Machine Learning Methods for Attack Detection in the Smart Grid

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semi-supervised) are employed with decision and feature level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than the attack detection algorithms which employ state vector estimation methods in the proposed attack detection framework.Comment: 14 pages, 11 Figure

The Discovery of a Potent, Selective, and Peripherally Restricted Pan-Trk Inhibitor (PF-06273340) for the Treatment of Pain

The neurotrophin family of growth factors, comprised of nerve growth factor (NGF), brain derived neurotrophic factor (BDNF), neurotrophin 3 (NT3), and neurotrophin 4 (NT4), is implicated in the physiology of chronic pain. Given the clinical efficacy of anti-NGF monoclonal antibody (mAb) therapies, there is significant interest in the development of small molecule modulators of neurotrophin activity. Neurotrophins signal through the tropomyosin related kinase (Trk) family of tyrosine kinase receptors, hence Trk kinase inhibition represents a potentially “druggable” point of intervention. To deliver the safety profile required for chronic, nonlife threatening pain indications, highly kinase-selective Trk inhibitors with minimal brain availability are sought. Herein we describe how the use of SBDD, 2D QSAR models, and matched molecular pair data in compound design enabled the delivery of the highly potent, kinase-selective, and peripherally restricted clinical candidate PF-06273340

How do tsetse recognise their hosts? The role of shape in the responses of tsetse (Glossina fuscipes and G. palpalis) to artificial hosts

Palpalis-group tsetse, particularly the subspecies of Glossina palpalis and G. fuscipes, are the most important transmitters of human African trypanomiasis (HAT), transmitting .95% of cases. Traps and insecticide-treated targets are used to control tsetse but more cost-effective baits might be developed through a better understanding of the fly’s host-seeking behaviour.Electrocuting grids were used to assess the numbers of G. palpalis palpalis and G. fuscipes quanzensis attracted to and landing on square or oblong targets of black cloth varying in size from 0.01 m2 to 1.0 m2. For both species, increasing the size of a square target from 0.01 m2 (dimensions = 0.1 x 0.1 m) to 1.0 m2 (1.0 x 1.0 m) increased the catch ,4x however the numbers of tsetse killed per unit area of target declined with target size suggesting that the most cost efficient targets are not the largest. For G. f. quanzensis, horizontal oblongs, (1 m wide x 0.5 m high) caught, 1.8x more tsetse than vertical ones (0.5 m wide x 1.0 m high) but the opposite applied for G. p. palpalis. Shape preference was consistent over the range of target sizes. For G. p. palpalis square targets caught as many tsetse as the oblong; while the evidence is less strong the same appears to apply to G. f. quanzensis. The results suggest that targets used to control G. p. palpalis and G. f. quanzensis should be square, and that the most cost-effective designs, as judged by the numbers of tsetse caught per area of target, are likely to be in the region of 0.25 x 0.25 m2. The preference of G. p. palpalis for vertical oblongs is unique amongst tsetse species, and it is suggested that this response might be related to its anthropophagic behaviour and hence importance as a vector of HAT

The Splicing Efficiency of Activating HRAS Mutations Can Determine Costello Syndrome Phenotype and Frequency in Cancer

Costello syndrome (CS) may be caused by activating mutations in codon 12/13 of the HRAS proto-oncogene. HRAS p.Gly12Val mutations have the highest transforming activity, are very frequent in cancers, but very rare in CS, where they are reported to cause a severe, early lethal, phenotype. We identified an unusual, new germline p.Gly12Val mutation, c.35_36GC>TG, in a 12-year-old boy with attenuated CS. Analysis of his HRAS cDNA showed high levels of exon 2 skipping. Using wild type and mutant HRAS minigenes, we confirmed that c.35_36GC>TG results in exon 2 skipping by simultaneously disrupting the function of a critical Exonic Splicing Enhancer (ESE) and creation of an Exonic Splicing Silencer (ESS). We show that this vulnerability of HRAS exon 2 is caused by a weak 3' splice site, which makes exon 2 inclusion dependent on binding of splicing stimulatory proteins, like SRSF2, to the critical ESE. Because the majority of cancer- and CS- causing mutations are located here, they affect splicing differently. Therefore, our results also demonstrate that the phenotype in CS and somatic cancers is not only determined by the different transforming potentials of mutant HRAS proteins, but also by the efficiency of exon 2 inclusion resulting from the different HRAS mutations. Finally, we show that a splice switching oligonucleotide (SSO) that blocks access to the critical ESE causes exon 2 skipping and halts proliferation of cancer cells. This unravels a potential for development of new anti-cancer therapies based on SSO-mediated HRAS exon 2 skipping

Systems consequences of amplicon formation in human breast cancer

Chromosomal structural variations play an important role in determining the transcriptional landscape of human breast cancers. To assess the nature of these structural variations, we analyzed eight breast tumor samples with a focus on regions of gene amplification using mate-pair sequencing of long-insert genomic DNA with matched transcriptome profiling. We found that tandem duplications appear to be early events in tumor evolution, especially in the genesis of amplicons. In a detailed reconstruction of events on chromosome 17, we found large unpaired inversions and deletions connect a tandemly duplicated ERBB2 with neighboring 17q21.3 amplicons while simultaneously deleting the intervening BRCA1 tumor suppressor locus. This series of events appeared to be unusually common when examined in larger genomic data sets of breast cancers albeit using approaches with lesser resolution. Using siRNAs in breast cancer cell lines, we showed that the 17q21.3 amplicon harbored a significant number of weak oncogenes that appeared consistently coamplified in primary tumors. Down-regulation of BRCA1 expression augmented the cell proliferation in ERBB2-transfected human normal mammary epithelial cells. Coamplification of other functionally tested oncogenic elements in other breast tumors examined, such as RIPK2 and MYC on chromosome 8, also parallel these findings. Our analyses suggest that structural variations efficiently orchestrate the gain and loss of cancer gene cassettes that engage many oncogenic pathways simultaneously and that such oncogenic cassettes are favored during the evolution of a cancer.Singapore. Agency for Science, Technology and ResearchNational Science Foundation (U.S.) (East Asia and Pacific Summer Institutes (OISE-1108282)

Spatial filters yield stable features for error-related potentials across conditions

Error-related potentials (ErrP) have been increasingly studied in psychophysical experiments as well as for brain-machine interfacing. In the latter case, the generalisation capabilities of ErrP decoders is a crucial element to avoid frequent recalibration processes, thus increasing their usability. Previous studies have suggested that ErrP signals are rather stable across recording sessions. Also, studies using protocols of serial stimuli presentation show that these potentials do not change significantly with the presentation rate. Here we complement these studies by analysing the decoding generalisation capabilities. Using data from monitoring experiments, we evaluate how much the performance degrades when tested in a condition different than the one the decoder was trained with. Moreover, we compare different spatial filtering techniques to see which preprocessing steps yield less-sensitive features for ErrP decoding