Privacy Preserving Data Mining using Jaya based Genetic Algorithm

Privacy protection has emerged as a key concern in the field of data mining because of the rise in the sharing of sensitive data across networks among organizations, governments, and other parties. For   knowledge extraction from these huge set of data, association rule mining is used to analyze the patterns of data. For a variety of optimization issues encountered in the real world, evolutionary algorithms (EAs) offer efficient solutions. The available EA solutions in the privacy-preserving area are limited to specific issues like cost function evaluation. Here, a JAYA based Genetic algorithm has been proposed for privacy preservation.“JAYA” means victory a word from Sanskrit origin..This algorithm doesn't need any parameters that are specific to it and it moves towards best solution avoiding the worst. Hence the name Jaya. Jaya based genetic algorithm is applied to original dataset of chromosomes. Privacy preservation is achieved by comparing the support of original dataset of chromosome with the simulation output

A Secure IoT-Enabled Machine Learning Framework for Brain Tumor Classification and Prediction Using MR Image Data

Brain tumor identification and classification have improved due to the quick development of medical imaging and machine learning technology. This paper presents two approaches to secure image transmission in the Internet of Things (IoT): a comprehensive approach for brain tumor prediction and classification using a strong IoT infrastructure with cutting-edge machine learning models and a security approach with the implementation of the AES-ECC hybrid model in the MQTT communication protocol for image data encryption and decryption. We make use of a heterogeneous dataset that we sourced from the Kaggle Dataset platform, which includes four different types of MRI scans of brain tumors from 2870 patients. Our proposed methodology starts with the safe acquisition and transfer of MRI images through an IoT protocol infrastructure to a cloud-based platform. CNN, DenseNet, ResNet and G-Net are some of the sophisticated machine learning models that are used to interpret and analyse these pictures. The computer is trained to identify photos of brain tumors into the appropriate groups using all above four models. According to the data, our suggested CNN model performs better than the others, obtaining an amazing 89% accuracy rate. Nonetheless, we want to achieve even greater improvement in forecast precision by utilising ensemble boosting methodologies. Boosting the CNN model with Ada-Boost, Gradient Boost, XG Boost and Cat Boost algorithms aims to maximize prediction performance. We find that the CNN algorithm combined with XG Boost outperforms all other ensemble methods with an amazing accuracy rate of 97%. This encouraging result highlights how combining cutting-edge machine learning algorithms with IoT infrastructure can lead to better brain tumor classification and prognosis. The creation of more precise and effective diagnostic instruments for the identification of brain tumors is one of our study's many implications, one that will ultimately improve patient outcomes and the healthcare industry

Probing the Higgs Field Using Massive Particles as Sources and Detectors

In the Standard Model, all massive elementary particles acquire their masses by coupling to a background Higgs field with a non-zero vacuum expectation value. What is often overlooked is that each massive particle is also a source of the Higgs field. A given particle can in principle shift the mass of a neighboring particle. The mass shift effect goes beyond the usual perturbative Feynman diagram calculations which implicitly assume that the mass of each particle is rigidly fixed. Local mass shifts offer a unique handle on Higgs physics since they do not require the production of on-shell Higgs bosons. We provide theoretical estimates showing that the mass shift effect can be large and measurable, especially near pair threshold, at both the Tevatron and the LHC.Comment: 6 pages, no figures; Version 2 corrects some typographical errors of factors of 2 in equations 14, 17, 18 and 19 (all of the same origin) and mentions a linear collider as an interesting place to test the results of this pape

Itaconate confers tolerance to late NLRP3 inflammasome activation

Itaconate is a unique regulatory metabolite that is induced upon Toll-like receptor (TLR) stimulation in myeloid cells. Here, we demonstrate major inflammatory tolerance and cell death phenotypes associated with itaconate production in activated macrophages. We show that endogenous itaconate is a key regulator of the signal 2 of NLR family pyrin domain containing 3 (NLRP3) inflammasome activation after long lipopolysaccharide (LPS) priming, which establishes tolerance to late NLRP3 inflammasome activation. We show that itaconate acts synergistically with inducible nitric oxide synthase (iNOS) and that the ability of various TLR ligands to establish NLRP3 inflammasome tolerance depends on the pattern of co-expression of IRG1 and iNOS. Mechanistically, itaconate accumulation upon prolonged inflammatory stimulation prevents full caspase-1 activation and processing of gasdermin D, which we demonstrate to be post-translationally modified by endogenous itaconate. Altogether, our data demonstrate that metabolic rewiring in inflammatory macrophages establishes tolerance to NLRP3 inflammasome activation that, if uncontrolled, can result in pyroptotic cell death and tissue damage

A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli

Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as “phenotypic noise.” In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alon

Quantum entanglement and disentanglement of multi-atom systems

We present a review of recent research on quantum entanglement, with special emphasis on entanglement between single atoms, processing of an encoded entanglement and its temporary evolution. Analysis based on the density matrix formalism are described. We give a simple description of the entangling procedure and explore the role of the environment in creation of entanglement and in disentanglement of atomic systems. A particular process we will focus on is spontaneous emission, usually recognized as an irreversible loss of information and entanglement encoded in the internal states of the system. We illustrate some certain circumstances where this irreversible process can in fact induce entanglement between separated systems. We also show how spontaneous emission reveals a competition between the Bell states of a two qubit system that leads to the recently discovered "sudden" features in the temporal evolution of entanglement. An another problem illustrated in details is a deterministic preparation of atoms and atomic ensembles in long-lived stationary squeezed states and entangled cluster states. We then determine how to trigger the evolution of the stable entanglement and also address the issue of a steered evolution of entanglement between desired pairs of qubits that can be achieved simply by varying the parameters of a given system.Comment: Review articl

Anthracycline rechallenge using pegylated liposomal doxorubicin in patients with metastatic breast cancer: a pooled analysis using individual data from four prospective trials

BACKGROUND: The aim of this study was to determine the activity of anthracycline rechallenge using pegylated liposomal doxorubicin (PLD) in patients with metastatic breast cancer (MBC) previously treated with conventional anthracyclines. METHODS: Pooled individual data from four prospective trials were used, and the primary end point of the pooled analysis was clinical benefit rate (CBR). The studies comprised 935 patients, of whom 274 had received PLD in the metastatic setting after prior exposure to conventional anthracyclines (rechallenge population). RESULTS: The majority of patients were heavily pretreated. Previous anthracycline therapy was administered in the adjuvant (14%) or metastatic setting (46%), or both (40%). The overall CBR from rechallenge with PLD was 37.2% (95% CI, 32.4-42.0). In univariate analyses, the CBR was significantly higher in patients with less exposure to prior chemotherapy, in taxane-naive patients, and in patients with a favourable Eastern Cooperative Group performance status of 0 vs 1 vs 2 (53.3 vs 35.5 vs 18.2%; P<0.001). In multivariate analyses, performance status proved to be the only independent predictor of the CBR achieved with PLD rechallenge (P=0.038). There was no statistically significant difference in CBR regarding the setting, cumulative dose of and/or resistance to prior anthracyclines, or time since prior anthracycline administration. CONCLUSION: Anthracycline rechallenge using PLD is effective in patients with MBC who have a favourable performance status, regardless of setting, resistance, cumulative dose or time since prior conventional anthracycline therapy. British Journal of Cancer (2010) 103, 1518-1523. doi:10.1038/sj.bjc.6605961 www.bjcancer.com Published online 26 October 2010 (C) 2010 Cancer Research U

Calibration of the Logarithmic-Periodic Dipole Antenna (LPDA) Radio Stations at the Pierre Auger Observatory using an Octocopter

An in-situ calibration of a logarithmic periodic dipole antenna with a frequency coverage of 30 MHz to 80 MHz is performed. Such antennas are part of a radio station system used for detection of cosmic ray induced air showers at the Engineering Radio Array of the Pierre Auger Observatory, the so-called Auger Engineering Radio Array (AERA). The directional and frequency characteristics of the broadband antenna are investigated using a remotely piloted aircraft (RPA) carrying a small transmitting antenna. The antenna sensitivity is described by the vector effective length relating the measured voltage with the electric-field components perpendicular to the incoming signal direction. The horizontal and meridional components are determined with an overall uncertainty of 7.4^{+0.9}_{-0.3} % and 10.3^{+2.8}_{-1.7} % respectively. The measurement is used to correct a simulated response of the frequency and directional response of the antenna. In addition, the influence of the ground conductivity and permittivity on the antenna response is simulated. Both have a negligible influence given the ground conditions measured at the detector site. The overall uncertainties of the vector effective length components result in an uncertainty of 8.8^{+2.1}_{-1.3} % in the square root of the energy fluence for incoming signal directions with zenith angles smaller than 60{\deg}.Comment: Published version. Updated online abstract only. Manuscript is unchanged with respect to v2. 39 pages, 15 figures, 2 table

Is Body Fat a Predictor of Race Time in Female Long-Distance Inline Skaters?

Purpose: The aim of this study was to evaluate predictor variables of race time in female ultra-endurance inliners in the longest inline race in Europe. Methods: We investigated the association between anthropometric and training characteristics and race time for 16 female ultraendurance inline skaters, at the longest inline marathon in Europe, the ‘Inline One-eleven’ over 111 km in Switzerland, using bi- and multivariate analysis. Results: The mean (SD) race time was 289.7 (54.6) min. The bivariate analysis showed that body height (r=0.61), length of leg (r=0.61), number of weekly inline skating training sessions (r=-0.51)and duration of each training unit (r=0.61) were significantly correlated with race time. Stepwise multiple regressions revealed that body height, duration of each training unit, and age were the best variables to predict race time. Conclusion: Race time in ultra-endurance inline races such as the ‘Inline One-eleven’ over 111 km might be predicted by the following equation (r2 = 0.65): Race time (min) = -691.62 + 521.71 (body height, m) + 0.58 (duration of each training unit, min) + 1.78 (age, yrs) for female ultra-endurance inline skaters