Sifat Morfologi Buluh, Dendrocalamus Asper (Schultes F.) Backer Ex Heyne dan Perkaitannya dengan Faktor Edafik Terpilih di Bahagian Sri Aman, Sarawak

Sifat morfologi buluh, Dendrocalamus asper (Schultes f.) Backer ex Heyne dan perkaitannya dengan faktor edafik terpilih telah dikajikan di lima kawasan di bahagian Sri Aman, Sarawak. 96 plot telah disetkan di kawasan kajian berkenaan. Kedua-dua ciri-ciri fizikal dan kimia edafik termasuk pH, kandungan nitrogen, jumlah fosforus, kalsium tertukarkan, magnesium tertukarkan, kalium tertukarkan, natrium tertukarkan, kelembapan, kandungan bahan organik, ketumpatan pukal dan tekstur telah ditentukan melalui analisis makmal. Keputusan menunjukkan bahawa tekstur tanah adalah pasir-Ioam, min bagi pH, kandungan nitrogen, jumlah fosforus, kalsium tertukarkan, magnesium tertukarkan, kalium tertukarkan, natrium tertukarkan, kelembapan, kandungan bahan organik, ketumpatan pukal masing-masing adalah 5.20±0.05, 0.23±0.01%, 269.50±17.00 mgr¹, 1.43±0.18 cmol/kg, 0.91±0.06 cmol/kg, 0.30±0.01 cmol/kg, 0.10±0.02 cmol/kg, 14.26±0.61%, 9.51±0.42% dan 1.00±0.03 gcm⁻³

Model Tikus Omenn Syndrome

In 1965, Gilbert Omenn described a rare, inherited disorder which produced paradoxical combination of immunodeficiency and immune dysregulation. The term Omenn Syndrome was coined originating from this report. More than 40 years ago since it first described, the pathogenesis of OS has remained mysterious and these could be due to unavailable animal model for OS yet. This paper evaluates and discusses 2 murine models of OS that have been found and developed recently by 2 research groups from Japan and Italia. These two murine models bearing mutation in the V(D)J recombination analogous to those causing human OS. These two mouse models have been recapitulate the disease and provide insight into the pathogenesis of OS and also it relation with immunodeficiency combined with autoimmunity and atopy in OS and other related diseases

Elucidation of chemical reaction networks through genetic algorithm

PhD ThesisObtaining chemical reaction network experimentally is a time consuming and expensive method. It requires a lot of specialised equipment and expertise in order to achieve concrete results. Using data mining method on available quantitative information such as concentration data of chemical species can help build the chemical reaction network faster, cheaper and with less expertise. The aim of this work is to design an automated system to determine the chemical reaction network (CRN) from the concentration data of participating chemical species in an isothermal chemical batch reactor. Evolutionary algorithm ability to evolve optimum results for a non-linear problem is chosen as the method to go forward. Genetic algorithm’s simplicity is modified such that it can be used to model the CRN with just integers. The developed automated system has shown it can elucidate the CRN of two fictitious CRNs requiring only a few a priori information such as initial chemical species concentration and molecular weight of chemical species. Robustness of the automated system is tested multiple times with different level of noise in system and introduction of unmeasured chemical species and uninvolved chemical species. The automated system is also tested against an experimental data from the reaction of trimethyl orthoacetate and allyl alcohol which had shown mixed results. This had prompted for the inclusion of NSGA-II algorithm in the automated system to increase its ability to discover multiple reactions. At the end of the work, a final form of the automated system is presented which can process datasets from different initial conditions and different operating temperature which shows a good performance in elucidating the CRNs. It is concluded that automated system is susceptible to ‘overfitting’ where it designs its CRN structure to fit the measured chemical species but with enough variation in the data, it had shown it is capable of elucidating the true CRN even in the presence of unmeasured chemical species, noise and unrelated chemical species