Cyber Inference System for Substation Anomalies Against Alter-and-Hide Attacks

Alarms reported to energy control centers are an indication of abnormal events caused by either weather interruptions, system errors, or possibly intentional anomalies. Although these initiating events are random, e.g., faults on transmission lines struck by lightning, the existence of electronically altered measurements may implicate the process to identify root causes of abnormal events. This paper is concerned with alter-andhide (AaH) attacks by tampering the actual measurements to normal states with the background of disruptive switching actions that hide the true values of local events from operators at the control center. A cyber inference system (CyIS) framework is proposed to synthesize all sequential, missing, or altered alarms of related substations against AaH attacks. The stochastic nature of such attack events is modeled with probabilities as an integer programming problem with multiple scenarios. The proposed method is utilized to verify alarm scenarios for a conclusion of the potential AaH attacks on the substations.postprin

Shape and structure of N=Z 64Ge; Electromagnetic transition rates from the application of the Recoil Distance Method to knock-out reaction

Transition rate measurements are reported for the first and the second 2+ states in N=Z 64Ge. The experimental results are in excellent agreement with large-scale Shell Model calculations applying the recently developed GXPF1A interactions. Theoretical analysis suggests that 64Ge is a collective gamma-soft anharmonic vibrator. The measurement was done using the Recoil Distance Method (RDM) and a unique combination of state-of-the-art instruments at the National Superconducting Cyclotron Laboratory (NSCL). States of interest were populated via an intermediate-energy single-neutron knock-out reaction. RDM studies of knock-out and fragmentation reaction products hold the promise of reaching far from stability and providing lifetime information for excited states in a wide range of nuclei

Whole exome sequencing links dental tumor to an autosomal-dominant mutation in ANO5 gene associated with gnathodiaphyseal dysplasia and muscle dystrophies

Tumors of the jaws may represent different human disorders and frequently associate with pathologic bone fractures. In this report, we analyzed two affected siblings from a family of Russian origin, with a history of dental tumors of the jaws, in correspondence to original clinical diagnosis of cementoma consistent with gigantiform cementoma (GC, OMIM: 137575). Whole exome sequencing revealed the heterozygous missense mutation c.1067G \u3e A (p.Cys356Tyr) in ANO5 gene in these patients. To date, autosomal-dominant mutations have been described in the ANO5 gene for gnathodiaphyseal dysplasia (GDD, OMIM: 166260), and multiple recessive mutations have been described in the gene for muscle dystrophies (OMIM: 613319, 611307); the same amino acid (Cys) at the position 356 is mutated in GDD. These genetic data and similar clinical phenotypes demonstrate that the GC and GDD likely represent the same type of bone pathology. Our data illustrate the significance of mutations in single amino-acid position for particular bone tissue pathology. Modifying role of genetic variations in another gene on the severity of the monogenic trait pathology is also suggested. Finally, we propose the model explaining the tissue-specific manifestation of clinically distant bone and muscle diseases linked to mutations in one gene

Particle-hole excited states in 133 Te

Excited states in neutron-rich ${}^{133}\mathrm{Te}$ have been identified with the Gamma sphere array by measuring three- and higher-fold prompt coincidence events following spontaneous fission of ${}^{252}\mathrm{Cf}.$ Four types of particle-hole bands built on the known 334.3 keV isomer in ${}^{133}\mathrm{Te}$ are identified. The yrast and near yrast particle-hole states observed up to 6.2 MeV in ${}^{133}\mathrm{Te}$ have characteristics quite similar to those in ${}^{134}\mathrm{Te}.$ These states are interpreted as a result of coupling a neutron \ensuremath{\nu}{h}_{11/2} hole to the ${}^{134}\mathrm{Te}$ core. The group of states observed above 5.214 MeV is the result of a neutron particle-hole excitation of the double magic core nucleus ${}^{132}\mathrm{Sn},$ and is a candidate for a tilted rotor band. Shell-model calculations considering ${}^{132}\mathrm{Sn}$ as a closed core have been performed and have provided guidance to the interpretation of the levels below 4.3 MeV. Very good agreement between theory and experiment is obtained for these states

BioInfer: a corpus for information extraction in the biomedical domain

BACKGROUND: Lately, there has been a great interest in the application of information extraction methods to the biomedical domain, in particular, to the extraction of relationships of genes, proteins, and RNA from scientific publications. The development and evaluation of such methods requires annotated domain corpora. RESULTS: We present BioInfer (Bio Information Extraction Resource), a new public resource providing an annotated corpus of biomedical English. We describe an annotation scheme capturing named entities and their relationships along with a dependency analysis of sentence syntax. We further present ontologies defining the types of entities and relationships annotated in the corpus. Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies. Supporting software is provided with the corpus. The corpus is unique in the domain in combining these annotation types for a single set of sentences, and in the level of detail of the relationship annotation. CONCLUSION: We introduce a corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers. The corpus will be maintained and further developed with a current version being available at

Landscape Mapping of Functional Proteins in Insulin Signal Transduction and Insulin Resistance: A Network-Based Protein-Protein Interaction Analysis

The type 2 diabetes has increased rapidly in recent years throughout the world. The insulin signal transduction mechanism gets disrupted sometimes and it's known as insulin-resistance. It is one of the primary causes associated with type-2 diabetes. The signaling mechanisms involved several proteins that include 7 major functional proteins such as INS, INSR, IRS1, IRS2, PIK3CA, Akt2, and GLUT4. Using these 7 principal proteins, multiple sequences alignment has been created. The scores between sequences also have been developed. We have constructed a phylogenetic tree and modified it with node and distance. Besides, we have generated sequence logos and ultimately developed the protein-protein interaction network. The small insulin signal transduction protein arrangement shows complex network between the functional proteins