data sets.     

Landscape-aware location-privacy protection in location-based services

Mobile network providers have developed a variety of location-based services (LBSs), such as friend-finder, point of interest services, emergency rescue and many other safety and security services. The protection of location-privacy has consequently become a key aspect to the success of LBSs, since users consider their own physical location and movements highly privacy-sensitive, and demand for solutions able to protect such an information in a variety of environments. The idea behind location-privacy protection is that the individual should be able to set the level at which the location information is released to avoid undesired exploitation by a potential attacker: one of the approaches to this problem is given by the application of spatial obfuscation techniques, actuated by a trusted agent, and consisting in artificial perturbations of the location information collected by sensing technologies, before its disclosure to third parties. In many situations, however, landscape/map information can help a third party to perform Bayesian inference over spatially obfuscated data and to refine the user’s location estimate up to a violation of the original user’s location-privacy requirements. The goal of this paper is to provide a map-dependent obfuscation procedure that enables the release of the maximum possible user’s location information, that does not lead to a violation of the original user’s location-privacy requirements, even when refined through map-based inference.     

Real time identification of discrete event systems using Petri nets

The paper defines the identification problem for Discrete Event Systems (DES) as the problem of inferring a Petri Net () model using the observation of the events and the available output vectors, that correspond to the markings of the measurable places. Two cases are studied considering different levels of the system knowledge. In the first case the place and transition sets are assumed known. Hence, an integer linear programming problem is defined in order to determine a modelling the DES. In the second case the transition and place sets are assumed unknown and only an upper bound of the number of places is given. Hence, the identification problem is solved by an identification algorithm that observes in real time the occurred events and the corresponding output vectors. The integer linear programming problem is defined at each observation so that the can be recursively identified. Some results and examples characterize the identified systems and show the flexibility and simplicity of the proposed technique. Moreover, an application to the synthesis of supervisory control of systems via monitor places is proposed.     

A convex optimization approach to filtering in jump linear systems with state dependent transitions

We introduce a new methodology to construct a Gaussian mixture approximation to the true filter density in hybrid Markovian switching systems. We relax the assumption that the mode transition process is a Markov chain and allow it to depend on the actual and unobservable state of the system. The main feature of the method is that the Gaussian densities used in the approximation are selected as the solution of a convex programming problem which trades off sparsity of the solution with goodness of fit. A meaningful example shows that the proposed method can outperform the widely used interacting multiple model (IMM) filter and GPB2 in terms of accuracy at the expenses of an increase in computational time.     

The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas

Remote sensing hyperspectral sensors are important and powerful instruments for addressing classification problems in complex forest scenarios, as they allow one a detailed characterization of the spectral behavior of the considered information classes. However, the processing of hyperspectral data is particularly complex both from a theoretical viewpoint [e.g. problems related to the Hughes phenomenon (Hughes, 1968) and from a computational perspective. Despite many previous investigations that have been presented in the literature on feature reduction and feature extraction in hyperspectral data, only a few studies have analyzed the role of spectral resolution on the classification accuracy in different application domains. In this paper, we present an empirical study aimed at understanding the relationship among spectral resolution, classifier complexity, and classification accuracy obtained with hyperspectral sensors for the classification of forest areas. We considered two different test sets characterized by images acquired by an AISA Eagle sensor over 126 bands with a spectral resolution of 4.6 nm, and we subsequently degraded its spectral resolution to 9.2, 13.8, 18.4, 23, 27.6, 32.2 and 36.8 nm. A series of classification experiments were carried out with bands at each of the degraded spectral resolutions, and bands selected with a feature selection algorithm at the highest spectral resolution (4.6 nm). The classification experiments were carried out with three different classifiers: Support Vector Machine, Gaussian Maximum Likelihood with Leave-One-Out-Covariance estimator, and Linear Discriminant Analysis. From the experimental results, important conclusions can be made about the choice of the spectral resolution of hyperspectral sensors as applied to forest areas, also in relation to the complexity of the adopted classification methodology. The outcome of these experiments are also applicable in terms of directing the user towards a more efficient use of the current instruments (e.g. programming of the spectral channels to be acquired) and classification techniques in forest applications, as well as in the design of future hyperspectral sensors.