Representation and decision making in the immune system

The immune system has long been attributed cognitive capacities such as "recognition" of pathogenic agents; "memory" of previous infections; "regulation" of a cavalry of detector and effector cells; and "adaptation" to a changing environment and evolving threats. Ostensibly, in preventing disease the immune system must be capable of discriminating states of pathology in the organism; identifying causal agents or ``pathogens''; and correctly deploying lethal effector mechanisms. What is more, these behaviours must be learnt insomuch as the paternal genes cannot encode the pathogenic environment of the child. Insights into the mechanisms underlying these phenomena are of interest, not only to immunologists, but to computer scientists pushing the envelope of machine autonomy. This thesis approaches these phenomena from the perspective that immunological processes are inherently inferential processes. By considering the immune system as a statistical decision maker, we attempt to build a bridge between the traditionally distinct fields of biological modelling and statistical modelling. Through a mixture of novel theoretical and empirical analysis we assert the efficacy of competitive exclusion as a general principle that benefits both. For the immunologist, the statistical modelling perspective allows us to better determine that which is phenomenologically sufficient from the mass of observational data, providing quantitative insight that may offer relief from existing dichotomies. For the computer scientist, the biological modelling perspective results in a theoretically transparent and empirically effective numerical method that is able to finesse the trade-off between myopic greediness and intractability in domains such as sparse approximation, continuous learning and boosting weak heuristics. Together, we offer this as a modern reformulation of the interface between computer science and immunology, established in the seminal work of Perelson and collaborators, over 20 years ago.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Representation and Decision Making in the Immune System

The immune system has long been attributed cognitive capacities such as "recognition" of pathogenic agents; "memory" of previous infections; "regulation" of a cavalry of detector and effector cells; and "adaptation" to a changing environment and evolving threats. Ostensibly, in preventing disease the immune system must be capable of discriminating states of pathology in the organism; identifying causal agents or "pathogens"; and correctly deploying lethal effector mechanisms. What is more, these behaviours must be learnt insomuch as the paternal genes cannot encode the pathogenic environment of the child. Insights into the mechanisms underlying these phenomena are of interest, not only to immunologists, but to computer scientists pushing the envelope of machine autonomy.This thesis approaches these phenomena from the perspective that immunological processes are inherently inferential processes. By considering the immune system as a statistical decision maker, we attempt to build a bridge between the traditionally distinct fields of biological modelling and statistical modelling. Through a mixture of novel theoretical and empirical analysis we assert the efficacy of competitive exclusion as a general principle that benefits both. For the immunologist, the statistical modelling perspective allows us to better determine that which is phenomenologically sufficient from the mass of observational data, providing quantitative insight that may offer relief from existing dichotomies. For the computer scientist, the biological modelling perspective results in a theoretically transparent and empirically effective numerical method that is able to finesse the trade-off between myopic greediness and intractability in domains such as sparse approximation, continuous learning and boosting weak heuristics. Together, we offer this as a modern reformulation of the interface between computer science and immunology, established in the seminal work of Perelson and collaborators, over 20 years ago

Inference of transcriptional regulation using gene expression data from the bovine and human genomes

<p>Abstract</p> <p>Background</p> <p>Gene expression is in part regulated by sequences in promoters that bind transcription factors. Thus, co-expressed genes may have shared sequence motifs representing putative transcription factor binding sites (TFBSs). However, for agriculturally important animals the genomic sequence is often incomplete. The more complete human genome may be able to be used for this prediction by taking advantage of the expected evolutionary conservation in TFBSs between the species.</p> <p>Results</p> <p>A method of <it>de novo </it>TFBS prediction based on MEME was implemented, tested, and validated on a muscle-specific dataset.</p> <p>Muscle specific expression data from EST library analysis from cattle was used to predict sets of genes whose expression was enriched in muscle and cardiac tissues. The upstream 1500 bases from calculated orthologous genes were extracted from the human reference set. A set of common motifs were discovered in these promoters. Slightly over one third of these motifs were identified as known TFBSs including known muscle specific binding sites. This analysis also predicted several highly statistically significantly overrepresented sites that may be novel TFBS.</p> <p>An independent analysis of the equivalent bovine genomic sequences was also done, this gave less detailed results than the human analysis due to both the quality of orthologue prediction and assembly in promoter regions. However, the most common motifs could be detected in both sets.</p> <p>Conclusion</p> <p>Using promoter sequences from human genes is a useful approach when studying gene expression in species with limited or non-existing genomic sequence. As the bovine genome becomes better annotated it can in turn serve as the reference genome for other agriculturally important ruminants, such as sheep, goat and deer.</p

On the role of the AIS practitioner

Cognisant of the gulf between engineers and immunologists that currenty hinders a truly inter-disciplinary approach to the field of Artificial Immune Systems (AIS), we propose a redefinition of the term AIS practitioner, as an individual who identifies those components and interactions captured in computational immunology models that are responsible for a particular property of interest (POI), and distils from these a set of algorithms and principles that can be applied in an engineering domain. We outline the role of the cross-disciplinary practitioner and the potential benefits to the field

Interval running with self-selected recovery:Physiology, performance and perception

Item previously deposited in UWS repository at: https://research-portal.uws.ac.uk/en/publications/interval-running-with-self-selected-recovery-physiology-performanItem not available in this repository.Rosie Arthur – ORCID: 0000-0003-0651-4056 https://orcid.org/0000-0003-0651-4056This study (1) compared the physiological responses and performance during a high-intensity interval training (HIIT) session incorporating externally regulated (ER) and self-selected (SS) recovery periods and (2) examined the psychophysiological cues underpinning SS recovery durations. Following an incremental maximal exercise test to determine maximal aerobic speed (MAS), 14 recreationally active males completed 2 HIIT sessions on a non-motorised treadmill. Participants performed 12 × 30 s running intervals at a target intensity of 105% MAS interspersed with 30 s (ER) or SS recovery periods. During SS, participants were instructed to provide themselves with sufficient recovery to complete all 12 efforts at the required intensity. A semi-structured interview was undertaken following the completion of SS. Mean recovery duration was longer during SS (51 ± 15 s) compared to ER (30 ± 0 s; p < .001; d = 1.46 ± 0.46). Between-interval heart rate recovery was higher (SS: 19 ± 9 b min−1; ER: 8 ± 5 b min−1; p < .001; d = 1.43 ± 0.43) and absolute time ≥90% maximal heart rate (HRmax) was lower (SS: 335 ± 193 s; ER: 433 ± 147 s; p = .075; d = 0.52 ± 0.39) during SS compared to ER. Relative time ≥105% MAS was greater during SS (90 ± 6%) compared to ER (74 ± 20%; p < .01; d = 0.87 ± 0.40). Different sources of afferent information underpinned decision-making during SS. The extended durations of recovery during SS resulted in a reduced time ≥90% HRmax but enhanced time ≥105% MAS, compared with ER exercise. Differences in the afferent cue utilisation of participants likely explain the large levels of inter-individual variability observed.The authors wish to thank Oriam: Scotland’s National Performance Centre who provided funding to support a Masters studentship for Gary McEwan.https://doi.org/10.1080/17461391.2018.147281118pubpub

On the role of the AIS practitioner

Cognisant of the gulf between engineers and immunologists that currenty hinders a truly inter-disciplinary approach to the field of Artificial Immune Systems (AIS), we propose a redefinition of the term AIS practitioner, as an individual who identifies those components and interactions captured in computational immunology models that are responsible for a particular property of interest (POI), and distils from these a set of algorithms and principles that can be applied in an engineering domain. We outline the role of the cross-disciplinary practitioner and the potential benefits to the field

Decision-making accuracy of soccer referees in relation to markers of internal and external load

This study examined the relationships between the decision-making performances of soccer referees and markers of physiological load. Following baseline measurements and habituation procedures, 13 national-level male referees completed a novel Soccer Referee Simulation whilst simultaneously adjudicating on a series of video-based decision-making clips. The correctness of each decision was assessed in relation to the mean heart rate (HR), respiratory rate (RR), minute ventilation (VE), perceptions of breathlessness (RPE-B) and local muscular (RPE-M) exertion and running speeds recorded in the 10-s and 60-s preceding decisions. There was a significant association between decision-making accuracy and the mean HR (p = 0.042; VC = 0.272) and RR (p = 0.024, VC = 0.239) in the 10-s preceding decisions, with significantly more errors observed when HR ≥ 90% of HRmax (OR, 5.39) and RR ≥ 80% of RRpeak (OR, 3.34). Decision-making accuracy was also significantly associated with the mean running speeds performed in the 10-s (p = 0.003; VC = 0.320) and 60-s (p = 0.016; VC = 0.253) preceding decisions, with workloads of ≥250 m·min−1 associated with an increased occurrence of decisional errors (OR, 3.84). Finally, there was a significant association between decision-making accuracy and RPE-B (p = 0.021; VC = 0.287), with a disproportionate number of errors occurring when RPE-B was rated as “very strong” to “maximal” (OR, 7.19). Collectively, the current data offer novel insights into the detrimental effects that high workloads may have upon the decision-making performances of soccer referees. Such information may be useful in designing combined physical and decision-making training programmes that prepare soccer referees for the periods of match play that prove most problematic to their decision-making

Rumen Protozoa Play a Significant Role in Fungal Predation and Plant Carbohydrate Breakdown

The rumen protozoa, alongside fungi, comprise the eukaryotic portion of the rumen microbiome. Rumen protozoa may account for up to 50% of biomass, yet their role in this ecosystem remains unclear. Early experiments inferred a role in carbohydrate and protein metabolism, but due to their close association with bacteria, definitively attributing these functions to the protozoa was challenging. The advent of ‘omic technologies has created opportunities to broaden our understanding of the rumen protozoa. This study aimed to utilise these methods to further our understanding of the role that protozoa play in the rumen in terms of their metabolic capacities, and in doing so, contribute valuable sequence data to reduce the chance of mis or under-representation of the rumen protozoa in meta’omic datasets. Rumen protozoa were isolated and purified using glucose-based sedimentation and differential centrifugation, extracted RNA was Poly(A) fraction enriched and DNase treated before use in a phage-based, cDNA metatranscriptomic library. Biochemical activity testing of the phage library showed 6 putatively positive plaques in response to carboxymethyl cellulose agar (indicative of cellulose activity), and no positive results for tributyrin (indicative of esterase/lipase activity) or egg yolk agar (indicative of proteolysis). Direct sequencing of the cDNA was also conducted using the Illumina HiSeq 2500. The metatranscriptome identified a wealth of carbohydrate-active enzymes which accounted for 8% of total reads. The most highly expressed carbohydrate-active enzymes were glycosyl hydrolases 5 and 11, polysaccharide lyases and deacetylases, xylanases and enzymes active against pectin, mannan and chitin; the latter likely used to digest rumen fungi which contain a chitin-rich cell membrane. Codon usage analysis of expressed genes also showed evidence of horizontal gene transfer, suggesting that many of these enzymes were acquired from the rumen bacteria in an evolutionary response to the carbohydrate-rich environment of the rumen. This study provides evidence of the significant contribution that the protozoa make to carbohydrate breakdown in the rumen, potentially using horizontally acquired genes, and highlights their predatory capacity

A statistics based Digital Twin for the combined consideration of heat treatment and machining for predicting distortion

This paper introduces a novel concept of Digital Twinning of heat treatment and machining for predicting distortion. A set of physical experiments were conducted, and statistical models based on these trials were created. The experiments involved heat-treating AA7075 billets with multiple input conditions and measuring distortion during machining trials. This trained a Gaussian Process machining model to reproduce the real-life behaviour of a part, and to predict distortions. These predictions matched the shape and magnitude of data points of the trials. The paper suggests further refinements of the model. The developed statistical tool enables distortion prediction to produce right-first-time parts