Role of phosphoproteomics in the development of personalized cancer therapies

Cell signalling pathways driven by protein and lipid kinases contribute to the onset and progression of virtually all cancer types. Consequently, several inhibitors against these enzymes have clinical utility for the treatment of different forms of cancer. A problem that hampers further development is that not all patients respond equally well to kinase inhibitors and a significant proportion of those that initially respond eventually develop resistance. This review considers how an integrative analysis of kinase signalling may be used to address this issue. Advances in the biophysics of mass spectrometry, in biochemical procedures for phosphopeptide enrichment, and in computational approaches for label-free quantification have contributed to the development of phosphoproteomics workflows compatible with the analysis of clinical material. These developments, together with new bioinformatics tools to derive information on signalling circuitry from phosphoproteomics data, allow investigating kinase networks with unprecedented depth. Phosphoproteomics technology is starting to be used in translational research and, with further developments, such methods may also be able to measure the circuitry of cancer signalling networks in routine clinical assays. This review reflects on how this information could be used to accurately predict the best kinase inhibitor for each individual cancer patient.     

Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks — A Case Study for the Optimal Ordering of Tables

Parameter setting for evolutionary algorithms is still an important issue in evolutionary computation. There are two main approaches to parameter setting: parameter tuning and parameter control. In this paper, we introduce self-adaptive parameter control of a genetic algorithm based on Bayesian network learning and simulation. The nodes of this Bayesian network are genetic algorithm parameters to be controlled. Its structure captures probabilistic conditional (in)dependence relationships between the parameters. They are learned from the best individuals, i.e., the best configurations of the genetic algorithm. Individuals are evaluated by running the genetic algorithm for the respective parameter configuration. Since all these runs are time-consuming tasks, each genetic algorithm uses a small-sized population and is stopped before convergence. In this way promising individuals should not be lost. Experiments with an optimal search problem for simultaneous row and column orderings yield the same optima as state-of-the-art methods but with a sharp reduction in computational time. Moreover, our approach can cope with as yet unsolved high-dimensional problems.     

Robustness analysis of discrete predictor-based controllers for input-delay systems

In this article, robustness to model uncertainties are analysed in the context of discrete predictor-based state-feedback controllers for discrete-time input-delay systems with time-varying delay, in an LMI framework. The goal is comparing robustness of predictor-based strategies with respect to other (sub)optimal state feedback ones. A numerical example illustrates that improvements in tolerance to modelling errors can be achieved by using the predictor framework.     

Tuning of PID controllers based on simplified single parameter optimisation

A simple method to design PID controllers in the frequency domain based on a simplified constrained optimisation is proposed. The method is based on the use of a single tuning parameter, defined as the quotient between the final crossover frequency and the zero of the controller. The tuning procedure is based on the maximisation of the controller gain subject to an equality constraint in the phase margin and an inequality constraint in the gain margin. The main advantage of the proposed method is that, even though the maximisation of the controller gain is straightforward, since there is only one parameter to be tuned, the solution is close to the optimal tuning obtained with direct numerical optimisation methods. Moreover the method is applicable to any linear model structure, including dead time and non-minimum phase systems.     

Potential effects in multi-resolution post-classification change detection

Change detection is one of the primary applications of remote-sensing data, and many techniques have been developed during the past three decades. Although frequently criticized and despite many alternatives, due to its simplicity and intuitive manner, post-classification change detection still remains one of the most applied techniques. Many studies in the field of change detection analysis acknowledge, for instance, the impact of misregistration, inconsistencies in classification schemes or differences in methods for image interpretation. However, there are additional, rarely studied influences that can cause large errors in change detection results, including integrating multi-resolution data, the adjacency effect and the minimum mapping units (MMUs) that are applied to the classification before change detection. This study demonstrates these effects for the complex land cover of the Alvarado mangrove area at the Mexican Gulf Coast, employing 10 m Système Pour l'Observation de la Terre 5 (SPOT-5) high geometric resolution (HRG)‐based and 57 m Landsat Multispectral Scanner (MSS) classifications. As a baseline, the proportion of the fine spatial resolution classes within the coarse spatial resolution cells were derived, from which proportional change matrices were computed. The analysis employs difference measures to compare change matrices and proportional maps. The impact of various tested resampling functions was negligible if coarse resolution data were refined. For coarsening fine spatial resolution data, change matrix comparison was comparatively small but yielded differences of approximately 20% in spatially explicit measures. Incorrect positional alignment indicated differences of up to 5% in the change matrix for a misregistration of 100 m and even higher spatially explicit differences (28%). The discrepancies due to the adjacency effect were rather low. MMUs of 25 ha resulted in differences of up to 36% in the change matrix. The magnitude of the discrepancies of all studied effects depends on the class diversity in the map, and some can also be related to the difference in spatial resolution.     

Genetic algorithm for S-transform optimisation in the analysis and classification of electrical signal perturbations

At present, many analytical methods are used for the analysis, detection and classification of electrical signal perturbations. One of these methods, the S-transform, has proven effective under specific conditions for acquiring information and parameters of interest associated with a signal. However, depending on the nature of the signal and the input parameters, this method offers different results that sometimes negatively impact the quality of information obtained in the time and frequency domains.This paper describes the design of a genetic algorithm that optimises the S-transform for analysis and classification of the perturbations in electrical signals. This algorithm provides the best parameter values for optimising the Gaussian window, maximising the precision obtained with regard to classification and, later, analysis (via other techniques, such as neural networks or rule-based systems).This paper demonstrates the effectiveness of the S-transform (specified herein) with respect to the original S-transform and reports the best values obtained after optimisation via a comparative study that includes both typical cases and perturbations in modern electrical systems.     

A Qualitative Study of Hard Decision Making in Managing Global Software Development Teams

We empirically explored the implication of hard decisions in the context of the management of global software development (GSD) teams. We hypothesized and empirically tested our belief that hard decision making in conventional development projects is very similar to GSD and differences arise from the nature of the relationship and the ranking of issues. Findings from a Delphi study illustrated our approach and shed some light into the management of GSD teams.     

Evaluation of candidate biocides to control the biofouling Asian clam in the drinking water treatment industry: An environmentally friendly approach

The biofouling Asian clam Corbicula fluminea greatly affects freshwater-dependent facilities in Europe and North America, including in the Great Lakes region. As chlorination has become increasingly restricted, finding alternative control agents is a priority. Due to the species' epifaunal nature, the body of knowledge on Dreissena polymorpha is larger than that on the Asian clam, and there is a tendency to assume that mitigation methods should work similarly for both species. However, this generalisation is inaccurate, and the optimisation of Asian clam control relies on species-specific toxicological data. This paper reports information on the potential of three candidate biocides for C. fluminea control: (i) the cationic polydiallyldimethylammonium chloride (polyDADMAC), (ii) potassium chloride and (iii) aluminum sulphate. While these chemicals may be employed in a range of contexts, they are particularly suitable for the highly regulated drinking water industry. LT₅₀ values ranging from 284.3 h, for polyDADMAC applied at 10 mg/L, to 855.1 h, for an aluminum sulphate concentration of 11 mg/L, were obtained. Ecotoxicological standard tests with Pseudokirchneriella subcapitata and Daphnia magna suggested that, amongst the three biocides, potassium chloride is the one representing lower potential environmental hazard (with, for example, 48 h-EC₅₀ higher than 1 g/L for Daphnia immobilisation, which compares to values of 20.2 and 112.5 mg/L for polyDADMAC and aluminum sulphate, respectively). The three chemicals are promising control agents at dosages compatible with waterworks' operational requirements with polyDADMAC having the highest biocidal activity, but also posing more environmental risks.     

Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices

This paper addresses challenges relating to applying data mining techniques to detect stock price manipulations and extends previous results by incorporating the analysis of intraday trade prices in addition to closing prices for the investigation of trade-based manipulations. In particular, this work extends previous results on the topic by analysing empirical evidence in normal and manipulated hourly data and the particular characteristics of intraday trades within suspicious hours. Furthermore, the analytical models described in this paper reinforce the results of previous market manipulation studies that are based on traditional statistical and econometrical methods providing an alternative portfolio of methods and techniques originating from the data mining and knowledge discovery areas. With the application of the analytical approach described in this paper, it is possible to identify new fraud manipulation pattern characteristics encoded as decision trees which can be readily employed in fraud detection systems. The paper also proposes a number of policy recommendations towards increasing the effectiveness of the operational processes executed by stock exchange fraud departments and regulatory authorities.