7,600 research outputs found

    Principles of ontophylogenetic development of artificial general intelligence systems based on multi-agent neurocognitive architectures

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    The purpose of the study is to study the possibilities of multigenerational optimization of behavior control systems for agents of general artificial intelligence capable of independently solving a universal range of tasks in a real environment. The main principles of ontophylogenetic synthesis of control systems for agents of general artificial intelligence based on multi-agent neurocognitive architectures have been developed. Methods and algorithms for synthesizing the phenotypes of control systems of intelligent agents according to their genotypes are proposed. A software package for simulating the processes of ontophylogenetic synthesis of multi-agent neurocognitive architectures has been developed and experiments have been carried out to create phenotypes of intelligent agents based on them. A complex genome of an intelligent agent has been developed, the features of a multichromosome genetic algorithm for organizing calculations in the paradigm of multigenerational optimization of multiagent neurocognitive architectures have been established and substantiated. It is shown that multigenerational optimization of the multi-agent neurocognitive architecture of intelligent agents can contribute to the achievement of adaptive resistance to the operating conditions of a general artificial intelligence agent, provide the synthesis of its suboptimal structural and functional scheme, accelerate learning and algorithms for finding solutions to a universal range of problems solved by this agent in its ecological niche

    Viral infections and complications in inflammatory bowel disease

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    This thesis aimed to clarify the impact of several viral infections, their complications and vaccination responses in patients with inflammatory bowel disease (IBD). The first part focuses on two herpes viruses, Epstein-Barr virus (EBV) and cytomegalovirus (CMV), that can cause severe opportunistic infections, mostly as a rare complication of the use of immunosuppressants. In the second part we studied the risk of the premalignant condition cervical intraepithelial neoplasia (CIN) and cervical cancer caused by human papillomavirus (HPV) in women with IBD and we aimed to identify risk factors, in particular by studying the exposure to immunosuppressants in detail. The third part describes an uncommon case of a hepatitis E virus (HEV) infection in a patient using the gut-selective biologic agent vedolizumab. In the last part of this thesis, we focused on vaccination responses to influenza vaccination and severe acute respiratory coronavirus 2 (SARS-Cov-2) vaccination in immunocompromised patients with IBD and other immune-mediated inflammatory diseases (IMID)

    Viral infections and complications in inflammatory bowel disease

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    Ecology of methanotrophs in a landfill methane biofilter

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    Decomposing landfill waste is a significant anthropogenic source of the potent climate-active gas methane (CH₄). To mitigate fugitive methane emissions Norfolk County Council are trialling a landfill biofilter, designed to harness the methane oxidizing potential of methanotrophic bacteria. These methanotrophs can convert CH₄ to CO₂ or biomass and act as CH₄ sinks. The most active CH₄ oxidising regions of the Strumpshaw biofilter were identified from in-situ temperature, CH₄, O₂ and CO₂ profiles. While soil CH₄ oxidation potential was estimated and used to confirm methanotroph activity and determine optimal soil moisture conditions for CH₄ oxidation. It was observed that most CH₄ oxidation occurs in the top 60cm of the biofilter (up to 50% of CH4 input) at temperatures around 50ºC, optimal soil moisture was 10-27.5%. A decrease in in-situ temperature following CH₄ supply interruption suggested the high biofilter temperatures were driven by CH₄ oxidation. The biofilter soil bacterial community was profiled by 16S rRNA gene analysis, with methanotrophs accounting for ~5-10% of bacteria. Active methanotrophs at a range of different incubation temperatures were identified by ¹³CH₄ DNA stable-isotope probing coupled with 16S rRNA gene amplicon and metagenome analysis. These methods identified Methylocella, Methylobacter, Methylocystis and Crenothrix as potential CH₄ oxidisers at the lower temperatures (30ºC/37ºC) observed following system start-up or gas-feed interruption. At higher temperatures typical of established biofilter operation (45ºC/50ºC), Methylocaldum and an unassigned Methylococcaceae species were the dominant active methanotrophs. Finally, novel methanotrophs Methylococcus capsulatus (Norfolk) and Methylocaldum szegediense (Norfolk) were isolated from biofilter soil enrichments. Methylocaldum szegediense (Norfolk) may be very closely related to or the same species as one of the most abundant active methanotrophs in a metagenome from a 50ºC biofilter soil incubation, based on genome-to-MAG similarity. This isolate was capable of growth over a broad temperature range (37-62ºC) including the higher (in-situ) biofilter temperatures (>50ºC)

    Automated identification and behaviour classification for modelling social dynamics in group-housed mice

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    Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results. This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing. A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups. The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup. Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic

    Recombinant spidroins from infinite circRNA translation

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    Spidroins are a diverse family of peptides and the main components of spider silk. They can be used to produce sustainable, lightweight and durable materials for a large variety of medical and engineering applications. Spiders’ territorial behaviour and cannibalism precludes farming them for silk. Recombinant protein synthesis is the most promising way of producing these peptides. However, many approaches have been unsuccessful in obtaining large titres of recombinant spidroins or ones of sufficient molecular weight. The work described here is focused on expressing high molecular weight spidroins from short circular RNA molecules. Mammalian host cells were transfected with designed circular-RNA-producing plasmid vectors. A backsplicing approach was implemented to successfully circularise RNA in a variety of mammalian cell types. This approach could not express any recombinant spidroins based on a variety of qualitative protein assays. Further experiments investigated the reasons behind this. Additionally, due to the diversity of spidroins in a large number of spider lineages, there are potentially many spidroin sequences left to be discovered. A bioinformatic pipeline was developed that accepts transcriptome datasets from RNA sequencing and uses tandem repeat detection and profile HMM annotation to identify novel sequences. This pipeline was specifically designed for the identification of repeat domains in expressed sequences. 21 transcriptomes from 17 different species, encompassing a wide selection of basal and derived spider lineages, were investigated using this pipeline. Six previously undescribed spidroin sequences were discovered. This pipeline was additionally tested in the context of the suckerin protein family. These proteins have recently been investigated for their potential properties in medicine and engineering including adhesion in wet environments. The computational pipeline was able to double the number of suckerins known to date. Further phylogenetic analysis was implemented to expand on the knowledge of suckerins. This pipeline enables the identification of transcripts that may have been overlooked by more mainstream analysis methods such as pairwise homology searches. The spidroins and suckerins discovered by this pipeline may contribute to the large repertoire of potentially useful properties characteristic of this diverse peptide family

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

    Introduction to Psychology

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    Introduction to Psychology is a modified version of Psychology 2e - OpenStax

    Biological function and clinical implication of coagulation proteins during malignant transformation of pancreatic cells

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    The premalignant pancreatic cellular genotype can remain stable for years before rapid malignant transformation, often associated with inflammation. Tissue factor (TF) is an inflammatory modulator regulated by factor VIIa (fVIIa) for its levels and activity. The presence of TF in PDAC and its role in cell proliferation, angiogenesis, and metastasis suggests that TF may be a marker of the inflammatory microenvironment driving precursor lesions of pancreatic cancer. This study examined the in vitro influence of TF on pancreatic epithelial cells and its clinical value in detecting malignant transformation within pancreatic cyst fluid (PCyF). PCyF from 27 patients with pancreatic cystic lesions was analysed in a blinded fashion. TF and fVIIa levels were measured (ELISA), and the fVIIa:TF ratios were calculated. A cut-off value for TF concentration was determined and compared to the conventional assessment parameters (radiological features, CEA and amylase). Patients were categorised into four groups based on cytopathology and two groups based on indication for resection (‘resective’). Significant histological stage-dependent increases in TF levels were observed. Mean TF concentration was significantly higher (p=0.006) in the resective (high-grade dysplasia & malignant; 1.17 ng/ml, 95% CI 0.68, 1.67) vs non-resective group (benign & low-grade dysplasia; 0.27 ng/ml, 95% CI 0.1, 0.44), with a strong positive correlation (r= 0.746, p <0.001, TF cut-off 0.75 ng/ml, AUC 0.877, p=0.002). The fVIIa:TF ratio did not add further value. Incubation of pancreatic cells with recombinant TF resulted in increased expression of a marker of epithelial to mesenchymal transition (Vimentin). This influence was moderated by supplementation with fVIIa in benign (hTERT-HPNE) but not overtly malignant pancreatic cells (AsPC-1). Cyst-associated TF levels appear to correlate with cytological progression to the malignant phenotype and may allow better discrimination (specificity 94%) of the ‘resective’ lesion, reduce healthcare costs and offer a more nuanced tool for monitoring indeterminate cystic lesions
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