title: Regression modelling of spectroscopic data using lazy learning and deep neural networks

authors: Fraser, Huon

subjects: Regression

abstract: Neural networks show promise in modelling infrared (IR) spectroscopic data, but many of the proposed solutions in the literature are either Multilayer Perceptrons with a single hidden layer or are adapted from successful solutions in other domains such as convolutional networks from image processing.

This thesis investigates the use of Monte Carlo search to build candidate deep neural networks from scratch. As the prediction targets are real numbers and the modelling is prone to error from outliers, lazy, locally weighted methods are also investigated. 

Predictions from the network are compared to predictions made from locally weighting the features extracted from the same network. To measure progress, these results are compared to classical statistical methods commonly used to model spectroscopic data, such as partial least squares (PLS) regression.

Results suggest that deep networks trained via Monte-Carlo search outperform classical approaches. They also show that PLS dimension reduction as a preprocessing technique can improve the quality of search at little cost in predictive ability. Encouraging results using this approach are obtained on the publicly available Mangoes dataset. Finally, a streaming regression solution is investigated in which selected deep neural network models perform well. In this setting, however, locally weighted regression can result in sporadic outlier predictions, so an ensemble solution is proposed as a remedy.

text: Regression modelling of spectroscopic data using lazy learning and deep neural networks Neural networks show promise in modelling infrared (IR) spectroscopic data, but many of the proposed solutions in the literature are either Multilayer Perceptrons with a single hidden layer or are adapted from successful solutions in other domains such as convolutional networks from image processing.

This thesis investigates the use of Monte Carlo search to build candidate deep neural networks from scratch. As the prediction targets are real numbers and the modelling is prone to error from outliers, lazy, locally weighted methods are also investigated. 

Predictions from the network are compared to predictions made from locally weighting the features extracted from the same network. To measure progress, these results are compared to classical statistical methods commonly used to model spectroscopic data, such as partial least squares (PLS) regression.

Results suggest that deep networks trained via Monte-Carlo search outperform classical approaches. They also show that PLS dimension reduction as a preprocessing technique can improve the quality of search at little cost in predictive ability. Encouraging results using this approach are obtained on the publicly available Mangoes dataset. Finally, a streaming regression solution is investigated in which selected deep neural network models perform well. In this setting, however, locally weighted regression can result in sporadic outlier predictions, so an ensemble solution is proposed as a remedy.

year: 2022

title: Groundtruthing next-gen sequencing for microbial ecology-biases and errors in community structure estimates from PCR amplicon pyrosequencing

authors: Lee, Charles Kai-Wu

subjects: RNA 16S

abstract: Analysis of microbial communities by high-throughput pyrosequencing of SSU rRNA gene PCR amplicons has transformed microbial ecology research and led to the observation that many communities contain a diverse assortment of rare taxa-a phenomenon termed the Rare Biosphere. Multiple studies have investigated the effect of pyrosequencing read quality on operational taxonomic unit (OTU) richness for contrived communities, yet there is limited information on the fidelity of community structure estimates obtained through this approach. Given that PCR biases are widely recognized, and further unknown biases may arise from the sequencing process itself, a priori assumptions about the neutrality of the data generation process are at best unvalidated. Furthermore, post-sequencing quality control algorithms have not been explicitly evaluated for the accuracy of recovered representative sequences and its impact on downstream analyses, reducing useful discussion on pyrosequencing reads to their diversity and abundances. Here we report on community structures and sequences recovered for in vitro-simulated communities consisting of twenty 16S rRNA gene clones tiered at known proportions. PCR amplicon libraries of the V3-V4 and V6 hypervariable regions from the in vitro-simulated communities were sequenced using the Roche 454 GS FLX Titanium platform. Commonly used quality control protocols resulted in the formation of OTUs with >1% abundance composed entirely of erroneous sequences, while over-aggressive clustering approaches obfuscated real, expected OTUs. The pyrosequencing process itself did not appear to impose significant biases on overall community structure estimates, although the detection limit for rare taxa may be affected by PCR amplicon size and quality control approach employed. Meanwhile, PCR biases associated with the initial amplicon generation may impose greater distortions in the observed community structure.

text: Groundtruthing next-gen sequencing for microbial ecology-biases and errors in community structure estimates from PCR amplicon pyrosequencing Analysis of microbial communities by high-throughput pyrosequencing of SSU rRNA gene PCR amplicons has transformed microbial ecology research and led to the observation that many communities contain a diverse assortment of rare taxa-a phenomenon termed the Rare Biosphere. Multiple studies have investigated the effect of pyrosequencing read quality on operational taxonomic unit (OTU) richness for contrived communities, yet there is limited information on the fidelity of community structure estimates obtained through this approach. Given that PCR biases are widely recognized, and further unknown biases may arise from the sequencing process itself, a priori assumptions about the neutrality of the data generation process are at best unvalidated. Furthermore, post-sequencing quality control algorithms have not been explicitly evaluated for the accuracy of recovered representative sequences and its impact on downstream analyses, reducing useful discussion on pyrosequencing reads to their diversity and abundances. Here we report on community structures and sequences recovered for in vitro-simulated communities consisting of twenty 16S rRNA gene clones tiered at known proportions. PCR amplicon libraries of the V3-V4 and V6 hypervariable regions from the in vitro-simulated communities were sequenced using the Roche 454 GS FLX Titanium platform. Commonly used quality control protocols resulted in the formation of OTUs with >1% abundance composed entirely of erroneous sequences, while over-aggressive clustering approaches obfuscated real, expected OTUs. The pyrosequencing process itself did not appear to impose significant biases on overall community structure estimates, although the detection limit for rare taxa may be affected by PCR amplicon size and quality control approach employed. Meanwhile, PCR biases associated with the initial amplicon generation may impose greater distortions in the observed community structure.

year: 2012

title: Catchment and lake water quality modelling to assess management options for Lake Tutira

authors: McBride, Chris G.

abstract: Lake Tutira is an important ecological, historical and cultural asset to the Hawke’s Bay. It is a highly utilised trout fishery, a popular recreational destination, and is particularly significant to Ngāti Kahungunu, for its historical eel fishery and rongoa (medicinal) harakeke (flax) beds. The lake and its catchment have been substantially altered from their natural condition, including conversion of forested catchment to pasture, invasion by aquatic macrophytes such as Hydrilla sp., the introduction of grass carp (Ctenopharyngaodon idella) for weed management, and diversion of the largest stream inflow, Papakiri Stream (Sandy Creek), away from the lake and directly to the outflow (Mahiaruhe Stream) in the early 1980s. Lake Tutira’s history of poor water quality dates back decades, with Trophic Level Index (TLI; a metric of overall water quality) values from the 1960s similar to recent years, and blooms of cyanobacteria (blue-green algae) recorded as early as the 1970s. Nevertheless, the years since 2010 have been characterised by very poor water quality and particularly severe blooms, sometimes resulting in near-complete loss of oxygen from surface waters and associated fish kill events. This has attracted media attention and provided impetus from community and stakeholders to assess available management options for restoring more natural conditions and improving water quality in the lake. 
This report examines the drivers of poor water quality and algal blooms the lake, and assesses various management options that have either been specifically proposed for Tutira, or have proven effective for other lakes in New Zealand and globally. Here we report on monitoring undertaken by Hawke’s Bay Regional Council in the catchment (surface inflows) and lake. We also apply state-of-the-art computer models for simulating physical, chemical, and biological processes in the catchment and lake. These simulations are then used to evaluate a range of management ‘scenarios’ to provide guidance on the likely effects of management options tested with the model.

text: Catchment and lake water quality modelling to assess management options for Lake Tutira Lake Tutira is an important ecological, historical and cultural asset to the Hawke’s Bay. It is a highly utilised trout fishery, a popular recreational destination, and is particularly significant to Ngāti Kahungunu, for its historical eel fishery and rongoa (medicinal) harakeke (flax) beds. The lake and its catchment have been substantially altered from their natural condition, including conversion of forested catchment to pasture, invasion by aquatic macrophytes such as Hydrilla sp., the introduction of grass carp (Ctenopharyngaodon idella) for weed management, and diversion of the largest stream inflow, Papakiri Stream (Sandy Creek), away from the lake and directly to the outflow (Mahiaruhe Stream) in the early 1980s. Lake Tutira’s history of poor water quality dates back decades, with Trophic Level Index (TLI; a metric of overall water quality) values from the 1960s similar to recent years, and blooms of cyanobacteria (blue-green algae) recorded as early as the 1970s. Nevertheless, the years since 2010 have been characterised by very poor water quality and particularly severe blooms, sometimes resulting in near-complete loss of oxygen from surface waters and associated fish kill events. This has attracted media attention and provided impetus from community and stakeholders to assess available management options for restoring more natural conditions and improving water quality in the lake. 
This report examines the drivers of poor water quality and algal blooms the lake, and assesses various management options that have either been specifically proposed for Tutira, or have proven effective for other lakes in New Zealand and globally. Here we report on monitoring undertaken by Hawke’s Bay Regional Council in the catchment (surface inflows) and lake. We also apply state-of-the-art computer models for simulating physical, chemical, and biological processes in the catchment and lake. These simulations are then used to evaluate a range of management ‘scenarios’ to provide guidance on the likely effects of management options tested with the model.

year: 2017