Special issue on computational intelligence algorithms and applications

Ye

Phosphatidylinositol (4,5)-bisphosphate turnover by INP51 regulates the cell wall integrity pathway in "Saccharomyces cerevisiae"

Signal transduction pathways are important for the cell to transduce external or internal stimuli where second messengers play an important role as mediators of the stimuli. One important group of second messengers are the phosphoinositide family present in organisms ranging from yeast to mammals. The dephosphorylation and phosphorylation cycle of the phosphatidylinositol species are thought to be important in signaling for recruitment or activation of proteins involved in vesicular transport and/or to control the organization of the actin cytoskeleton. In mammals, phosphatidylinositol (4,5)bisphosphate (PI(4,5)P2) signaling is essential and regulated by various kinases and phosphatases. In the model organism Saccharomyces cerevisiae PI(4,5)P2 signaling is also essential but the regulation remains unclear. My dissertation focuses on the regulation of PI(4,5)P2 signaling in Saccharomyces cerevisiae. The organization of the actin cytoskeleton in Saccharomyces cerevisiae is regulated by different proteins such as calmodulin, CMD1, and here I present data that CMD1 plays a role in the regulation of the only phosphatidylinositol 4-phosphate 5-kinase, MSS4, in Saccharomyces cerevisiae. CMD1 regulates MSS4 activity through an unknown mechanism and thereby controls the organization of the actin cytoskeleton. MSS4 and CMD1 do not physically interact but MSS4 seems to be part of a large molecular weight complex as shown by gel filtration chromatography. This complex could contain regulators of the MSS4 activity. The complex is not caused by dimerization of MSS4 since MSS4 does not interact with itself. Two pathways, the cell wall integrity pathway and TORC2 (target of rapamycin complex 2) signaling cascade are important for the organization of the actin cytoskeleton. Loss of TOR2 function results in a growth defect that can be suppressed by MSS4 overexpression. To further characterize the link between MSS4 and the TORC2 signaling pathway and the cell wall integrity pathway we looked for targets of PI(4,5)P2. The TORC2 pathway and the cell wall integrity pathway signal to the GEF ROM2, an activator of the small GTPase RHO1. In our study we identified ROM2 as a target of PI(4,5)P2 signaling. We observed that the ROM2 localization changes in an mss4 conditional mutant. This suggests that the proper localization needs PI(4,5)P2. This could be mediated by the putative PI(4,5)P2 binding pleckstrin homology (PH) domain of ROM2. To better understand the regulation of PI(4,5)P2 levels in Saccharomyces cerevisiae we focused on one of the PI(4,5)P2 5-phosphatases, INP51. Here we present evidence that INP51 is a new negative regulator of the cell wall integrity pathway as well as the TORC2 pathway. INP51 probably regulates these two pathways by the turnover of PI(4,5)P2 thereby inactivating the effector/s. The deletion of INP51 does not result in any phenotype, but when combined with mutations of the cell wall integrity pathway we observe synthetic interaction. INP51 together with the GTPase activating protein (GAP) SAC7, responsible for the negative regulation of RHO1, negatively regulates the cell wall integrity pathway during vegetative growth. One of the targets of cell wall integrity pathway, the cell wall component chitin, which is normally deposited at the bud end, bud neck and forms bud scars, is delocalized in the mother cell in the sac7 inp51 double deletion mutant. In addition, another downstream component of the cell wall integrity pathway, the MAP kinase MPK1, has increased phosphorylation and protein level in the sac7 inp51 double deletion mutant. This suggests that INP51 is important for the negative regulation of the cell wall integrity pathway. Furthermore, we show evidence that INP51 forms a complex with TAX4 or IRS4, with two EH-domain containing proteins, that positively regulates the activity of INP51 and in this manner negatively regulate the cell wall integrity pathway. The EH-domain is known to bind the NPF-motif. This motif is present in INP51 and is important for INP51 interaction with TAX4 or IRS4. The EH-NPF interaction is a conserved mechanism to build up protein networks. The interaction between an EH-domain containing protein and a PI(4,5)P2 5-phosphatase is conserved. This is demonstrated by the epidermal growth factor substrate EPS15 (EH) interaction with the PI(4,5)P2 5-phosphatase synaptojanin the mammalian orthologue of the Saccharomyces cerevisiae INP proteins. In summary, INP51 together with TAX4 and IRS4, forms complexes important for regulation of PI(4,5)P2 levels. The complexes are linked to the TORC2 signaling pathway and the cell wall integrity pathway, specifically regulating MPK1 activation and chitin biosynthesis. The work presented in this dissertation facilitates the development of a model of the complex regulation of PI(4,5)P2 signaling in Saccharomyces cerevisiae

Analysis Of A Neuro-Fuzzy Approach Of Air Pollution: Building A Case Study

This work illustrates the necessity of an Artificial Intelligence (AI)-based approach of air quality in urban and industrial areas. Some related results of Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) for environmental data are considered: ANNs are proposed to the problem of short-term predicting of air pollutant concentrations in urban/industrial areas, with a special focus in the south-eastern Romania. The problems of designing a database about air quality in an urban/industrial area are discussed. First results confirm ANNs as an improvement of classical models and show the utility of ANNs in a well built air monitoring center

Cancelling Harmonic Power Line Interference in Biopotentials

Biopotential signals, like the electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), and so on, contain vital information about the health state of human body. The morphology and time/frequency parameters of the biopotentials are of interest when diagnostic information is extracted and analyzed. The powerline interference (PLI), with the fundamental PLI component of 50 Hz/60 Hz and its harmonics, is one of the most disturbing noise sources in biopotential recordings that hampers the analysis of the electrical signals generated by the human body. The aim of this chapter is to review the existing methods to eliminate harmonics PLI from biopotential signals and to analyze the distortion introduced by some of the most basic approaches for PLI cancelation and whether this distortion affects the diagnostic performance in biopotentials investigations

Towards a Data Quality Framework for Heterogeneous Data

yesEvery industry has significant data output as a product of their working process, and with the recent advent of big data mining and integrated data warehousing it is the case for a robust methodology for assessing the quality for sustainable and consistent processing. In this paper a review is conducted on Data Quality (DQ) in multiple domains in order to propose connections between their methodologies. This critical review suggests that within the process of DQ assessment of heterogeneous data sets, not often are they treated as separate types of data in need of an alternate data quality assessment framework. We discuss the need for such a directed DQ framework and the opportunities that are foreseen in this research area and propose to address it through degrees of heterogeneity

Random masking interleaved scrambling technique as a countermeasure for DPA/DEMA attacks in cache memories

Memory remanence in SRAMs and DRAMs is usually exploited through cold-boot attacks and the targets are the main memory and the L2 cache memory. Hence, a sudden power shutdown may give an attacker the opportunity to download the contents of the memory and extract critical data. Side-channel attacks such as differential power or differential electromagnetic analysis have proven to be very effective against memory security. Furthermore, blending cold-boot attacks with DPA or DEMA can overpower even a high-level of security in cache or main memories. In this scope, data scrambling techniques have been explored and employed to improve the security, with a minor penalty in performance. Enforcing security techniques and methods in cache memories is risky because any substantial reduction in the cache memory speed might be devastating to the CPU, which is why the performance penalty must be minimal. In this paper, we introduce an improved scrambling technique which uses random masking of the scrambling vector and it is designed to protect cache memories against cold-boot and differential power or electromagnetic attacks. The technique is analyzed in terms of area, power and speed, while the level of security is evaluated through adversary models and simulated attacks

Deep Quantile Regression for Unsupervised Anomaly Detection in Time-Series

YesTime-series anomaly detection receives increasing research interest given the growing number of data-rich application domains. Recent additions to anomaly detection methods in research literature include deep neural networks (DNNs: e.g., RNN, CNN, and Autoencoder). The nature and performance of these algorithms in sequence analysis enable them to learn hierarchical discriminative features and time-series temporal nature. However, their performance is affected by usually assuming a Gaussian distribution on the prediction error, which is either ranked, or threshold to label data instances as anomalous or not. An exact parametric distribution is often not directly relevant in many applications though. This will potentially produce faulty decisions from false anomaly predictions due to high variations in data interpretation. The expectations are to produce outputs characterized by a level of confidence. Thus, implementations need the Prediction Interval (PI) that quantify the level of uncertainty associated with the DNN point forecasts, which helps in making better-informed decision and mitigates against false anomaly alerts. An effort has been made in reducing false anomaly alerts through the use of quantile regression for identification of anomalies, but it is limited to the use of quantile interval to identify uncertainties in the data. In this paper, an improve time-series anomaly detection method called deep quantile regression anomaly detection (DQR-AD) is proposed. The proposed method go further to used quantile interval (QI) as anomaly score and compare it with threshold to identify anomalous points in time-series data. The tests run of the proposed method on publicly available anomaly benchmark datasets demonstrate its effective performance over other methods that assumed Gaussian distribution on the prediction or reconstruction cost for detection of anomalies. This shows that our method is potentially less sensitive to data distribution than existing approaches.Petroleum Technology Development Fund (PTDF) PhD Scholarship, Nigeria (Award Number: PTDF/ ED/PHD/IAT/884/16

The wild bootstrap resampling in regression imputation algorithm with a Gaussian Mixture Model

YesUnsupervised learning of finite Gaussian mixture model (FGMM) is used to learn the distribution of population data. This paper proposes the use of the wild bootstrapping to create the variability of the imputed data in single miss-ing data imputation. We compare the performance and accuracy of the proposed method in single imputation and multiple imputation from the R-package Amelia II using RMSE, R-squared, MAE and MAPE. The proposed method shows better performance when compared with the multiple imputation (MI) which is indeed known as the golden method of missing data imputation techniques

Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective

YesImmunotherapy treatments can be essential sometimes and a waste of valuable resources in other cases, depending on the diagnosis results. Therefore, researchers in immunotherapy need to be updated on the current status of research by exploring: application domains e.g. warts, datasets e.g. immunotherapy, classifiers or algorithms e.g. kNN and software tools. The research objectives were: 1) to study the immunotherapy-related published literature from a supervised machine learning perspective. In addition, to reproduce immunotherapy classifiers reported in research papers. 2) To find gaps and challenges both in publications and practical work, which may be the basis for further research. Immunotherapy, diabetes, cryotherapy, exasens data and ”one unbalanced dataset” are explored. The results are compared with published literature. To address the found gaps in further research: novel experiments, unbalanced studies, focus on effectiveness and a new classifier algorithm are suggested