2,539 research outputs found

    Methamphetamine-Induced DNA Double-Stranded Breaks: The Impact of the Dopamine Transporter and Insights Into the Mechanisms of DNA Damage in Mouse Neuro 2a Cells

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    Methamphetamine (METH) abuse remains a global health concern, with emerging evidence highlighting its genotoxic potential. In the central nervous system METH enters dopaminergic cells primarily through the dopamine transporter (DAT), which controls the dynamics of dopamine (DA) neurotransmission by driving the reuptake of extracellular DA into the presynaptic neuronal cell. Additional effects of METH on the storage of DA in synaptic vesicles lead to the dysregulated cytosolic accumulation of DA. Previous studies have shown that after METH disrupts intracellular vesicular stores of DA, the excess DA in the cytosol is rapidly oxidized. This generates an abundance of reactive oxygen species (ROS) that promote oxidative stress leading to cellular damage, including DNA damage. Our investigation begins by establishing a cellular system that overexpresses a functional DAT (GFP-DATC2) and elucidating the role of DAT and DA in METH-induced nuclear DNA damage, with a focus on nuclear DNA double-stranded breaks (DSBs). DNA DSBs, which represent the most deleterious form of DNA damage, were measured using two classical and well established approaches namely, detection of phosphorylated H2AX (gH2AX) and the neutral comet assay. Immunohistochemistry (IHC) experiments reveal an increase in g-H2AX expression in METH-treated N2A GFP-DATC2 cells compared to control N2A GFP cells, highlighting the link between DAT expression and DNA DSB formation. IHC analyses also demonstrated that the DAT blocker GBR 12909 significantly reduces METH-induced DNA DSBs in GFP-DATC2 cells, providing further evidence of DAT\u27s pivotal role in this genotoxic process. Interestingly, the DNA DSBs induced by exposing cells to low concentrations of METH were repaired after METH removal. Investigation using the comet assay to functionally characterize nuclear DNA DSBs shows that co-treatment with METH and inhibitors targeting cytoplasmic ROS production, from DA, such as N-acetyl-l-cysteine (NAC), substantially decreases METH-induced DNA DSBs. Also in regard to DA regulation such as, pargyline and tetrabenazine, inhibitors of monoamine oxidase B (MAO-B) and vesicular monoamine transporter 2 (VMAT2) respectively showed a strong inhibition of METH-induced DNA DSBs. Intriguingly, inhibition of monoamine oxidase A (MAO-A) on the other hand fails to elicit a similar effect, suggesting the distinctive contributions of MAO-B in this context. Our findings also shed light on L-DOPA\u27s dual role in DNA damage, which includes a protective effect in preventing METH-induced DNA DSBs at low concentrations. In summary, our studies unravel the intricate mechanisms underpinning METH-induced DNA DSBs in N2A cells overexpressing DAT. Highlighting the central roles of DAT, DA, ROS, MAO-B, and VMAT2 in the genotoxic processes induced by METH. These insights into the molecular pathways of METH-induced DNA DSBs may guide future therapeutic strategies to mitigate the detrimental effects of METH abuse on genomic stability

    Evolutionary ecology of obligate fungal and microsporidian invertebrate pathogens

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    The interactions between hosts and their parasites and pathogens are omnipresent in the natural world. These symbioses are not only key players in ecosystem functioning, but also drive genetic diversity through co-evolutionary adaptations. Within the speciose invertebrates, a plethora of interactions with obligate fungal and microsporidian pathogens exist, however the known interactions is likely only a fraction of the true diversity. Obligate invertebrate fungal and microsporidian pathogen require a host to continue their life cycle, some of which have specialised in certain host species and require host death to transmit to new hosts. Due to their requirement to kill a host to spread to a new one, obligate fungal and microsporidian pathogens regulate invertebrate host populations. Pathogen specialisation to a single or very few hosts has led to some fungi evolving the ability to manipulate their host’s behaviour to maximise transmission. The entomopathogenic fungus, Entomophthora muscae, infects houseflies (Musca domestica) over a week-long proliferation cycle, resulting in flies climbing to elevated positions, gluing their mouthparts to the substrate surface, and raising their wings to allow for a clear exit from fungal conidia through the host abdomen. These sequential behaviours are all timed to occur within a few hours of sunset. The E. muscae mechanisms used in controlling the mind of the fly remain relatively unknown, and whether other fitness costs ensue from an infection are understudied.European Commissio

    The Comet Interceptor Mission

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    Here we describe the novel, multi-point Comet Interceptor mission. It is dedicated to the exploration of a little-processed long-period comet, possibly entering the inner Solar System for the first time, or to encounter an interstellar object originating at another star. The objectives of the mission are to address the following questions: What are the surface composition, shape, morphology, and structure of the target object? What is the composition of the gas and dust in the coma, its connection to the nucleus, and the nature of its interaction with the solar wind? The mission was proposed to the European Space Agency in 2018, and formally adopted by the agency in June 2022, for launch in 2029 together with the Ariel mission. Comet Interceptor will take advantage of the opportunity presented by ESA’s F-Class call for fast, flexible, low-cost missions to which it was proposed. The call required a launch to a halo orbit around the Sun-Earth L2 point. The mission can take advantage of this placement to wait for the discovery of a suitable comet reachable with its minimum ΔV capability of 600 ms−1. Comet Interceptor will be unique in encountering and studying, at a nominal closest approach distance of 1000 km, a comet that represents a near-pristine sample of material from the formation of the Solar System. It will also add a capability that no previous cometary mission has had, which is to deploy two sub-probes – B1, provided by the Japanese space agency, JAXA, and B2 – that will follow different trajectories through the coma. While the main probe passes at a nominal 1000 km distance, probes B1 and B2 will follow different chords through the coma at distances of 850 km and 400 km, respectively. The result will be unique, simultaneous, spatially resolved information of the 3-dimensional properties of the target comet and its interaction with the space environment. We present the mission’s science background leading to these objectives, as well as an overview of the scientific instruments, mission design, and schedule

    The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

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    In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis

    A decision-making approach for the health-aware energy management of ship hybrid power plants

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    Although autonomous shipping has attracted increasing interest, its further develop-ment requires innovative solutions to operate autonomous ships without the direct in-tervention of human operators. This study aims to develop a health-aware energy management (HAEM) approach for ship hybrid power plants, integrating the health monitoring information from reliability tools with the energy management tools. This approach employs the equivalent consumption minimisation strategy (ECMS) along with a Dynamic Bayesian network (DBN), as well as the utopia decision-making meth-od and a model for the ship hybrid power plant. The HAEM approach is demonstrated for a parallel hybrid power plant of a pilot boat considering realistic operating profiles. The results demonstrate that by employing HAEM approach for the investigated ship power plant operating for 300 hours reduces its failure rate almost fourfold at the cost of fuel consumption increase of around 1.5 %, compared to the respective operation with the ECMS. This study is expected to contribute towards the development of su-pervisory control of autonomous power plants

    Evaluation of In Vitro Distribution and Plasma Protein Binding of Selected Antiviral Drugs (Favipiravir, Molnupiravir and Imatinib) against SARS-CoV-2

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    There are a number of uncertainties regarding plasma protein binding and blood distribution of the active drugs favipiravir (FAVI), molnupiravir (MOLNU) and imatinib (IMA), which were recently proposed as therapeutics for the treatment of COVID-19 disease. Therefore, proton dissociation processes, solubility, lipophilicity, and serum protein binding of these three substances were investigated in detail. The drugs display various degrees of lipophilicity at gastric (pH 2.0) and blood pH (pH 7.4). The determined pKa values explain well the changes in lipophilic character of the respective compounds. The serum protein binding was studied by membrane ultrafiltration, frontal analysis capillary electrophoresis, steady-state fluorometry, and fluorescence anisotropy techniques. The studies revealed that the ester bond in MOLNU is hydrolyzed by protein constituents of blood serum. Molnupiravir and its hydrolyzed form do not bind considerably to blood proteins. Likewise, FAVI does not bind to human serum albumin (HSA) and α1-acid glycoprotein (AGP) and shows relatively weak binding to the protein fraction of whole blood serum. Imatinib binds to AGP with high affinity (logKâ€Č = 5.8–6.0), while its binding to HSA is much weaker (logKâ€Č ≀ 4.0). The computed constants were used to model the distribution of IMA in blood plasma under physiological and ‘acute-phase’ conditions as well

    Hyper-acute effects of sub-concussive soccer headers on brain function and hemodynamics

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    IntroductionSub-concussive head impacts in soccer are drawing increasing research attention regarding their acute and long-term effects as players may experience thousands of headers in a single season. During these impacts, the head experiences rapid acceleration similar to what occurs during a concussion, but without the clinical implications. The physical mechanism and response to repetitive impacts are not completely understood. The objective of this work was to examine the immediate functional outcomes of sub-concussive level impacts from soccer heading in a natural, non-laboratory environment.MethodsTwenty university level soccer athletes were instrumented with sensor-mounted bite bars to record impacts from 10 consecutive soccer headers. Pre- and post-header measurements were collected to determine hyper-acute changes, i.e., within minutes after exposure. This included measuring blood flow velocity using transcranial Doppler (TCD) ultrasound, oxyhemoglobin concentration using functional near infrared spectroscopy imaging (fNIRS), and upper extremity dual-task (UEF) neurocognitive testing.ResultsOn average, the athletes experienced 30.7 ± 8.9 g peak linear acceleration and 7.2 ± 3.1 rad/s peak angular velocity, respectively. Results from fNIRS measurements showed an increase in the brain oxygenation for the left prefrontal cortex (PC) (p = 0.002), and the left motor cortex (MC) (p = 0.007) following the soccer headers. Additional analysis of the fNIRS time series demonstrates increased sample entropy of the signal after the headers in the right PC (p = 0.02), right MC (p = 0.004), and left MC (p = 0.04).DiscussionThese combined results reveal some variations in brain oxygenation immediately detected after repetitive headers. Significant changes in balance and neurocognitive function were not observed in this study, indicating a mild level of head impacts. This is the first study to observe hemodynamic changes immediately after sub-concussive impacts using non-invasive portable imaging technology. In combination with head kinematic measurements, this information can give new insights and a framework for immediate monitoring of sub-concussive impacts on the head

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
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