1,630 research outputs found

    The interplay between Natural Killer cells and Pancreatic Stellate cells in Pancreatic Ductal Adenocarcinoma

    Get PDF
    Pancreatic ductal adenocarcinoma (PDAC) is a disease with dismal prognosis. With five-year survival rates of less than 11%, PDAC is set to become the second leading cause of cancer related deaths by 2040. The role of pancreatic stellate cells in pancreatic ductal adenocarcinoma has been well established. However, to date, little remains know about the interaction between these crucial stromal cells and the innate lymphocytes, natural killer (NK) cells, in PDAC. Herein we demonstrate that naïve NK cells possess the functional efficacy to target and kill both quiescent (qPSC) and activated (aPSC) pancreatic stellate cells. Furthermore, qPSC, but not aPSC education of NK cells resulted in decreased NK cell-mediated cancer cell cytotoxicity. NK-PSC direct co-culture was found to modulate both PSC and NK phenotype, as well as functional changes within NK cells, an effect not observed with TranswellTM separation. Multiplex Luminex ELISA further revealed upregulation of IFN-γ and related chemokines in NK cells co-cultured with PSC (activated/quiescent), suggesting that this pathway may be involved in phenotypic modulation. Through global proteomic analysis we demonstrate NK cell-induced differential protein changes in aPSC versus qPSC. Furthermore, we demonstrate changes in intracellular NK pathways as a result of direct contact with PSCs, indicating a dynamic, bidirectional interaction between these two key players. Using multiplex immunohistochemical analysis, we demonstrate that NK cell proximity to CAFs, and not total NK cell infiltrate is correlated with overall survival in PDAC. Consequently, we suggest that the spatial biology of NK/CAFs may play a prognostic role in PDAC and may potentially be used as a tool for patient stratification Taken together, our results demonstrate a significant bidirectional relationship between NK cells and PSC/CAFs in the context of PDAC, providing novel insight into this crucial cell-cell interaction

    Sound Event Detection by Exploring Audio Sequence Modelling

    Get PDF
    Everyday sounds in real-world environments are a powerful source of information by which humans can interact with their environments. Humans can infer what is happening around them by listening to everyday sounds. At the same time, it is a challenging task for a computer algorithm in a smart device to automatically recognise, understand, and interpret everyday sounds. Sound event detection (SED) is the process of transcribing an audio recording into sound event tags with onset and offset time values. This involves classification and segmentation of sound events in the given audio recording. SED has numerous applications in everyday life which include security and surveillance, automation, healthcare monitoring, multimedia information retrieval, and assisted living technologies. SED is to everyday sounds what automatic speech recognition (ASR) is to speech and automatic music transcription (AMT) is to music. The fundamental questions in designing a sound recognition system are, which portion of a sound event should the system analyse, and what proportion of a sound event should the system process in order to claim a confident detection of that particular sound event. While the classification of sound events has improved a lot in recent years, it is considered that the temporal-segmentation of sound events has not improved in the same extent. The aim of this thesis is to propose and develop methods to improve the segmentation and classification of everyday sound events in SED models. In particular, this thesis explores the segmentation of sound events by investigating audio sequence encoding-based and audio sequence modelling-based methods, in an effort to improve the overall sound event detection performance. In the first phase of this thesis, efforts are put towards improving sound event detection by explicitly conditioning the audio sequence representations of an SED model using sound activity detection (SAD) and onset detection. To achieve this, we propose multi-task learning-based SED models in which SAD and onset detection are used as auxiliary tasks for the SED task. The next part of this thesis explores self-attention-based audio sequence modelling, which aggregates audio representations based on temporal relations within and between sound events, scored on the basis of the similarity of sound event portions in audio event sequences. We propose SED models that include memory-controlled, adaptive, dynamic, and source separation-induced self-attention variants, with the aim to improve overall sound recognition

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

    Full text link
    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    Synthetic Aperture Radar (SAR) Meets Deep Learning

    Get PDF
    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

    On factor models for high-dimensional time series

    Get PDF
    The aim of this thesis is to develop statistical methods for use with factor models for high-dimensional time series. We consider three broad areas: estimation, changepoint detection, and determination of the number of factors. In Chapter 1, we sketch the backdrop for our thesis and review key aspects of the literature. In Chapter 2, we develop a method to estimate the factors and parameters in an approximate dynamic factor model. Specifically, we present a spectral expectation-maximisation (or \spectral EM") algorithm, whereby we derive the E and M step equations in the frequency domain. Our E step relies on the Wiener-Kolmogorov smoother, the frequency domain counterpart of the Kalman smoother, and our M step is based on maximisation of the Whittle Likelihood with respect to the parameters of the model. We initialise our procedure using dynamic principal components analysis (or \dynamic PCA"), and by leveraging results on lag-window estimators of spectral density by Wu and Zaffaroni (2018), we establish consistency-with-rates of our spectral EM estimator of the parameters and factors as both the dimension (N) and the sample size (T) go to infinity. We find rates commensurate with the literature. Finally, we conduct a simulation study to numerically validate our theoretical results. In Chapter 3, we develop a sequential procedure to detect changepoints in an approximate static factor model. Specifically, we define a ratio of eigenvalues of the covariance matrix of N observed variables. We compute this ratio each period using a rolling window of size m over time, and declare a changepoint when its value breaches an alarm threshold. We investigate the asymptotic behaviour (as N;m ! 1) of our ratio, and prove that, for specific eigenvalues, the ratio will spike upwards when a changepoint is encountered but not otherwise. We use a block-bootstrap to obtain alarm thresholds. We present simulation results and an empirical application based on Financial Times Stock Exchange 100 Index (or \FTSE 100") data. In Chapter 4, we conduct an exploratory analysis which aims to extend the randomised sequential procedure of Trapani (2018) into the frequency domain. Specifically, we aim to estimate the number of dynamically loaded factors by applying the test of Trapani (2018) to eigenvalues of the estimated spectral density matrix (as opposed to the covariance matrix) of the data

    Development of a clinical ready cell therapy product with improved functionality

    Get PDF
    Mesenchymal stromal cells (MSCs) currently hold great promise in modifying a plethora of diseases. There have been numerous clinical trials performed using cryopreserved MSCs however still little is known about their exact mechanism of action (MOA) and in vivo cell tracking analysis has proven confusing. The known multi-potency of MSCs has gained the attention of clinicians and researchers far and wide which makes MSCs the most utilized cell type in regenerative medicine, yet still there is only one available MSC product (none in USA or Australia) with market authorisation available for therapeutic use. Currently across most jurisdictions the regulatory bodies require a Phase 3 trial with endpoints met to gain market approval, this has yet to be successfully completed, therefore the development of the MSC product including thorough pilot trials must be of upmost importance and racing to market with an undeveloped product may hinder not only the stature of the organisation but the whole sector. This thesis aimed to present a development story for a pilot study of a cryopreserved MSC final product that was clinical trial ready. In most jurisdictions a Phase 2 trial and onwards should show safety and efficacy. To do that it is imperative that product development covers characteristics of the product, qualified or validated tools for assessing product qualities and development strategies to improve the product qualities in line with targeted clinical outcomes i.e., potency. Donor selection is one of the first and potentially most important steps in the development of an allogenic cell therapy product. We assessed four healthy donors of each sex of a relatively matched age, cultured and fully characterized the cells with the intention to provide data which would help us rank our donors. To do this we applied a matrix approach to determine a donor fit for trial based on «em»in vitro«/em» assessment. This included qualification of assays and assessment of cell characteristics and potency as well as cell secretome characteristics and potency. We were able to identify a characteristic of the wider population in which provided a most suitable donor choice, being female. Female MSC (fMSC) and Male MSC (mMSC) showed similar characteristics in terms of growth, phenotype and MSC secretome (MSC-S) molecules however, when we assessed in vitro immune modulation and the correlation to secretion of potent immunoregulators like Indoleamine 2,3 deoxygenise (IDO1), fMSC consistently outperformed their male counterparts. fMSC consistently suppressed peripheral blood mononuclear cell (PBMC) proliferation significantly more than mMSC. The enhanced immunosuppression of fMSCs was attributed to the production of higher concentrations of the anti-inflammatory IL-1RA, PGE-2, IDO1 and prolonged expression of VCAM-1 post activation relative to mMSCs. In contrast, mMSCs produces more inflammatory G-CSF than fMSCs. Moreover, fMSCs, but not mMSCs induced downregulation of the IL-2 receptor, CD25 and sustained expression of the early T cell activation marker, CD69 in PBMCs thus further highlighting the differences in immunomodulation potentials between the sexes. This analysis allowed us to select fMSC as our first port of call in our quest for a clinical ready MSC product. The multipotency of the MSC has been attributed somewhat not only to the cells themselves but to the secreted molecules, some of which are naturally occurring upon proliferation and others in response to the local micro environmental conditions. The characterisation of the MSC secretome via ligand binding assays, mass spectrometry and microparticle analysis revealed the MSC-S contains a myriad of analytes, including cytokines, chemokines, enzymes, growth factors, extracellular matrix (ECM) proteins and factors involved in ECM remodelling, different types of extracellular vesicles including exosomes, microvesicles, apoptotic bodies and others. Specifically, we identified MSC-S was positive for MCP-1, TGF-β/LAP, IL-6, IL-8, VEGF-A, Eotaxin and RANTES, HGF, TNFR1, TIMP-1, SCGF-b1 and GRO-α, ECM molecules fibronectin and collagen 1 (alpha 1,2 and 4) as well as chaperones HSP27, HSP70 and CLU and microvesicles, likely exosomes with a size of ~120 nm. By employing the MSC-S in combination with DMSO as a cytoprotective agent (CPA) we were able to identify the MSC-S containing molecules ≥10 kDa (S10) was as potent as the unpurified MSC-S as determined by the level of IDO1 expression, whereas all other sub-fractions (0-10 kDa, 0-30 kDa, 0-100 kDa, above 30 kDa and above 100 kDa) had significantly lower expression of IDO1 p<0.05. Further analysis revealed a superiority of the MSC-S10 compared to other CPAs showing increased MSC proliferation and viability post thaw while decreasing MSC apoptotic populations and TNF-α production by Th1 cells. This effect was due to an increase in MSC motility and functionality likely attributed to the MSC-S10 containing anti-oxidative HSP27, HSP70, clusterin as well as cytokines and pro proliferative ECM molecule expression. mRNA-SEQ of the MSC under normal and inflammatory conditions over time revealed the MSC cryopreserved in MSC-S10 showed differentially expressed genes (DEGs) relative to MSC alone. This included a downregulation of stress related DNAJB1, DNAJA4, HSPA6, HSPA1B HSPA1A, SOD2 HSPA7 and HSPH1 and an upregulation of anti-apoptotic BIRC5, BIRC3, BBC3 and BCL2A as well as upregulation of cytoskeletal modelling genes like ARC, AURKA and DPP4. Moreover MSC-S upregulated immunomodulatory genes IL4, G-SCF, IDO1, CCL8, IL33, ITGA2, GBP4 and TNFAIP3. In conclusion we had a developed a fully characterized female MSC with a co-active cytoprotective agent using the purified MSC-S and defined a group of target genes to indicate potency. These measures allowed us to form the opinion we had successfully developed a clinical trial ready MSC therapy

    Precision mass measurements for the astrophysical rp-process and electron cooling of trapped ions

    Get PDF
    Precision mass measurements of rare isotopes with decay half-lives far below one second are of importance to a variety of applications including studies of nuclear structure and nuclear astrophysics as well as tests of fundamental symmetries. The first part of this thesis discusses mass measurements of neutron-deficient gallium isotopes in direct vicinity of the proton drip line. The reported measurements of 60-63Ga were performed with the MR-TOF-MS of TRIUMF's Ion Trap for Atomic and Nuclear Science (TITAN) in Vancouver, Canada. The measurements mark the first direct mass determination of 60Ga and yield a 61Ga mass value three times more precise than the literature value from AME2020. Our 60Ga mass value constrains the location of the proton dripline in the gallium isotope chain and extends the experimentally evaluated IMME for isospin triplets up to A=60. The improved precision of the 61Ga mass has important implications for the astrophysical rapid proton capture process (rp-process). Calculations in a single-zone model demonstrate that the improved mass data substantially reduces uncertainties in the predicted light curves of Type I X-ray bursts. TITAN has demonstrated that charge breeding provides a powerful means to increase the precision and resolving power of Penning trap mass measurements of radioactive ions. However, the charge breeding process deteriorates the ion beam quality, thus mitigating the benefits associated with Penning trap mass spectrometry of highly charged ions (HCI). As a potential remedy for the beam quality loss, a cooler Penning trap has been developed in order to investigate the prospects of electron cooling the HCI prior to the mass measurement. The second part of this thesis reports exploratory studies of electron cooling of singly charged ions in this cooler Penning trap. Comparison of measured ion energy evolutions to a cooling model provides a detailed understanding of the underlying cooling dynamics. Extrapolation of the model enables the deduction of tentative estimates of the expected cooling times for radioactive HCI

    University of Windsor Graduate Calendar 2023 Spring

    Get PDF
    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp

    Electron Thermal Runaway in Atmospheric Electrified Gases: a microscopic approach

    Get PDF
    Thesis elaborated from 2018 to 2023 at the Instituto de Astrofísica de Andalucía under the supervision of Alejandro Luque (Granada, Spain) and Nikolai Lehtinen (Bergen, Norway). This thesis presents a new database of atmospheric electron-molecule collision cross sections which was published separately under the DOI : With this new database and a new super-electron management algorithm which significantly enhances high-energy electron statistics at previously unresolved ratios, the thesis explores general facets of the electron thermal runaway process relevant to atmospheric discharges under various conditions of the temperature and gas composition as can be encountered in the wake and formation of discharge channels

    Denoising Diffusion MRI: Considerations and implications for analysis

    Get PDF
    Development of diffusion MRI (dMRI) denoising approaches has experienced considerable growth over the last years. As noise can inherently reduce accuracy and precision in measurements, its effects have been well characterised both in terms of uncertainty increase in dMRI-derived features and in terms of biases caused by the noise floor, the smallest measurable signal given the noise level. However, gaps in our knowledge still exist in objectively characterising dMRI denoising approaches in terms of both of these effects and assessing their efficacy. In this work, we reconsider what a denoising method should and should not do and we accordingly define criteria to characterise the performance. We propose a comprehensive set of evaluations, including i) benefits in improving signal quality and reducing noise variance, ii) gains in reducing biases and the noise floor and improving, iii) preservation of spatial resolution, iv) agreement of denoised data against a gold standard, v) gains in downstream parameter estimation (precision and accuracy), vi) efficacy in enabling noise-prone applications, such as ultra-high-resolution imaging. We further provide newly acquired complex datasets (magnitude and phase) with multiple repeats that sample different SNR regimes to highlight performance differences under different scenarios. Without loss of generality, we subsequently apply a number of exemplar patch-based denoising algorithms to these datasets, including Non-Local Means, Marchenko-Pastur PCA (MPPCA) in the magnitude and complex domain and NORDIC, and compare them with respect to the above criteria and against a gold standard complex average of multiple repeats. We demonstrate that all tested denoising approaches reduce noise-related variance, but not always biases from the elevated noise floor. They all induce a spatial resolution penalty, but its extent can vary depending on the method and the implementation. Some denoising approaches agree with the gold standard more than others and we demonstrate challenges in even defining such a standard. Overall, we show that dMRI denoising performed in the complex domain is advantageous to magnitude domain denoising with respect to all the above criteria
    corecore