Antibacterial activity of Eucalpytus citriodora Hk. oil on few clinically important bacteria

The antibacterial activity of Eucalyptus citriodora oil was evaluated. The volatile oil was extracted by steam distillation method. The tested bacterial strains were Escherichia coli ATCC 25922, Staphylococcus aureus, Proteus mirabilis NCIM2241, Pseudomonas aeruginosa ATCC27853, Proteus vulgaris NCTC8313, Salmonella typhimurium, Enterobacter aerogenes ATCC13048, Pseudomonas testosteroni NCIM 5098, Alcaligenes fecalis, Bacillus cereus ATCC11778 and Citrobacter freundiiATCC10787. Piperacillin and Amikacin were used as the positive controls. The activity of the oil increased with increase in concentration but decreased after a certain level. The study suggests that isolation of the active compound from oil would give more satisfactory and promising results

Scalar-Induced Compactifications in Higher Dimensional Supergravities

We discuss compactifications of higher dimensional supergravities which are induced by scalars. In particular, we consider vector multiplets coupled to the supergravity multiplet in the case of D=9, 8 and D=7 minimal supergravities. These vector multiplets contain scalars, which parametrize coset spaces of the general form SO(10-D,n)/SO(10-D)xSO(n), where n is the number of vector multiplets. We discuss the compactification of the supergravity theory to D-2 dimensons, which is induced by non-trivial vacuum scalar field configurations. There are singular and non-singular solutions, which preserve half of the supersymmetries.Comment: 25 pages, JHEP

Critical exponents and the correlation length in the charge exchange manganite spin glass Eu_{0.5}Ba_{0.5}MnO_{3}

The critical regime of the charge exchange (CE) manganite spin glass Eu_{0.5}Ba_{0.5}MnO_{3} is investigated using linear and non linear magnetic susceptibility and the divergence of the third ordered susceptibility (chi{_3}) signifying the onset of a conventional freezing transition is experimentally demonstrated. The divergence in chi{_3}, dynamical scaling of the linear susceptibility and relevant scaling equations are used to determine the critical exponents associated with this freezing transition, the values of which match well with the 3D Ising universality class. Magnetic field dependence of the spin glass response function is used to estimate the spin correlation length which is seen to be larger than the charge/orbital correlation length reported in this system.Comment: 4 pages, 4 Figure

PiRaNhA: A server for the computational prediction of RNA-binding residues in protein sequences

The PiRaNhA web server is a publicly available online resource that automatically predicts the location of RNA-binding residues (RBRs) in protein sequences. The goal of functional annotation of sequences in the field of RNA binding is to provide predictions of high accuracy that require only small numbers of targeted mutations for verification. The PiRaNhA server uses a support vector machine (SVM), with position-specific scoring matrices, residue interface propensity, predicted residue accessibility and residue hydrophobicity as features. The server allows the submission of up to 10 protein sequences, and the predictions for each sequence are provided on a web page and via email. The prediction results are provided in sequence format with predicted RBRs highlighted, in text format with the SVM threshold score indicated and as a graph which enables users to quickly identify those residues above any specific SVM threshold. The graph effectively enables the increase or decrease of the false positive rate. When tested on a non-redundant data set of 42 protein sequences not used in training, the PiRaNhA server achieved an accuracy of 85%, specificity of 90% and a Matthews correlation coefficient of 0.41 and outperformed other publicly available servers. The PiRaNhA prediction server is freely available at http://www.bioinformatics.sussex.ac.uk/PIRANHA. © The Author(s) 2010. Published by Oxford University Press

Achieving Fairness in the Stochastic Multi-armed Bandit Problem

We study an interesting variant of the stochastic multi-armed bandit problem, called the Fair-SMAB problem, where each arm is required to be pulled for at least a given fraction of the total available rounds. We investigate the interplay between learning and fairness in terms of a pre-specified vector denoting the fractions of guaranteed pulls. We define a fairness-aware regret, called $r$-Regret, that takes into account the above fairness constraints and naturally extends the conventional notion of regret. Our primary contribution is characterizing a class of Fair-SMAB algorithms by two parameters: the unfairness tolerance and the learning algorithm used as a black-box. We provide a fairness guarantee for this class that holds uniformly over time irrespective of the choice of the learning algorithm. In particular, when the learning algorithm is UCB1, we show that our algorithm achieves $O(\ln T)$ $r$-Regret. Finally, we evaluate the cost of fairness in terms of the conventional notion of regret.Comment: arXiv admin note: substantial text overlap with arXiv:1905.1126