2,505 research outputs found

    Investigating the Innate Immune Systems of Bats and Their Roles as Zoonotic Viral Reservoirs

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    The zoonotic spillover of viral pathogens from wild animal reservoirs into human populations remains the leading cause of emerging and re-emerging infectious diseases globally. Bats represent important viral reservoirs, notorious for the diversity and richness of the viruses they host, several of which are highly pathogenic when transmitted to humans. Remarkably, bats appear to host an abundance of these viruses without exhibiting any clinical signs of disease. A dominant hypothesis for this ability suggests that bats can control viral replication early in the innate immune response, which acts as the first line of defence against infection. However, bat immunology remains fundamentally understudied, largely due to their high species diversity and the lack of accessible reagents required for bat research. Therefore, in this work we explored and characterised key components of bat innate immunity to gain a better understanding of bats as viral reservoirs and contribute to the currently limited literature. Here, we demonstrated the in vitro transcriptomic response of the bat model species, Pteropus alecto (P.alecto) upon stimulation with the bat henipavirus Cedar virus and also with a type III bat interferon (paIFNλ). These investigations highlighted key transcripts, some of which were immune-related, in the response of bats to the separate stimuli and presents a foundation for further research into significant genes concerned in bat viral infection. Building from genome-wide transcriptomics, three distinctive bat innate immune genes representative of different stages of interferon signalling were selected for comparative genomics and functional characterisation. Our work demonstrated the conservation of genes between bats and humans, including IRF7, IFIT5 and IFI35. Specific findings for IRF7 included its successful translocation to the cell nucleus upon stimulation. IFIT5 and IFI35 were specifically selected for exploration due to previous research demonstrating the respective antiviral and conflicting anti- or pro-viral roles of these genes in humans. Significantly, our research demonstrated the direct antiviral action of P.alecto IFIT5 against negative-sense RNA viruses. Collectively, our findings offer valuable contributions to the field of bat antiviral immunity and provide the framework for future investigative studies into the role and function of the bat innate immune system and bat viral tolerance mechanisms

    Associations between sex, body mass index and the individual microglial response in Alzheimer’s disease

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    Background and objectives 18-kDa translocator protein position-emission-tomography (TSPO-PET) imaging emerged for in vivo assessment of neuroinflammation in Alzheimer’s disease (AD) research. Sex and obesity effects on TSPO-PET binding have been reported for cognitively normal humans (CN), but such effects have not yet been systematically evaluated in patients with AD. Thus, we aimed to investigate the impact of sex and obesity on the relationship between β-amyloid-accumulation and microglial activation in AD. Methods 49 patients with AD (29 females, all Aβ-positive) and 15 Aβ-negative CN (8 female) underwent TSPO-PET ([18F]GE-180) and β-amyloid-PET ([18F]flutemetamol) imaging. In 24 patients with AD (14 females), tau-PET ([18F]PI-2620) was additionally available. The brain was parcellated into 218 cortical regions and standardized-uptake-value-ratios (SUVr, cerebellar reference) were calculated. Per region and tracer, the regional increase of PET SUVr (z-score) was calculated for AD against CN. The regression derived linear effect of regional Aβ-PET on TSPO-PET was used to determine the Aβ-plaque-dependent microglial response (slope) and the Aβ-plaque-independent microglial response (intercept) at the individual patient level. All read-outs were compared between sexes and tested for a moderation effect of sex on associations with body mass index (BMI). Results In AD, females showed higher mean cortical TSPO-PET z-scores (0.91 ± 0.49; males 0.30 ± 0.75; p = 0.002), while Aβ-PET z-scores were similar. The Aβ-plaque-independent microglial response was stronger in females with AD (+ 0.37 ± 0.38; males with AD − 0.33 ± 0.87; p = 0.006), pronounced at the prodromal stage. On the contrary, the Aβ-plaque-dependent microglial response was not different between sexes. The Aβ-plaque-independent microglial response was significantly associated with tau-PET in females (Braak-II regions: r = 0.757, p = 0.003), but not in males. BMI and the Aβ-plaque-independent microglial response were significantly associated in females (r = 0.44, p = 0.018) but not in males (BMI*sex interaction: F(3,52) = 3.077, p = 0.005). Conclusion While microglia response to fibrillar Aβ is similar between sexes, women with AD show a stronger Aβ-plaque-independent microglia response. This sex difference in Aβ-independent microglial activation may be associated with tau accumulation. BMI is positively associated with the Aβ-plaque-independent microglia response in females with AD but not in males, indicating that sex and obesity need to be considered when studying neuroinflammation in AD

    Human Activity Recognition and Fall Detection Using Unobtrusive Technologies

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    As the population ages, health issues like injurious falls demand more attention. Wearable devices can be used to detect falls. However, despite their commercial success, most wearable devices are obtrusive, and patients generally do not like or may forget to wear them. In this thesis, a monitoring system consisting of two 24×32 thermal array sensors and a millimetre-wave (mmWave) radar sensor was developed to unobtrusively detect locations and recognise human activities such as sitting, standing, walking, lying, and falling. Data were collected by observing healthy young volunteers simulate ten different scenarios. The optimal installation position of the sensors was initially unknown. Therefore, the sensors were mounted on a side wall, a corner, and on the ceiling of the experimental room to allow performance comparison between these sensor placements. Every thermal frame was converted into an image and a set of features was manually extracted or convolutional neural networks (CNNs) were used to automatically extract features. Applying a CNN model on the infrared stereo dataset to recognise five activities (falling plus lying on the floor, lying in bed, sitting on chair, sitting in bed, standing plus walking), overall average accuracy and F1-score were 97.6%, and 0.935, respectively. The scores for detecting falling plus lying on the floor from the remaining activities were 97.9%, and 0.945, respectively. When using radar technology, the generated point clouds were converted into an occupancy grid and a CNN model was used to automatically extract features, or a set of features was manually extracted. Applying several classifiers on the manually extracted features to detect falling plus lying on the floor from the remaining activities, Random Forest (RF) classifier achieved the best results in overhead position (an accuracy of 92.2%, a recall of 0.881, a precision of 0.805, and an F1-score of 0.841). Additionally, the CNN model achieved the best results (an accuracy of 92.3%, a recall of 0.891, a precision of 0.801, and an F1-score of 0.844), in overhead position and slightly outperformed the RF method. Data fusion was performed at a feature level, combining both infrared and radar technologies, however the benefit was not significant. The proposed system was cost, processing time, and space efficient. The system with further development can be utilised as a real-time fall detection system in aged care facilities or at homes of older people

    Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool

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    Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things applications, communication and sensing technologies have rapidly evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years. Enabled by significant advancements in electromagnetic (EM) hardware, mmWave and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz, respectively, can be employed for a host of applications. The main feature of THz systems is high-bandwidth transmission, enabling ultra-high-resolution imaging and high-throughput communications; however, challenges in both the hardware and algorithmic arenas remain for the ubiquitous adoption of THz technology. Spectra comprising mmWave and THz frequencies are well-suited for synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide spectrum of tasks like material characterization and nondestructive testing (NDT). This article provides a tutorial review of systems and algorithms for THz SAR in the near-field with an emphasis on emerging algorithms that combine signal processing and machine learning techniques. As part of this study, an overview of classical and data-driven THz SAR algorithms is provided, focusing on object detection for security applications and SAR image super-resolution. We also discuss relevant issues, challenges, and future research directions for emerging algorithms and THz SAR, including standardization of system and algorithm benchmarking, adoption of state-of-the-art deep learning techniques, signal processing-optimized machine learning, and hybrid data-driven signal processing algorithms...Comment: Submitted to Proceedings of IEE

    Morphometric reorganization induced by working memory training: perspective from vertex and network levels

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    Der sich beschleunigende globale Alterungsprozess und die Tatsache, dass sich die kog-nitiven Fähigkeiten mit dem Alter verschlechtern, was sich erheblich auf die Lebensquali-tät älterer Erwachsener auswirkt, insbesondere bei altersbedingten Störungen (z. B. kogni-tiver Beeinträchtigung, Demenz), weisen auf einen dringenden Bedarf an Ansätzen zum Schutz und zur Verbesserung der kognitiven Fähigkeiten sowie an Untersuchungen der neuronalen Substrate altersbedingter Veränderungen und der Neuroplastizität hin. Da man davon ausgeht, dass das Arbeitsgedächtnis (WM) die grundlegende Ursache für altersbe-dingte kognitive Beeinträchtigungen bei einer Vielzahl von kognitiven Fähigkeiten dar-stellt, ist das Arbeitsgedächtnistraining (WMT) zu einem aktuellen Thema und einem be-liebten Ansatz geworden. Frühere Studien haben gezeigt, dass das Arbeitsgedächtnistrai-ning (WMT) die kognitive Leistung verbessert. Die spezifischen Auswirkungen sowie die zugrunde liegenden neurobiologischen Mechanismen sind jedoch nach wie vor um-stritten. Ziel dieser Arbeit ist es, die durch das WMT induzierte neuronale strukturelle Plastizität auf mehreren Ebenen sowie die Verhaltenseffekte des WMT zu untersuchen. In der ers-ten Studie untersuchten wir die topographischen Veränderungen der Morphologie der grauen Substanz durch WMT, indem wir vier strukturelle Metriken (d.h. die kortikale Dicke, das kortikale Volumen, die kortikale Oberfläche und den lokalen Gyrifikationsin-dex, LGI) sowie die subkortikalen Volumina explorierten. Konkret wurden 59 gesunde Probanden mittleren Alters nach dem Zufallsprinzip entweder einem adaptiven WMT oder einer nicht-adaptiven Intervention zugewiesen. Alle Teilnehmer unterzogen sich vor und nach der 8-wöchigen WMT-Phase einer Neurobildgebung sowie kognitiven Tests. Vor und nach dem WMT wurden vier kortikale Metriken auf Scheitelpunktniveau und sieben subkortikale Volumina sowie die globale mittlere kortikale Dicke berechnet. Das wich-tigste Ergebnis war, dass die WMT-Gruppe im Vergleich zur aktiven Kontrollgruppe eine größere Zunahme der kortikalen Faltung in den bilateralen parietalen Regionen zeigte. Die Ergebnisse deuten darauf hin, dass strukturelle Veränderungen durch WMT in WM-bezogenen Regionen, insbesondere in parietalen Regionen, die Verarbeitung einer höhe-ren WM-Belastung erleichtern können. Darüber hinaus könnte die kortikale Faltung das relevanteste und plastischste Merkmal von WM und Lernen sein und WMT-Effekte stär-ker widerspiegeln als andere Metriken. Basierend auf den Ergebnissen der ersten Studie haben wir darüber hinaus untersucht, ob die trainingsinduzierten Effekte des WMT in der kortikalen Faltung auf Vertex-Ebene von topologischen Veränderungen begleitet werden. Zu diesem Zweck untersuchten wir in Studie zwei die durch WMT verursachte Plastizität auf Netzwerkebene mit Hilfe eines strukturellen Kovarianzansatzes (SC), der auf denselben Stichproben basiert. Es wurden gyrifikationsbasierte SC-Matrizen für jede Gruppe vor und nach dem Training sowie lon-gitudinale gyrifikationsbasierte SC-Matrizen erstellt. Innerhalb jeder Gruppe ergab die LGI-basierte SC-Analyse keine Hinweise auf WMT-induzierte Veränderungen der kor-tiko-kortikalen Verbindungen, weder in der WMT- noch in der aktiven Kontrollgruppe. Die Ergebnisse der longitudinalen SC-Analyse (unkorrigiert p < 0,005) zeigten, dass die trainingsinduzierten Veränderungen der kortikalen Faltungsintensität signifikante Unter-schiede zwischen Paaren von parietalen Regionen sowie Paaren von frontalen Regionen aufwiesen. Insgesamt deuten die kombinierten Ergebnisse dieser beiden Studien darauf hin, dass ers-tens WMT neuronale strukturelle Plastizität hervorrufen kann; zweitens die kortikale Fal-tung das relevanteste und plastischste Merkmal von WM und Lernen sein könnte, das die Auswirkungen von WMT besser widerspiegelt als andere Indikatoren auf Vertex-Ebene; und drittens die trainingsinduzierten lokalisierten Veränderungen der kortikalen Faltung von einem ähnlichen Muster vergleichbarer struktureller Veränderungen zwischen ROIs innerhalb der Regionen begleitet wurden. In Zukunft sind weitere Forschungen erforder-lich, um diese Ergebnisse zu wiederholen und zu validieren sowie um trainingsinduzierte topologische und topografische Veränderungen anhand einer breiteren Palette von Metri-ken und Eigenschaften zu untersuchen.The accelerating global aging process and the fact that cognitive abilities deteriorate with age, which has a significant impact on the quality of life of older adults, particularly those with age-related disorders (e.g., cognitive impairment, dementia), all point to an urgent need for approaches to protect and enhance cognitive abilities, as well as studies of the neural substrates of aging-related changes and neuroplasticity. Since working memory (WM) has been assumed to be the fundamental source of age-related cognitive impair-ments in a variety of cognitive abilities, working memory training (WMT) has become a hot topic as well as a popular approach. Previous studies have established that working memory training (WMT) improves cognitive performance. However, the specific effects, as well as the underlying neurobiological mechanisms, remain a matter of controversy. The purpose of this thesis is to investigate WMT-induced neural structural plasticity at multiple levels together with the behavioral effects of WMT. In study one, we investigated the topographic changes of grey matter morphology due to WMT by combining four structural metrics (i.e., cortical thickness (CT), cortical volume (CV), cortical surface area (CSA), and local gyrification index (LGI)) as well as subcortical volumes. Specifically, 59 healthy volunteers between the ages of 50 and 65 were randomly assigned to either an adaptive or a non-adaptive intervention. All participants underwent neuroimaging as well as cognitive testing before and after the 8-week intervention. Four cortical metrics at ver-tex level and seven subcortical volumes, as well as global mean cortical thickness, were calculated before and after the intervention. The most important finding was that the adap-tive WMT group showed greater increases in cortical folding in bilateral parietal regions in comparison to the active control group who performed the non-adaptive intervention. The results indicate that structural changes due to adaptive WMT in WM related regions, particularly parietal regions, may facilitate the processing of a higher WM load. In addi-tion, the cortical folding might be the most relevant and plastic feature of WM and learn-ing, reflecting WMT effects more than other metrics. Based on the findings of study one, we further asked whether the training-induced effects of WMT in cortical folding at vertex-level are accompanied by topological changes. To this end, study two investigated network-level plasticity due to WMT by using the struc-tural covariance (SC) approach based on the same samples. Gyrification based SC matri-ces for each group before and after training, together with longitudinal gyrification SC matrices, were constructed. Within each group, the LGI-based SC analysis revealed no evidence of WMT-induced changes in cortical-cortical connections, either in the WMT or the active control groups. The results of the longitudinal SC analysis (uncorrected p < 0.005) revealed that the training induced changes of cortical folding intensity showed sig-nificant difference between pairs of parietal regions as well as pairs of frontal regions. Overall, the combined findings of these two studies indicate that: firstly, WMT can pro-duce neural structural plasticity; secondly, cortical folding might be the most relevant and plastic feature of WM and learning, better reflecting the effects of WMT than other vertex-level indicators; and thirdly, the training induced localized changes in cortical folding were accompanied by the pattern of similar structural changes between ROIs within the regions. In the future, more research is required to replicate and validate these findings, as well as to investigate training-induced topological and topographic changes using a broader set of metrics and properties

    Efficient 3D Reconstruction, Streaming and Visualization of Static and Dynamic Scene Parts for Multi-client Live-telepresence in Large-scale Environments

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    Despite the impressive progress of telepresence systems for room-scale scenes with static and dynamic scene entities, expanding their capabilities to scenarios with larger dynamic environments beyond a fixed size of a few square-meters remains challenging. In this paper, we aim at sharing 3D live-telepresence experiences in large-scale environments beyond room scale with both static and dynamic scene entities at practical bandwidth requirements only based on light-weight scene capture with a single moving consumer-grade RGB-D camera. To this end, we present a system which is built upon a novel hybrid volumetric scene representation in terms of the combination of a voxel-based scene representation for the static contents, that not only stores the reconstructed surface geometry but also contains information about the object semantics as well as their accumulated dynamic movement over time, and a point-cloud-based representation for dynamic scene parts, where the respective separation from static parts is achieved based on semantic and instance information extracted for the input frames. With an independent yet simultaneous streaming of both static and dynamic content, where we seamlessly integrate potentially moving but currently static scene entities in the static model until they are becoming dynamic again, as well as the fusion of static and dynamic data at the remote client, our system is able to achieve VR-based live-telepresence at close to real-time rates. Our evaluation demonstrates the potential of our novel approach in terms of visual quality, performance, and ablation studies regarding involved design choices

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
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