Chosŏn Yugyo yŏnwŏn /

Colophone title.

Chuyŏn sŏnjip /

Mode of access: Internet.

Cheŏn Chosasŏ : che-1.

List of dikes in tabular form, with preface in Japanese.

Tafvelsamlingen på Finspong.

Introductory text by G. Göthe.

Haedong myŏngjangjŏn.

6-kwŏn.

Im Chʻungmin Kong silgi.

Mode of access: Internet.

Sanggong wŏlbo.

Mode of access: Internet.

Kwanbo.

Reprint title: Ku Hanʼguk kwanbo.

Chosŏn.

Mode of access: Internet.

Saga Ólafs konungs Tryggvasonar,

Edited by C. C. Rafn, R. K. Rask and Þorgeir Guðmundsson.

Codon-precise, synthetic, antibody fragment libraries built using automated hexamer codon additions and validated through next generation sequencing

We have previously described ProxiMAX, a technology that enables the fabrication of precise, combinatorial gene libraries via codon-by-codon saturation mutagenesis. ProxiMAX was originally performed using manual, enzymatic transfer of codons via blunt-end ligation. Here we present Colibra™: an automated, proprietary version of ProxiMAX used specifically for antibody library generation, in which double-codon hexamers are transferred during the saturation cycling process. The reduction in process complexity, resulting library quality and an unprecedented saturation of up to 24 contiguous codons are described. Utility of the method is demonstrated via fabrication of complementarity determining regions (CDR) in antibody fragment libraries and next generation sequencing (NGS) analysis of their quality and diversity.

Comprehensive characterization of Napier grass as a feedstock for thermochemical conversion

Study on Napier grass leaf (NGL), stem (NGS) and leaf and stem (NGT) was carried out. Proximate, ultimate and structural analyses were evaluated. Functional groups and crystalline components in the biomass were examined. Pyrolysis study was conducted in a thermogravimetric analyzer under nitrogen atmosphere of 20 mL/min at constant heating rate of 10 K/min. The results reveal that Napier grass biomass has high volatile matter, higher heating value, high carbon content and lower ash, nitrogen and sulfur contents. Structural analysis shows that the biomass has considerable cellulose and lignin contents which are good candidates for good quality bio-oil production. From the pyrolysis study, degradation of extractives, hemicellulose, cellulose and lignin occurred at temperature around 478, 543, 600 and above 600 K, respectively. Kinetics of the process was evaluated using reaction order model. New equations that described the process were developed using the kinetic parameters and data compared with experimental data. The results of the models fit well to the experimental data. The proposed models may be a reliable means for describing thermal decomposition of lignocellulosic biomass under nitrogen atmosphere at constant heating rate.

Towards the prediction of mutations in genomic sequences

Bio-systems are inherently complex information processing systems. Furthermore, physiological complexities of biological systems limit the formation of a hypothesis in terms of behavior and the ability to test hypothesis. More importantly the identification and classification of mutation in patients are centric topics in today's cancer research. Next generation sequencing (NGS) technologies can provide genome-wide coverage at a single nucleotide resolution and at reasonable speed and cost. The unprecedented molecular characterization provided by NGS offers the potential for an individualized approach to treatment. These advances in cancer genomics have enabled scientists to interrogate cancer-specific genomic variants and compare them with the normal variants in the same patient. Analysis of this data provides a catalog of somatic variants, present in tumor genome but not in the normal tissue DNA. In this dissertation, we present a new computational framework to the problem of predicting the number of mutations on a chromosome for a certain patient, which is a fundamental problem in clinical and research fields. We begin this dissertation with the development of a framework system that is capable of utilizing published data from a longitudinal study of patients with acute myeloid leukemia (AML), who's DNA from both normal as well as malignant tissues was subjected to NGS analysis at various points in time. By processing the sequencing data at the time of cancer diagnosis using the components of our framework, we tested it by predicting the genomic regions to be mutated at the time of relapse and, later, by comparing our results with the actual regions that showed mutations (discovered at relapse time). We demonstrate that this coupling of the algorithm pipeline can drastically improve the predictive abilities of searching a reliable molecular signature. Arguably, the most important result of our research is its superior performance to other methods like Radial Basis Function Network, Sequential Minimal Optimization, and Gaussian Process. In the final part of this dissertation, we present a detailed significance, stability and statistical analysis of our model. A performance comparison of the results are presented. This work clearly lays a good foundation for future research for other types of cancer.^