6,397 research outputs found

    Studying the interplay between ageing and Parkinson's disease using the zebrafish model

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    Parkinsonā€™s disease (PD) is a neurodegenerative disorder characterised by the loss of dopaminergic neurons in the substantia nigra. Ageing is the major risk factor for developing PD but the interplay between ageing and PD remains elusive. To investigate the effect of ageing on PD-relevant pathological mechanisms, zebrafish mutant lines harbouring mutations in ageing-associated genes (klotho-/-, sirt1-/-, satb1a-/-, satb1b-/- and satb1a-/-;satb1b-/-) were generated, using CRISPR/Cas9 gene editing. Likewise, a chemical model for SIRT1 deficiency was utilised. klotho-/- zebrafish displayed an accelerated ageing phenotype at 3mpf and reduced survival to 6mpf. Dopaminergic neuron number, MPP+ susceptibility and microglial number were unaffected in klotho-/- larvae. NAD+ levels were decreased in 6mpf klotho-/- brains. However, ATP levels and DNA damage were unaffected. sirt1-/- zebrafish did not display a phenotype through adulthood. il-1Ī² and il-6 were not upregulated in sirt1-/- larvae, and chemical inhibition of sirt1 did not increase microglial number. cdkn1a, il-1Ī² and il-6 were not upregulated in satb1a-/- and satb1b-/- larvae. Dopaminergic neuron number and MPP+ susceptibility were unaffected in satb1a-/- larvae. However, satb1b-/- larvae demonstrated a moderate decrease in dopaminergic neuron number but equal susceptibility to MPP+ as satb1b+/+ larvae. Adult satb1a-/- but not adult satb1b-/- zebrafish were emaciated. satb1a-/-;satb1b-/- zebrafish did not display a phenotype through adulthood. Transgenic zebrafish expressing human wildtype Ī±-Synuclein (Tg(eno2:hsa.SNCA-ires-EGFP)) were crossed with klotho-/- and sirt1-/- zebrafish, and treated with a sirt1-specific inhibitor. Neither genetic cross affected survival. The klotho mutation did not increase microglial number in Tg(eno2:hsa.SNCA-ires-EGFP) larvae. Likewise, sirt1 inhibition did not induce motor impairment or cell death in Tg(eno2:hsa.SNCA-ires-EGFP) larvae. In conclusion, the suitability of zebrafish for studying ageing remains elusive, as only 1 ageing-associated mutant line displayed accelerated ageing. However, zebrafish remain an effective model for studying PD-relevant pathological mechanisms due to the availability of CRISPR/Cas9 gene editing, neuropathological and neurobehavioral tools

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Bayesian Forecasting in Economics and Finance: A Modern Review

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    The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem -- model, parameters, latent states -- is able to be quantified explicitly, and factored into the forecast distribution via the process of integration or averaging. Allied with the elegance of the method, Bayesian forecasting is now underpinned by the burgeoning field of Bayesian computation, which enables Bayesian forecasts to be produced for virtually any problem, no matter how large, or complex. The current state of play in Bayesian forecasting in economics and finance is the subject of this review. The aim is to provide the reader with an overview of modern approaches to the field, set in some historical context; and with sufficient computational detail given to assist the reader with implementation.Comment: The paper is now published online at: https://doi.org/10.1016/j.ijforecast.2023.05.00

    Dissecting the mechanisms of transport of herpes simplex virus between Langerhans Cells & dendritic cells in epidermis and dermis following infection of human genital mucosa and skin

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    Herpes Simplex Virus (HSV) is a sexually transmitted infection (STI) that the World Health Organisation (WHO) has deemed a priority for a vaccine. CD8 and CD4T cells are important in the control and clearance of HSV, however no known vaccine has been able to stimulate CD8T cells. The dermal dendritic cells (dDCs) are suspected to play a role. Previously the host lab has shown in human tissue that HSV-1 infection of Langerhans cells (LCs) caused apoptosis and migration of LCs to the dermis, where they were phagocytosed by dDCs (termed HSV viral relay). Very little is known about the mechanisms of this relay. The host lab has also identified a second resident epidermal immune cell, Epi-cDC2s, which are infectable by HSV. This thesis aims to unravel the mechanisms involved in the relay. RNA-seq and cell surface phenotyping on human dDCs subsets showed that was differential chemokine receptor expression. Bead-based immunoassays were used to determine the chemokines produced by HSV-1 infected LCs and Epi-cDC2s,and showed HSV infected LCs produced increased CXCR3 ligands, while HSV infected Epi-cDC2s produced increased CCR5 ligands. The importance of these chemokine axes was investigated using chemotaxis assays. An cyclic immunofluorescent microscopy panel was then developed to investigate whether this migration could be seen in situ in HSV infected foreskin explants. Underneath epidermal foci of infection, there was migration of both cDC1s and cDC2s towards the basement membrane. Under foci of infection there was a greater proportion of cDC2s clustering with LCs. The uptake of HSV infected epidermal cells by the dDC subsets was examined using imaging cytometry. Preliminary results suggest that there were no significant differences between the ability of dDCs to phagocytose HSV infected epidermal cells. Understanding the mechanisms and the role of each dDC subset in the HSV viral relay will determine which dDC subsets are crucial for CD8 and CD4 T cell stimulation

    Spaceā€Scale Resolved Surface Fluxes Across a Heterogeneous, Midā€Latitude Forested Landscape

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    The Earth\u27s surface is heterogeneous at multiple scales owing to spatial variability in various properties. The atmospheric responses to these heterogeneities through fluxes of energy, water, carbon, and other scalars are scale-dependent and nonlinear. Although these exchanges can be measured using the eddy covariance technique, widely used tower-based measurement approaches suffer from spectral losses in lower frequencies when using typical averaging times. However, spatially resolved measurements such as airborne eddy covariance measurements can detect such larger scale (meso-Ī², meso-Ī³) transport. To evaluate the prevalence and magnitude of these flux contributions, we applied wavelet analysis to airborne flux measurements over a heterogeneous mid-latitude forested landscape, interspersed with open water bodies and wetlands. The measurements were made during the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors intensive field campaign. We ask, how do spatial scales of surface-atmosphere fluxes vary over heterogeneous surfaces across the day and across seasons? Measured fluxes were separated into smaller-scale turbulent and larger-scale mesoscale contributions. We found significant mesoscale contributions to sensible and latent heat fluxes through summer to autumn which would not be resolved in single-point tower measurements through traditional time-domain half-hourly Reynolds decomposition. We report scale-resolved flux transitions associated with seasonal and diurnal changes of the heterogeneous study domain. This study adds to our understanding of surface-atmospheric interactions over unstructured heterogeneities and can help inform multi-scale model-data integration of weather and climate models at a sub-grid scale

    Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks

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    We develop a novel graph-based trainable framework to maximize the weighted sum energy efficiency (WSEE) for power allocation in wireless communication networks. To address the non-convex nature of the problem, the proposed method consists of modular structures inspired by a classical iterative suboptimal approach and enhanced with learnable components. More precisely, we propose a deep unfolding of the successive concave approximation (SCA) method. In our unfolded SCA (USCA) framework, the originally preset parameters are now learnable via graph convolutional neural networks (GCNs) that directly exploit multi-user channel state information as the underlying graph adjacency matrix. We show the permutation equivariance of the proposed architecture, which is a desirable property for models applied to wireless network data. The USCA framework is trained through a stochastic gradient descent approach using a progressive training strategy. The unsupervised loss is carefully devised to feature the monotonic property of the objective under maximum power constraints. Comprehensive numerical results demonstrate its generalizability across different network topologies of varying size, density, and channel distribution. Thorough comparisons illustrate the improved performance and robustness of USCA over state-of-the-art benchmarks.Comment: Published in IEEE Transactions on Wireless Communication

    Nanoprobes for Tumor Theranostics

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    This book reports cutting-edge technology in nanoprobes or nanobiomaterials used for the accurate diagnosis and therapy of tumors, involving a multidisciplinary of chemistry, materials science, oncology, biology, and medicine

    Molecular Mechanisms and Therapies of Colorectal Cancer

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    Colorectal cancer (CRC) is currently the third leading cause of cancer-related mortality, with 1.9 million incidence cases and 0.9 million deaths worldwide. The global number of new CRC cases is predicted to reach 3.2 million in 2040, based on the projection of aging, population growth, and human development.In clinics, despite advances of diagnosis and surgical procedures, 20% of the patients with CRC present with metastasis at the time of diagnosis, caused by residual tumor cells that have spread to distant organs prior to surgery, affecting the patient survival rate. Standard systemic chemotherapy, alternative therapies that target mechanisms involved in cancer progression and metastasis, immunotherapy, and combination therapies are the major CRC-treatment strategies. In the advanced stage of CRC the transforming growth factor-beta (TGF-Ī²) plays an oncogenic role by promoting cancer cell proliferation, cancer cell self-renewal, epithelial-to-mesenchymal transition, invasion, tumor progression, metastatic spread, and immune escape. Furthermore, high levels of TGF-Ī²1 confers poor prognosis and is associated with early recurrence after surgery, resistance to chemo- or immunotherapy, and shorter survival. Based on the body of experimental evidence indicating that TGF-Ī² signaling has the potential to be a good therapeutic target in CRC, several anti-TGF-Ī² drugs have been investigated in cancer clinical trials. Here, we presented a comprehensive collection of manuscripts regarding studies on targeting the TGF-Ī² signaling in CRC to improve patientā€™s prognosis and personalized treatments

    Antimicrobial Peptides Aka Host Defense Peptides ā€“ From Basic Research to Therapy

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    This Special Issue reprint will address the most current and innovative developments in the field of HDP research across a range of topics, such as structure and function analysis, modes of action, anti-microbial effects, cell and animal model systems, the discovery of novel host-defense peptides, and drug development

    Factorized Fusion Shrinkage for Dynamic Relational Data

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    Modern data science applications often involve complex relational data with dynamic structures. An abrupt change in such dynamic relational data is typically observed in systems that undergo regime changes due to interventions. In such a case, we consider a factorized fusion shrinkage model in which all decomposed factors are dynamically shrunk towards group-wise fusion structures, where the shrinkage is obtained by applying global-local shrinkage priors to the successive differences of the row vectors of the factorized matrices. The proposed priors enjoy many favorable properties in comparison and clustering of the estimated dynamic latent factors. Comparing estimated latent factors involves both adjacent and long-term comparisons, with the time range of comparison considered as a variable. Under certain conditions, we demonstrate that the posterior distribution attains the minimax optimal rate up to logarithmic factors. In terms of computation, we present a structured mean-field variational inference framework that balances optimal posterior inference with computational scalability, exploiting both the dependence among components and across time. The framework can accommodate a wide variety of models, including dynamic matrix factorization, latent space models for networks and low-rank tensors. The effectiveness of our methodology is demonstrated through extensive simulations and real-world data analysis
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