Cyber Black Box: Network intrusion forensics system for collecting and preserving evidence of attack

Once the system is compromised, the forensics and investigation are always executed after the attacks and the loss of some useful instant evidence. Since there is no log information necessary for analyzing an attack cause after the cyber incident occurs, it is difficult to analyze the cause of an intrusion even after an intrusion event is recognized. Moreover, in an advanced cyber incident such as advanced persistent threats, several months or more are expended in only analyzing a cause, and it is difficult to find the cause with conventional security equipment. In this paper, we introduce a network intrusion forensics system for collecting and preserving the evidence of an intrusion, it is called Cyber Black Box that is deployed in Local Area Network environment. It quickly analyzes a cause of an intrusion event when the intrusion event occurs, and provides a function of collecting evidence data of the intrusion event. The paper also describes the experimental results of the network throughput performance by deploying our proposed system in an experimental testbed environment

EST sequencing and gene expression profiling in Scutellaria baicalensis

Scutellaria baicalensis is an important medicinal plant, but few genomic resources are available for this species, as well as for other non-model plants. One of the major new directions in genome research is to discover the full spectrum of genes transcribed from the whole genome. Here, we report extensive transcriptome data of the early growth stage of S. baicalensis. This transcriptome consensus sequence was constructed by de novo assembly of shotgun sequencing data, obtained using multiple next-generation DNA sequencing (NGS) platforms (Roche/454 GS_FLX+ and Illumina/Solexa HiSeq2000). We show that this new approach to obtain extensive mRNA is an efficient strategy for genome-wide transcriptome analysis. We obtained 1,226,938 and 161,417,646 reads using the GS_FLX and the Illumina/Solexa HiS-eq2000, respectively. De novo assembly of the high-quality GS_FLX and Illumina reads (95 % and 75 %) resulted in more than 82 Mb of mRNA consensus sequence, which we assembled into 51,188 contigs, with at least 500 bp per contig. Of these contigs, 39,581 contained known genes, as determined by BLASTX searches against non-redundant NCBI database. Of these, 20,498 different genes were expressed during the early growth stage of S. baicalensis. We have made the expressed sequences available on a public database. Our results demonstrate the utility of combining NGS technologies as a basis for the development of genomic tools in non-model, medicinal plant species. Knowledge of all described genes and quantitation of the expressed genes, including the transcription factors involved, will be useful in studies of the biology of S. baicalensis gene regulation

Precision genome engineering with programmable DNA-nicking enzymes

Zinc finger nucleases (ZFNs) are powerful tools of genome engineering but are limited by their inevitable reliance on error-prone nonhomologous end-joining (NHEJ) repair of DNA double-strand breaks (DSBs), which gives rise to randomly generated, unwanted small insertions or deletions (indels) at both on-target and off-target sites. Here, we present programmable DNA-nicking enzymes (nickases) that produce single-strand breaks (SSBs) or nicks, instead of DSBs, which are repaired by error-free homologous recombination (HR) rather than mutagenic NHEJ. Unlike their corresponding nucleases, zinc finger nickases allow site-specific genome modifications only at the on-target site, without the induction of unwanted indels. We propose that programmable nickases will be of broad utility in research, medicine, and biotechnology, enabling precision genome engineering in any cell or organism.

Partial Bus-Invert Coding for Power Optimization of Application-Specific Systems

This paper presents two bus coding schemes for power optimization of application-specific systems: Partial Bus-Invert coding and its extension to Multiway Partial Bus-Invert coding. In the first scheme, only a selected subgroup of bus lines is encoded to avoid unnecessary inversion of relatively inactive and/or uncorrelated bus lines which are not included in the subgroup. In the extended scheme, we partition a bus into multiple subbuses by clustering highly correlated bus lines and then encode each subbus independently. We describe a heuristic algorithm of partitioning a bus into subbuses for each encoding scheme. Experimental results for various examples indicate that both encoding schemes are highly efficient for application-specific systems

The Clinical Usefulness of 18F-FDG PET/CT for the Evaluation of Lymph Node Metastasis in Periorbital Malignancies

PURPOSE: Surgical treatment of malignancies in the oral cavity and subsequent radiotherapy often result in an oral condition unfavorable for prosthodontic rehabilitation. This study assessed the quality of life related to oral function in edentulous head and neck cancer patients following oncology treatment of malignancies in the lower region of the oral cavity. MATERIALS AND METHODS: Patients treated between 1990 and 2000 with surgery and radiotherapy for a squamous cell carcinoma in the oral cavity who were edentulous in the mandible and had been treated with a conventional, non-implant-retained denture received an invitation for a clinical check-up (clinical assessment, questionnaires regarding oral function and quality of life). RESULTS: Sixty-seven of the 84 patients who fulfilled the inclusion criteria were willing to participate in the study. The mean irradiation dosage that these patients had received in the oral region was 61.8 +/- 5.4 Gy. Half of the patients (n=33) were not very satisfied with their prostheses; they wore their mandibular prostheses at most a few hours per day. It was concluded from the clinical assessment that two thirds of the patients (n 4) could benefit from an implant-retained mandibular denture. Analyses of the questionnaires revealed no significant associations between functional assessments, quality of life, and parameters such as size of the primary tumor, location of the primary tumor, and different treatment regimes. Despite cancer treatment, the patients reported a rather good general quality of life. CONCLUSIONS: Sequelae resulting from radiotherapy probably dominate oral function and quality of life after oncology treatment. In two thirds of the patients, improvement of oral function and related quality of life would be expected with the use of an implant-retained mandibular denture

In-situ fabrication of cobalt-doped SrFe2As2 thin films by using pulsed laser deposition with excimer laser

The remarkably high superconducting transition temperature and upper critical field of iron(Fe)-based layered superconductors, despite ferromagnetic material base, open the prospect for superconducting electronics. However, success in superconducting electronics has been limited because of difficulties in fabricating high-quality thin films. We report the growth of high-quality c-axis-oriented cobalt(Co)-doped SrFe2As2 thin films with bulk superconductivity by using an in-situ pulsed laser deposition technique with a 248-nm-wavelength KrF excimer laser and an arsenic(As)-rich phase target. The temperature and field dependences of the magnetization showing strong diamagnetism and transport critical current density with superior Jc-H performance are reported. These results provide necessary information for practical applications of Fe-based superconductors.Comment: 8 pages, 3figures. to be published at Appl. Phys. Let

Application of machine learning in SNP discovery

<p>Abstract</p> <p>Background</p> <p>Single nucleotide polymorphisms (SNP) constitute more than 90% of the genetic variation, and hence can account for most trait differences among individuals in a given species. Polymorphism detection software PolyBayes and PolyPhred give high false positive SNP predictions even with stringent parameter values. We developed a machine learning (ML) method to augment PolyBayes to improve its prediction accuracy. ML methods have also been successfully applied to other bioinformatics problems in predicting genes, promoters, transcription factor binding sites and protein structures.</p> <p>Results</p> <p>The ML program C4.5 was applied to a set of features in order to build a SNP classifier from training data based on human expert decisions (True/False). The training data were 27,275 candidate SNP generated by sequencing 1973 STS (sequence tag sites) (12 Mb) in both directions from 6 diverse homozygous soybean cultivars and PolyBayes analysis. Test data of 18,390 candidate SNP were generated similarly from 1359 additional STS (8 Mb). SNP from both sets were classified by experts. After training the ML classifier, it agreed with the experts on 97.3% of test data compared with 7.8% agreement between PolyBayes and experts. The PolyBayes positive predictive values (PPV) (i.e., fraction of candidate SNP being real) were 7.8% for all predictions and 16.7% for those with 100% posterior probability of being real. Using ML improved the PPV to 84.8%, a 5- to 10-fold increase. While both ML and PolyBayes produced a similar number of true positives, the ML program generated only 249 false positives as compared to 16,955 for PolyBayes. The complexity of the soybean genome may have contributed to high false SNP predictions by PolyBayes and hence results may differ for other genomes.</p> <p>Conclusion</p> <p>A machine learning (ML) method was developed as a supplementary feature to the polymorphism detection software for improving prediction accuracies. The results from this study indicate that a trained ML classifier can significantly reduce human intervention and in this case achieved a 5–10 fold enhanced productivity. The optimized feature set and ML framework can also be applied to all polymorphism discovery software. ML support software is written in Perl and can be easily integrated into an existing SNP discovery pipeline.</p

SNP-PHAGE – High throughput SNP discovery pipeline

BACKGROUND: Single nucleotide polymorphisms (SNPs) as defined here are single base sequence changes or short insertion/deletions between or within individuals of a given species. As a result of their abundance and the availability of high throughput analysis technologies SNP markers have begun to replace other traditional markers such as restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs or microsatellite) markers for fine mapping and association studies in several species. For SNP discovery from chromatogram data, several bioinformatics programs have to be combined to generate an analysis pipeline. Results have to be stored in a relational database to facilitate interrogation through queries or to generate data for further analyses such as determination of linkage disequilibrium and identification of common haplotypes. Although these tasks are routinely performed by several groups, an integrated open source SNP discovery pipeline that can be easily adapted by new groups interested in SNP marker development is currently unavailable. RESULTS: We developed SNP-PHAGE (SNP discovery Pipeline with additional features for identification of common haplotypes within a sequence tagged site (Haplotype Analysis) and GenBank (-dbSNP) submissions. This tool was applied for analyzing sequence traces from diverse soybean genotypes to discover over 10,000 SNPs. This package was developed on UNIX/Linux platform, written in Perl and uses a MySQL database. Scripts to generate a user-friendly web interface are also provided with common queries for preliminary data analysis. A machine learning tool developed by this group for increasing the efficiency of SNP discovery is integrated as a part of this package as an optional feature. The SNP-PHAGE package is being made available open source at . CONCLUSION: SNP-PHAGE provides a bioinformatics solution for high throughput SNP discovery, identification of common haplotypes within an amplicon, and GenBank (dbSNP) submissions. SNP selection and visualization are aided through a user-friendly web interface. This tool is useful for analyzing sequence tagged sites (STSs) of genomic sequences, and this software can serve as a starting point for groups interested in developing SNP markers