467 research outputs found

    An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls

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    This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision- making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices

    Identifying alterations in adipose tissue-derived islet GPCR peptide ligand mRNAs in obesity: implications for islet function

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    In addition to acting as an energy reservoir, white adipose tissue is a vital endocrine organ involved in the modulation of cellular function and the maintenance of metabolic homeostasis through the synthesis and secretion of peptides, known as adipokines. It is known that some of these secretory peptides play important regulatory roles in glycaemic control by acting directly on islet β-cells or on insulin-sensitive tissues. Excess adiposity causes alterations in the circulating levels of some adipokines which, depending on their mode of action, can have pro-inflammatory, pro-diabetic or anti-inflammatory, anti-diabetic properties. Some adipokines that are known to act at β-cells have actions that are transduced by binding to G protein- coupled receptors (GPCRs). This large family of receptors represents ~35% of all current drug targets for the treatment of a wide range of diseases, including type 2 diabetes (T2D). Islets express ~300 GPCRs, yet only one islet GPCR is currently directly targeted for T2D treatment. This deficit represents a therapeutic gap that could be filled by the identification of adipose tissue-derived islet GPCR peptide ligands that increase insulin secretion and overall β-cell function. Thus, by defining their mechanisms of action, there is potential for the development of new pharmacotherapies for T2D. Therefore, this thesis describes experiments which aimed to compare the expression profiles of adipose tissue-derived islet GPCR peptide ligand mRNAs under lean and obese conditions, and to characterise the functional effects of a selected candidate of interest on islet cells. Visceral fat depots were retrieved from high-fat diet-induced and genetically obese mouse models, and from human participants. Fat pads were either processed as whole tissue, or mature adipocyte cells were separated from the stromal vascular fraction (SVF) which contains several other cell populations, including preadipocytes and macrophages. The expression levels of 155 islet GPCR peptide ligand mRNAs in whole adipose tissue or in isolated mature adipocytes were quantified using optimised RNA extraction and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) protocols. Comparisons between lean and obese states in mice models and humans revealed significant modifications in the expression levels of several adipokine mRNAs. As expected, mRNAs encoding the positive control genes, Lep and AdipoQ were quantifiable, with the expression of Lep mRNA increasing and that of AdipoQ mRNA decreasing in obesity. Expression of Ccl4 mRNA, encoding chemokine (C-C motif) ligand 4, was significantly upregulated in whole adipose tissue across all models of obesity compared to their lean counterparts. This coincided with elevated circulating Ccl4 peptide levels. This increase was not replicated in isolated mature adipocytes, indicating that the source of upregulated Ccl4 expression in obesity was the SVF of adipose tissue. Based on this significant increase in Ccl4 mRNA expression within visceral fat and its undetermined effects on β-cell function, Ccl4 was selected for further investigation in MIN6 β-cells and mouse islets. PRESTO-Tango β-arrestin reporter assays were performed to determine which GPCRs were activated by exogenous Ccl4. Experiments using HTLA cells expressing a protease-tagged β- arrestin and transfected with GPCR plasmids of interest indicated that 100ng/mL Ccl4 significantly activated Cxcr1 and Cxcr5, but it was not an agonist at the previously identified Ccl4-target GPCRs Ccr1, Ccr2, Ccr5, Ccr9 and Ackr2. RNA extraction and RT-qPCR experiments using MIN6 β-cells and primary islets from lean mice revealed the expression of Cxcr5 mRNA in mouse islets, but it was absent in MIN6 β-cells. The remaining putative Ccl4 receptors (Ccr1, Ccr2, Ccr5, Ccr9, Cxcr1 and Ackr2) were either absent or present at trace levels in mouse islets and MIN6 β-cells. Recombinant mouse Ccl4 protein was used for functional experiments at concentrations of 5, 10, 50 and 100ng/mL, based on previous reports of biological activities at these concentrations. Trypan blue exclusion testing was initially performed to assess the effect of exogenous Ccl4 on MIN6 β-cell viability and these experiments indicated that all concentrations (5-100ng/mL) were well-tolerated. Since β-cells have a low basal rate of apoptosis, cell death was induced by exposure to the saturated free fatty acid, palmitate, or by a cocktail of pro-inflammatory cytokines (interleukin-1β, tumour necrosis factor-α and interferon-γ). In MIN6 β-cells, Ccl4 demonstrated concentration-dependent protective effects against palmitate-induced and cytokine-induced apoptosis. Conversely, while palmitate and cytokines also increased apoptosis of mouse islets, Ccl4 did not protect islets from either inducer. Quantification of bromodeoxyuridine (BrdU) incorporation into β-cell DNA indicated that Ccl4 caused a concentration-dependent reduction in proliferation of MIN6 β-cells in response to 10% fetal bovine serum (FBS). In contrast, immunohistochemical quantification of Ki67-positive mouse islet β-cells showed no differences in β-cell proliferation between control- and Ccl4-treated islets. Whilst the number of β-cells and δ-cells were unaffected, α- cells were significantly depleted by Ccl4 treatment. Exogenous Ccl4 had no effect on nutrient- stimulated insulin secretion from both MIN6 β-cells and primary mouse islets. The 3T3-L1 preadipocyte cell line was used to assess potential Ccl4-mediated paracrine and/or autocrine signalling within adipose tissue. Ccl4 did not alter the mRNA expression of Pparγ, a master regulator of adipocyte differentiation, but did significantly downregulate the mRNA expression of the crucial adipogenic gene, adiponectin. Oil Red O staining and Western blotting were performed to assess lipid accumulation, and insulin and lipolytic signalling, respectively, and these experiments indicated that the observed Ccl4-induced decrease in adiponectin expression failed to correlate with any changes in adipocyte function. In summary, these data demonstrated anti-apoptotic and anti-proliferative actions of the adipokine, Ccl4, on MIN6 β-cells that were not replicated in mouse islets. The absence of any anti-apoptotic, insulin secretory and/or pro-proliferative effects of Ccl4 in islet β-cells suggests that it is unlikely to play a role in regulating β-cell function via crosstalk between adipose tissue and islets. The divergent functional effects highlight that whilst MIN6 cells are a useful primary β-cell surrogate for some studies, primary islets should always be used to confirm physiological relevance. On the other hand, significant α-cell depletion following Ccl4 treatment suggests a cell-specific function within the islets. Furthermore, Ccl4 impaired adiponectin mRNA expression in adipocytes, although, how adipocyte function is affected as a result requires further investigation. Collectively, these data have contributed increased understanding of the role of obesity in modifying the expression of adipose tissue-derived islet GPCR peptide ligands

    CARLA+: An Evolution of the CARLA Simulator for Complex Environment Using a Probabilistic Graphical Model

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    In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant to the complex urban environment, specifically, highlighting the challenges that are unaddressed and are within the scope of collaborative autonomous driving. To this end, we model the dynamic urban environment leveraging a probabilistic graphical model (PGM). To develop the proposed solution, a realistic simulation environment is required. There are a number of simulators—CARLA (Car Learning to Act), one of the prominent ones, provides rich features and environment; however, it still fails on a few fronts, for example, it cannot fully capture the complexity of an urban environment. Moreover, the classical CARLA mainly relies on manual code and multiple conditional statements, and it provides no pre-defined way to do things automatically based on the dynamic simulation environment. Hence, there is an urgent need to extend the off-the-shelf CARLA with more sophisticated settings that can model the required dynamics. In this regard, we comprehensively design, develop, and implement an extension of a classical CARLA referred to as CARLA+ for the complex environment by integrating the PGM framework. It provides a unified framework to automate the behavior of different actors leveraging PGMs. Instead of manually catering to each condition, CARLA+ enables the user to automate the modeling of different dynamics of the environment. Therefore, to validate the proposed CARLA+, experiments with different settings are designed and conducted. The experimental results demonstrate that CARLA+ is flexible enough to allow users to model various scenarios, ranging from simple controlled models to complex models learned directly from real-world data. In the future, we plan to extend CARLA+ by allowing for more configurable parameters and more flexibility on the type of probabilistic networks and models one can choose. The open-source code of CARLA+ is made publicly available for researchers

    Exploring annotations for deductive verification

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    Flexible temporal constraint management in modularized processes

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    Managing temporal process constraints in modularized processes is an important task, both during the design, as it allows the reuse of temporal (child) process models, and during the checking of temporal properties of processes, as it avoids the necessity of ‘‘unfolding’’ child processes within the main process model. Taking into account the capability of providing modular solutions, modeling and checking temporal features of processes is still an open problem in the context of process-aware information systems. In this paper, we present and discuss a novel approach to represent flexible temporal constraints in modularized time-aware BPMN process models. To support temporal flexibility, allowed task durations are represented through guarded ranges that allow a limited (guarded) restriction of task durations during process execution if it is necessary to guarantee the satisfaction of all temporal constraints. We, then, propose how to derive a compact representation of the overall temporal behavior of such time-aware BPMN models. Such compact representation of child processes allows us to check the dynamic controllability (DC) of a parent timeaware process model without ‘‘unfolding’’ the child process models. Dynamic controllability guarantees that process models can have process instances (i.e., executions) satisfying all the temporal constraints for any possible combination of allowed durations of tasks and child processes. Possible approaches for even more flexibility by solving some kinds of DC violations are then introduced. We use a real process model from a healthcare domain as a motivating example, and we also present a proof-of-concept prototype confirming the concrete applicability of the solutions we propose, followed by an experimental evaluation

    Nature-Based Solutions for Cities

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    Nature-based solutions (NBS) are increasingly being adopted to address climate change, health, and urban sustainability, yet ensuring they are effective and inclusive remains a challenge. Addressing these challenges through chapters by leading experts in both global south and north contexts, this forward-looking book advances the science of NBS in cities and discusses the frontiers for next-generation urban NBS

    It Goes Beyond Product - Business Innovativeness and Consumer's New Values Adoption

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    The concept of consumer behavior in today’s trend of competitiveness has been enriched by the study on consumer’s adaptation to new values. More specifically in this new era of digital technology business has been able to creatively promote values in which consumer’s loyalty is systematically developed. Business sells beyond product. Hierarchical regression and One-way Anova were employed to show the dynamic process of new values adoption. The respondents were Generation Z in Palembang – Indonesia. Within this scheme the process of new values adoption is conditioned by the innovative capacity of the business ie. innovativeness that attracts the market to learn newness. Consequently, consumer has become more advanced in his involvement to adapt with the innovativeness of the business. This conceptual research intends to rationalize the dynamic of consumer’s new values adoption within the frame of business innovativeness

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Higher-order interactions in single-cell gene expression: towards a cybergenetic semantics of cell state

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    Finding and understanding patterns in gene expression guides our understanding of living organisms, their development, and diseases, but is a challenging and high-dimensional problem as there are many molecules involved. One way to learn about the structure of a gene regulatory network is by studying the interdependencies among its constituents in transcriptomic data sets. These interdependencies could be arbitrarily complex, but almost all current models of gene regulation contain pairwise interactions only, despite experimental evidence existing for higher-order regulation that cannot be decomposed into pairwise mechanisms. I set out to capture these higher-order dependencies in single-cell RNA-seq data using two different approaches. First, I fitted maximum entropy (or Ising) models to expression data by training restricted Boltzmann machines (RBMs). On simulated data, RBMs faithfully reproduced both pairwise and third-order interactions. I then trained RBMs on 37 genes from a scRNA-seq data set of 70k astrocytes from an embryonic mouse. While pairwise and third-order interactions were revealed, the estimates contained a strong omitted variable bias, and there was no statistically sound and tractable way to quantify the uncertainty in the estimates. As a result I next adopted a model-free approach. Estimating model-free interactions (MFIs) in single-cell gene expression data required a quasi-causal graph of conditional dependencies among the genes, which I inferred with an MCMC graph-optimisation algorithm on an initial estimate found by the Peter-Clark algorithm. As the estimates are model-free, MFIs can be interpreted either as mechanistic relationships between the genes, or as substructures in the cell population. On simulated data, MFIs revealed synergy and higher-order mechanisms in various logical and causal dynamics more accurately than any correlation- or information-based quantities. I then estimated MFIs among 1,000 genes, at up to seventh-order, in 20k neurons and 20k astrocytes from two different mouse brain scRNA-seq data sets: one developmental, and one adolescent. I found strong evidence for up to fifth-order interactions, and the MFIs mostly disambiguated direct from indirect regulation by preferentially coupling causally connected genes, whereas correlations persisted across causal chains. Validating the predicted interactions against the Pathway Commons database, gene ontology annotations, and semantic similarity, I found that pairwise MFIs contained different but a similar amount of mechanistic information relative to networks based on correlation. Furthermore, third-order interactions provided evidence of combinatorial regulation by transcription factors and immediate early genes. I then switched focus from mechanism to population structure. Each significant MFI can be assigned a set of single cells that most influence its value. Hierarchical clustering of the MFIs by cell assignment revealed substructures in the cell population corresponding to diverse cell states. This offered a new, purely data-driven view on cell states because the inferred states are not required to localise in gene expression space. Across the four data sets, I found 69 significant and biologically interpretable cell states, where only 9 could be obtained by standard approaches. I identified immature neurons among developing astrocytes and radial glial cells, D1 and D2 medium spiny neurons, D1 MSN subtypes, and cell-cycle related states present across four data sets. I further found evidence for states defined by genes associated to neuropeptide signalling, neuronal activity, myelin metabolism, and genomic imprinting. MFIs thus provide a new, statistically sound method to detect substructure in single-cell gene expression data, identifying cell types, subtypes, or states that can be delocalised in gene expression space and whose hierarchical structure provides a new view on the semantics of cell state. The estimation of the quasi-causal graph, the MFIs, and inference of the associated states is implemented as a publicly available Nextflow pipeline called Stator
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