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The effects of Morris water maze learning on the number, morphology and molecular composition of rat hippocampal dentate gyrus synapses
spatial long-term memory formation is dependent upon the hippocampus and associated brain structures in mammals. Memory storage is believed to involve changes in the way information is exchanged between neurons, and this is principally governed by their synaptic connections. Changes can occur in the functional properties of individual synapses, but evidence suggests that morphological changes may also occur. Research described in this thesis has used the Morris water maze, a behavioural paradigm that requires rodents to form long-term memories about a spatial environment, and this learning task involves the function of the hippocampus. Electron microscopy was used to investigate the ultrastructural morphology and composition of synapses in the hippocampal dentate gyrus in several groups of animals. Three time- points were investigated, 3, 9 and 24 hours after the start of training, which also corresponded to small, intermediate and large amounts of training, as well as two different types of control, naĂŻve and swim-only. Animals investigated 3 hours after the start of training did not show significant long term memory for the task, whereas animals investigated 9 and 24 hours after the start of learning displayed long-term memory recall when measured by the quadrant analysis test (probe trial). Hippocampal dimensions and dentate granule cell densities were similar between all animal groups. No significant changes to synaptic ultrastructural morphology were evident in the 3 hour group. In the 9 hour group, significant increases in synapse density and synapse to neuron ratio were observed, with a simultaneous decrease in the synapse mean height and average area of PSD (post-synaptie density) per synapse. No significant changes were observed in the exercise-matched swim-only controls, suggesting that the changes were related to long-term memory formation. Morphological changes were not evident in the 24 hour group, despite long term memory recall, suggesting that the morphological changes following spatial learning in the Morris water maze are transient. The total amount of synaptic membrane was not significantly different between any of the groups, suggesting that although new, smaller synapses may be formed as a result of learning, changes also occur to existing synapses, which may result in their re-categorisation or even removal. Analysis of ionotropic glutamate receptors following training proved inconclusive, particularly for NMDA receptors, but did suggest that AMP A receptors are increased in the initial stages of learning, which may be a mechanism of short-term memory storage
Impact of mutation rate and selection at linked sites on DNA variation across the genomes of humans and other homininae
DNA diversity varies across the genome of many species. Variation in diversity across a genome might arise from regional variation in the mutation rate, variation in the intensity and mode of natural selection, and regional variation in the recombination rate. We show that both non-coding and non-synonymous diversity are positively correlated to a measure of the mutation rate and the recombination rate and negatively correlated to the density of conserved sequences in 50KB windows across the genomes of humans and non-human homininae. Interestingly, we find that while non-coding diversity is equally affected by these three genomic variables, non-synonymous diversity is mostly dominated by the density of conserved sequences. The positive correlation between diversity and our measure of the mutation rate seems to be largely a direct consequence of regions with higher mutation rates having more diversity. However, the positive correlation with recombination rate and the negative correlation with the density of conserved sequences suggests that selection at linked sites also affect levels of diversity. This is supported by the observation that the ratio of the number of non-synonymous to non-coding polymorphisms is negatively correlated to a measure of the effective population size across the genome. We show these patterns persist even when we restrict our analysis to GC-conservative mutations, demonstrating that the patterns are not driven by GC biased gene conversion. In conclusion, our comparative analyses describe how recombination rate, gene density, and mutation rate interact to produce the patterns of DNA diversity that we observe along the hominine genomes
Self-aware SGD: reliable incremental adaptation framework for clinical AI models
Healthcare is dynamic as demographics, diseases, and therapeutics constantly evolve. This dynamic nature induces inevitable distribution shifts in populations targeted by clinical AI models, often rendering them ineffective. Incremental learning provides an effective method of adapting deployed clinical models to accommodate these contemporary distribution shifts. However, since incremental learning involves modifying a deployed or in-use model, it can be considered unreliable as any adverse modification due to maliciously compromised or incorrectly labelled data can make the model unsuitable for the targeted application. This paper introduces self-aware stochastic gradient descent (SGD) , an incremental deep learning algorithm that utilises a contextual bandit-like sanity check to only allow reliable modifications to a model. The contextual bandit analyses incremental gradient updates to isolate and filter unreliable gradients. This behaviour allows self-aware SGD to balance incremental training and integrity of a deployed model. Experimental evaluations on the Oxford University Hospital datasets highlight that self-aware SGD can provide reliable incremental updates for overcoming distribution shifts in challenging conditions induced by label noise
Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning
As models based on machine learning continue to be developed for healthcare applications, greater effort is needed to ensure that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. Here we introduce a reinforcement learning framework capable of mitigating biases that may have been acquired during data collection. In particular, we evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments and aimed to mitigate any site (hospital)-specific and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show that our method achieves clinically effective screening performances, while significantly improving outcome fairness compared with current benchmarks and state-of-the-art machine learning methods. We performed external validation across three independent hospitals, and additionally tested our method on a patient intensive care unit discharge status task, demonstrating model generalizability
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
Accurate and reliable prediction of hospital admission location is important
due to resource-constraints and space availability in a clinical setting,
particularly when dealing with patients who come from the emergency department.
In this work we propose a student-teacher network via reinforcement learning to
deal with this specific problem. A representation of the weights of the student
network is treated as the state and is fed as an input to the teacher network.
The teacher network's action is to select the most appropriate batch of data to
train the student network on from a training set sorted according to entropy.
By validating on three datasets, not only do we show that our approach
outperforms state-of-the-art methods on tabular data and performs competitively
on image recognition, but also that novel curricula are learned by the teacher
network. We demonstrate experimentally that the teacher network can actively
learn about the student network and guide it to achieve better performance than
if trained alone.Comment: 16 pages, 31 figures, In Proceedings of the 37th International
Conference on Machine Learnin
Short-term genome stability of serial Clostridium difficile ribotype 027 isolates in an experimental gut model and recurrent human disease
Copyright: © 2013 Eyre et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedClostridium difficile whole genome sequencing has the potential to identify related isolates, even among otherwise indistinguishable strains, but interpretation depends on understanding genomic variation within isolates and individuals.Serial isolates from two scenarios were whole genome sequenced. Firstly, 62 isolates from 29 timepoints from three in vitro gut models, inoculated with a NAP1/027 strain. Secondly, 122 isolates from 44 patients (2–8 samples/patient) with mostly recurrent/on-going symptomatic NAP-1/027 C. difficile infection. Reference-based mapping was used to identify single nucleotide variants (SNVs).Across three gut model inductions, two with antibiotic treatment, total 137 days, only two new SNVs became established. Pre-existing minority SNVs became dominant in two models. Several SNVs were detected, only present in the minority of colonies at one/two timepoints. The median (inter-quartile range) [range] time between patients’ first and last samples was 60 (29.5–118.5) [0–561] days. Within-patient C. difficile evolution was 0.45 SNVs/called genome/year (95%CI 0.00–1.28) and within-host diversity was 0.28 SNVs/called genome (0.05–0.53). 26/28 gut model and patient SNVs were non-synonymous, affecting a range of gene targets.The consistency of whole genome sequencing data from gut model C. difficile isolates, and the high stability of genomic sequences in isolates from patients, supports the use of whole genome sequencing in detailed transmission investigations.Peer reviewe
Co-design and feasibility testing of a toolkit for mitigating the negative impact of out of hours mobile ICT demands
This thesis examines strategies for minimising the potential negative impact of out of hours mobile
ICT demands. It provides two studies in this area.
The first study is a Systematic Literature Review (SLR). This followed recognised SLR methodology,
and sought to identify the interventions and strategies that are effective for managing the negative
impact of out of hours work-related mobile ICT demands. The study also reviewed the negative
impacts that the interventions and strategies were seeking to reduce, and the factors which
influenced their success. The 13 studies identified through the review showed that the evidence
base is currently at the initial to promising stage. While a number of strategies and interventions
have been identified, the degree to which these have been systematically evaluated is currently
limited.
To address the limitations identified in the SLR, the second study used an established approach for
intervention development (co-design - Leask et al., 2019) to assemble a prototype toolkit to mitigate
the negative impact of out of hours mobile ICT demands. A total of 24 participants were involved in
the co-design process, which included focus groups and interviews at two time points. Reflexive
thematic analysis identified eight themes key to mitigating the impact of out of hours demands.
Using behavioural change principles (Michie et al., 2011), these were formulated into a prototype
toolkit, which was critically evaluated by the co-design team and a subsequent review by an
independent research consortium. The findings showed that the toolkit was received positively, and
was seen by participants as being an important tool in raising self-awareness and enabling goal oriented behavioural change amongst users.
A number of potential success factor and barriers were identified for future interventions in this
area. These, along with the findings of Studies 1 and 2, have been included within an integrated
framework model for mitigating the negative impact of out of hours mobile ICT demands
Adaptive evolution is substantially impeded by Hill–Robertson interference in Drosophila
Hill–Robertson interference (HRi) is expected to reduce the efficiency of natural selection when two or more linked selected sites do not segregate freely, but no attempt has been done so far to quantify the overall impact of HRi on the rate of adaptive evolution for any given genome. In this work, we estimate how much HRi impedes the rate of adaptive evolution in the coding genome of Drosophila melanogaster. We compiled a data set of 6,141 autosomal protein-coding genes from Drosophila, from which polymorphism levels in D. melanogaster and divergence out to D. yakuba were estimated. The rate of adaptive evolution was calculated using a derivative of the McDonald–Kreitman test that controls for slightly deleterious mutations. We find that the rate of adaptive amino acid substitution at a given position of the genome is positively correlated to both the rate of recombination and the mutation rate, and negatively correlated to the gene density of the region. These correlations are robust to controlling for each other, for synonymous codon bias and for gene functions related to immune response and testes. We show that HRi diminishes the rate of adaptive evolution by approximately 27%. Interestingly, genes with low mutation rates embedded in gene poor regions lose approximately 17% of their adaptive substitutions whereas genes with high mutation rates embedded in gene rich regions lose approximately 60%. We conclude that HRi hampers the rate of adaptive evolution in Drosophila and that the variation in recombination, mutation, and gene density along the genome affects the HRi effect
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