title: Screening of Ophiostoma species for removal of eucalyptus extractives

authors: Su, Yu-Chang

subjects: Eucalyptus camadulensis

abstract: Ophiostoma species have been demonstrated to metabolize wood extractives and be useful to the pulp and paper industry. In order to have new isolates for the Asian market, Eucalyptus camaldulensis logs were harvested from forest sites in central Taiwan and 28 strains of the Ophiostoma genus were isolated from them. These strains were subsequently inoculated onto Eucalyptus wood chips to evaluate their effects on weight losses of wood and the amounts of acetone extractives degraded. At the same time, Gas Chromatography-Mass Spectroscopy (GC-MS) analysis was conducted and by using calibration curves and a database of GC-MS mass spectra, changes in lipophilic compounds were analyzed. Fatty acids, hydrocarbons, sterol compounds, sterol esters, and triglycerides were significantly reduced after two weeks’ inoculation by the fungal strains. The results show that six of the strains were capable of reducing the lipophilic fractions by more than 60% in a two-week treatment. DNA of the most effective strains were analyzed and found to be a variant of Ophiostoma querci.

text: Screening of Ophiostoma species for removal of eucalyptus extractives Ophiostoma species have been demonstrated to metabolize wood extractives and be useful to the pulp and paper industry. In order to have new isolates for the Asian market, Eucalyptus camaldulensis logs were harvested from forest sites in central Taiwan and 28 strains of the Ophiostoma genus were isolated from them. These strains were subsequently inoculated onto Eucalyptus wood chips to evaluate their effects on weight losses of wood and the amounts of acetone extractives degraded. At the same time, Gas Chromatography-Mass Spectroscopy (GC-MS) analysis was conducted and by using calibration curves and a database of GC-MS mass spectra, changes in lipophilic compounds were analyzed. Fatty acids, hydrocarbons, sterol compounds, sterol esters, and triglycerides were significantly reduced after two weeks’ inoculation by the fungal strains. The results show that six of the strains were capable of reducing the lipophilic fractions by more than 60% in a two-week treatment. DNA of the most effective strains were analyzed and found to be a variant of Ophiostoma querci.

year: 2011

title: Maximum margin principal components

authors: Luo, Xianghui

subjects: Machine learning

abstract: Principal Component Analysis (PCA) is a very successful dimensionality reduction technique, widely used in predictive modeling. A key factor in its widespread use in this domain is the fact that the projection of a dataset onto its first $K$ principal components minimizes the sum of squared errors between the original data and the projected data over all possible rank $K$ projections. Thus, PCA provides optimal low-rank representations of data for least-squares linear regression under standard modeling assumptions. On the other hand, when the loss function for a prediction problem is not the least-squares error, PCA is typically a heuristic choice of dimensionality reduction -- in particular for classification problems under the zero-one loss. In this paper we target classification problems by proposing a straightforward alternative to PCA that aims to minimize the difference in margin distribution between the original and the projected data. Extensive experiments show that our simple approach typically outperforms PCA on any particular dataset, in terms of classification error, though this difference is not always statistically significant, and despite being a filter method is frequently competitive with Partial Least Squares (PLS) and Lasso on a wide range of datasets.

text: Maximum margin principal components Principal Component Analysis (PCA) is a very successful dimensionality reduction technique, widely used in predictive modeling. A key factor in its widespread use in this domain is the fact that the projection of a dataset onto its first $K$ principal components minimizes the sum of squared errors between the original data and the projected data over all possible rank $K$ projections. Thus, PCA provides optimal low-rank representations of data for least-squares linear regression under standard modeling assumptions. On the other hand, when the loss function for a prediction problem is not the least-squares error, PCA is typically a heuristic choice of dimensionality reduction -- in particular for classification problems under the zero-one loss. In this paper we target classification problems by proposing a straightforward alternative to PCA that aims to minimize the difference in margin distribution between the original and the projected data. Extensive experiments show that our simple approach typically outperforms PCA on any particular dataset, in terms of classification error, though this difference is not always statistically significant, and despite being a filter method is frequently competitive with Partial Least Squares (PLS) and Lasso on a wide range of datasets.

year: 2017

title: Moko – A Research Project

authors: Te Awekotuku, Ngahuia

subjects: Maori

abstract: Moko colours the lives, and the skins, of all the people involved in the making of this book “Mau Moko”, which began as the Marsden project, “Ta Moko – Culture, Body Modification, and the Psychology of Identity”, 2001-2005. We proposed to study the origins, significance, technology and practice of Ta Moko from the pre-contact period to contemporary times, and to explore how this important art form has become a dynamic and positive assertion of being Maori in our changing world.

text: Moko – A Research Project Moko colours the lives, and the skins, of all the people involved in the making of this book “Mau Moko”, which began as the Marsden project, “Ta Moko – Culture, Body Modification, and the Psychology of Identity”, 2001-2005. We proposed to study the origins, significance, technology and practice of Ta Moko from the pre-contact period to contemporary times, and to explore how this important art form has become a dynamic and positive assertion of being Maori in our changing world.

year: 2008