title: A review of multi-instance learning assumptions

authors: Foulds, James Richard

subjects: computer science

abstract: Multi-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many MI learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain; this ‘standard MI assumption’ is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area.

text: A review of multi-instance learning assumptions Multi-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many MI learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain; this ‘standard MI assumption’ is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area.

year: 2010

title: Focusing on teachers as learners in professional learning communities

authors: Swanson, Carolyn Julie

abstract: Practising teachers are part of professional learning communities in many educational settings. In this piece, the editors unpack what we mean by the term learning community and outline the nature of professional learning that can occur in professional learning communities. Like all good ideas, the concepts of communities of practice and communities of learners get captured, digested, and become formulas or recipes that have lost the full flavour of the original concepts. Therefore, it is crucial that we re-question the aims of professional learning communities and critically examine their current configurations. Are our professional learning communities safe places, where members can challenge current thinking, take risks and learn together with a shared purpose, and refresh ourselves? Or are the participants compelled to attend, working off someone else’s agenda and direction and lacking the freedom to explore?

text: Focusing on teachers as learners in professional learning communities Practising teachers are part of professional learning communities in many educational settings. In this piece, the editors unpack what we mean by the term learning community and outline the nature of professional learning that can occur in professional learning communities. Like all good ideas, the concepts of communities of practice and communities of learners get captured, digested, and become formulas or recipes that have lost the full flavour of the original concepts. Therefore, it is crucial that we re-question the aims of professional learning communities and critically examine their current configurations. Are our professional learning communities safe places, where members can challenge current thinking, take risks and learn together with a shared purpose, and refresh ourselves? Or are the participants compelled to attend, working off someone else’s agenda and direction and lacking the freedom to explore?

year: 2018

title: Studies on Paphies australis (Mesodesmatidae) in Whangārei, Aotearoa New Zealand: an investigation into their declining health.

authors: Howells, Joanne Elizabeth

abstract: Disease outbreaks in marine species can have devastating consequences, including increasing host mortality, destabilising commercial production, altering ecosystem functioning, and jeopardising human health. A changing ocean climate is expected to increase host stress and the risk of disease outbreaks. Particularly through new and emerging diseases and opportunistic infections.
Wild bivalve molluscs provide economic, ecological, and socio-cultural benefits. Healthy and sustainable kaimoana (shellfish/bivalves, seafood) are intrinsically linked to the wellbeing of many communities in New Zealand. Over the past decade there has been a noticeable decline in the general health of native marine bivalves, characterised by an increase in observation of mass die-offs. Reasons for this are not clear and could be due to a number of factors, such as sedimentation, harvesting pressures, or disease. Baseline health data on wild bivalve populations are very rare and therefore it is very difficult to identify the causation of ill health. There is a need to establish health baselines to help benchmark changes in infection prevalence and intensity and identify drivers of bivalve mortality events.
Wild bivalve populations are often located in remote and isolated parts of New Zealand. When a mortality event occurs, the logistics of obtaining quality samples for accurate diagnosis can be hindered due to time delays and sample degradation. To gain a better understanding of these limitations for diagnostics using bacteriology, in Chapter 2 I investigate typical sample collection methods (i.e., samples collected and sent to a testing laboratory) and sample collections immediately in the field to understand what effect different sample methods have on number and types of bacteria isolated. Although the results of Chapter 2 found there were limitations to both sampling methods, it did identify that typical sampling techniques were not wholly optimal, and the information gained by sampling immediately in the field retained additional information that could be essential for disease diagnostics. Field sampling for bacteriology was applied throughout this thesis research.
Pipi (Paphies australis) are native bivalves to New Zealand, inhabiting the shallow subtidal of estuaries and bays. Pipi have suffered serious population declines in an area of northern New Zealand (Whangārei) since 2009. I investigate possible drivers of the poor health of pipi in Whangārei, with the objective of identifying the mechanisms that have contributed to the decline, and prevention of the recovery of pipi in Whangārei, with a primary focus of disease as a causative agent. I establish a health baseline of pipi from Whangārei using histopathology, bacteriology, and qPCR of Endozoicomonas spp., a bacteria previously identified in mortality bivalves (Chapter 3). The health baseline was compared against samples collected during three mass mortality events to help identify drivers of mass mortality events (Chapter 4). Through laboratory experimentation, I test whether pipi exposed to stressors had higher infection intensity and prevalence of Endozoicomonas spp. (Chapter 5). Finally, using archived samples, I test whether the baseline data established was consistent through time and help to provide insights into considerations when analysing bivalve mass mortality events (Chapter 6).
I found that Endozoicomonas spp. make an important part of pipi host microbiome. Although Endozoicomonas spp. are highly prevalent in pipi collected during a mass mortality event, their infection intensity was significantly lower in moribund pipi than healthy pipi. The prevalence and infection intensity of Endozoicomonas spp. were not driven by environmental stressors. The stressors, however, did appear to impact host health, with high suspended sediment loads and water temperatures of 24°C appeared to be unfavourable to pipi. Overall, Endozoicomonas sp. does not appear to be a main driver in the declining population of pipi from Whangārei and is probably not important to bivalve health. The reduced prevalence and infection intensity of Endozoicomonas sp. in healthy pipi strongly suggests these bacteria are symbiotic. A relationship that has been reported for this bacterial group in species of coral and sponges.
The establishment of a health baseline represents a significant step forward towards biosecurity preparedness by being able to identify changes in infection prevalence and intensity against it. The operational use of these data when paired with historical data will be a powerful tool to identify patterns of disease dynamics and emerging disease.

text: Studies on Paphies australis (Mesodesmatidae) in Whangārei, Aotearoa New Zealand: an investigation into their declining health. Disease outbreaks in marine species can have devastating consequences, including increasing host mortality, destabilising commercial production, altering ecosystem functioning, and jeopardising human health. A changing ocean climate is expected to increase host stress and the risk of disease outbreaks. Particularly through new and emerging diseases and opportunistic infections.
Wild bivalve molluscs provide economic, ecological, and socio-cultural benefits. Healthy and sustainable kaimoana (shellfish/bivalves, seafood) are intrinsically linked to the wellbeing of many communities in New Zealand. Over the past decade there has been a noticeable decline in the general health of native marine bivalves, characterised by an increase in observation of mass die-offs. Reasons for this are not clear and could be due to a number of factors, such as sedimentation, harvesting pressures, or disease. Baseline health data on wild bivalve populations are very rare and therefore it is very difficult to identify the causation of ill health. There is a need to establish health baselines to help benchmark changes in infection prevalence and intensity and identify drivers of bivalve mortality events.
Wild bivalve populations are often located in remote and isolated parts of New Zealand. When a mortality event occurs, the logistics of obtaining quality samples for accurate diagnosis can be hindered due to time delays and sample degradation. To gain a better understanding of these limitations for diagnostics using bacteriology, in Chapter 2 I investigate typical sample collection methods (i.e., samples collected and sent to a testing laboratory) and sample collections immediately in the field to understand what effect different sample methods have on number and types of bacteria isolated. Although the results of Chapter 2 found there were limitations to both sampling methods, it did identify that typical sampling techniques were not wholly optimal, and the information gained by sampling immediately in the field retained additional information that could be essential for disease diagnostics. Field sampling for bacteriology was applied throughout this thesis research.
Pipi (Paphies australis) are native bivalves to New Zealand, inhabiting the shallow subtidal of estuaries and bays. Pipi have suffered serious population declines in an area of northern New Zealand (Whangārei) since 2009. I investigate possible drivers of the poor health of pipi in Whangārei, with the objective of identifying the mechanisms that have contributed to the decline, and prevention of the recovery of pipi in Whangārei, with a primary focus of disease as a causative agent. I establish a health baseline of pipi from Whangārei using histopathology, bacteriology, and qPCR of Endozoicomonas spp., a bacteria previously identified in mortality bivalves (Chapter 3). The health baseline was compared against samples collected during three mass mortality events to help identify drivers of mass mortality events (Chapter 4). Through laboratory experimentation, I test whether pipi exposed to stressors had higher infection intensity and prevalence of Endozoicomonas spp. (Chapter 5). Finally, using archived samples, I test whether the baseline data established was consistent through time and help to provide insights into considerations when analysing bivalve mass mortality events (Chapter 6).
I found that Endozoicomonas spp. make an important part of pipi host microbiome. Although Endozoicomonas spp. are highly prevalent in pipi collected during a mass mortality event, their infection intensity was significantly lower in moribund pipi than healthy pipi. The prevalence and infection intensity of Endozoicomonas spp. were not driven by environmental stressors. The stressors, however, did appear to impact host health, with high suspended sediment loads and water temperatures of 24°C appeared to be unfavourable to pipi. Overall, Endozoicomonas sp. does not appear to be a main driver in the declining population of pipi from Whangārei and is probably not important to bivalve health. The reduced prevalence and infection intensity of Endozoicomonas sp. in healthy pipi strongly suggests these bacteria are symbiotic. A relationship that has been reported for this bacterial group in species of coral and sponges.
The establishment of a health baseline represents a significant step forward towards biosecurity preparedness by being able to identify changes in infection prevalence and intensity against it. The operational use of these data when paired with historical data will be a powerful tool to identify patterns of disease dynamics and emerging disease.

year: 2023