A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis

This work presents a computationally-efficient, probabilistic approach to model-based damage diagnosis. Given measurement data, probability distributions of unknown damage parameters are estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) sampling. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. While the formulation is general for arbitrary component geometry, damage type, and sensor data, it is applied to the problem of strain-based crack characterization and experimentally validated using full-field strain data from digital image correlation (DIC). Access to full-field DIC data facilitates the study of the effectiveness of strain-based diagnosis as the distance between the location of damage and strain measurements is varied. The ability of the framework to accurately estimate the crack parameters and effectively capture the uncertainty due to measurement proximity and experimental error is demonstrated. Furthermore, surrogate modeling is shown to enable diagnoses on the order of seconds and minutes rather than several days required with the FE model

Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences

Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme

In this paper, defect detection and identification in aluminium joints is investigated based on guided wave monitoring. Guided wave testing is first performed on the selected damage feature from experiments, namely, the scattering coefficient, to prove the feasibility of damage identification. A Bayesian framework based on the selected damage feature for damage identification of three- dimensional joints of arbitrary shape and finite size is then presented. This framework accounts for both modelling and experimental uncertainties. A hybrid wave and finite element approach (WFE) is adopted to predict the scattering coefficients numerically corresponding to different size defects in joints. Moreover, the proposed approach leverages a kriging surrogate model in combination with WFE to formulate a prediction equation that links scattering coefficients to defect size. This equation replaces WFE as the forward model in probabilistic inference, resulting in a significant enhancement in computational efficiency. Finally, numerical and experimental case studies are used to validate the damage identification scheme. An investigation into how the location of sensors can impact the identified results is provided as well.European Union’s Horizon 2020 Marie Skłodowska-Curie 859957Science and Technology Development Fund, Macau SAR (File No.: FDCT/0101/2021/A2, FDCT/001/2021/AGJ and SKL-IOTSC(UM)-2021-2023

The Effect of Hyperthermia on Doxorubicin Therapy and Nanoparticle Penetration in Multicellular Ovarian Cancer Spheroids

The efficient treatment of cancer with chemotherapy is challenged by the limited penetration of drugs into the tumor. Nanoparticles (10 – 100 nanometers) have emerged as a logical choice to specifically deliver chemotherapeutics to tumors, however, their transport into the tumor is also impeded owing to their bigger size compared to free drug moieties. Currently, monolayer cell cultures, as models for drug testing, cannot recapitulate the structural and functional complexity of in-vivo tumors. Furthermore, strategies to improve drug distribution in tumor tissues are also required. In this study, we hypothesized that hyperthermia (43°C) will improve the distribution of silica nanoparticles in three-dimensional multicellular tumor spheroids. Tumor spheroids mimic the functional and histomorphological complexity of in-vivo avascular tumors and are therefore valuable tools to study drug distribution. Ovarian cancer (Skov3) and uterine sarcoma (MES-SA/Dx5) spheroids were generated using the liquid overlay method. The growth ratio and cytotoxicity assays showed that the application of adjuvant hyperthermia with Doxorubicin (DOX) did not yield higher cell killing compared to DOX therapy alone. These results illustrated the role of spheroids in resistance to heat and DOX. In order to study the cellular uptake kinetics of nanoparticles under hyperthermia conditions, the experimental measurements of silica nanoparticle uptake by cells were fitted using a novel inverse estimation method based on Bayesian estimation. This was coupled with advection reaction transport to model nanoparticle transport in spheroids. The model predicted an increase in Area Under the Curve (AUC) and penetration distance (W1/2) that were validated with in-vitro experiments in spheroids. Based on these observations, a novel multifunctional theranostic nanoparticle probe was created for generating highly localized hyperthermia by encapsulating a Near Infrared (NIR) dye, IR820 (for imaging and hyperthermia) and DOX in Organically modified silica nanoparticles (Ormosil). Pegylated Ormosil nanoparticles had an average diameter of 58.2±3.1 nm, zeta potential of -6.9 ± 0.1 mV and high colloidal stability in physiological buffers. Exposure of the IR820 within the nanoparticles to NIR laser led to the generation of hyperthermia as well as release of DOX which translated to higher cell killing in Skov3 cells, deeper penetration of DOX into spheroids and complete destruction of the spheroids. In-vivo bio-distribution studies showed higher fluorescence from organs and increased plasma elimination life of IR820 compared to free IR820. However, possible aggregation of particles on laser exposure and accumulation in lungs still remain a concern

A Bayesian mixture modelling approach for spatial proteomics.

Analysis of the spatial sub-cellular distribution of proteins is of vital importance to fully understand context specific protein function. Some proteins can be found with a single location within a cell, but up to half of proteins may reside in multiple locations, can dynamically re-localise, or reside within an unknown functional compartment. These considerations lead to uncertainty in associating a protein to a single location. Currently, mass spectrometry (MS) based spatial proteomics relies on supervised machine learning algorithms to assign proteins to sub-cellular locations based on common gradient profiles. However, such methods fail to quantify uncertainty associated with sub-cellular class assignment. Here we reformulate the framework on which we perform statistical analysis. We propose a Bayesian generative classifier based on Gaussian mixture models to assign proteins probabilistically to sub-cellular niches, thus proteins have a probability distribution over sub-cellular locations, with Bayesian computation performed using the expectation-maximisation (EM) algorithm, as well as Markov-chain Monte-Carlo (MCMC). Our methodology allows proteome-wide uncertainty quantification, thus adding a further layer to the analysis of spatial proteomics. Our framework is flexible, allowing many different systems to be analysed and reveals new modelling opportunities for spatial proteomics. We find our methods perform competitively with current state-of-the art machine learning methods, whilst simultaneously providing more information. We highlight several examples where classification based on the support vector machine is unable to make any conclusions, while uncertainty quantification using our approach provides biologically intriguing results. To our knowledge this is the first Bayesian model of MS-based spatial proteomics data.LG was supported by the BBSRC Strategic Longer and Larger grant (Award BB/L002817/1) and the Wellcome Trust Senior Investigator Award 110170/Z/15/Z awarded to KSL. PDWK was supported by the MRC (project reference MC_UP_0801/1). CMM was supported by a Wellcome Trust Technology Development Grant (Grant number 108467/Z/15/Z). OMC is a Wellcome Trust Mathematical Genomics and Medicine student supported financially by the School of Clinical Medicine, University of Cambridge. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A Bayesian method for material identification of composite plates via dispersion curves

Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation. A key property of guided waves is the fully defined relationship between central frequency and propagation characteristics (phase velocity, group velocity and wavenumber)—which is described using dispersion curves. For many guided wave-based strategies, accurate dispersion curve information is invaluable, such as group velocity for localisation. From experimental observations of dispersion curves, a system identification procedure can be used to determine the governing material properties. As well as returning an estimated value, it is useful to determine the distribution of these properties based on measured data. A method of simulating samples from these distributions is to use the iterative Markov-Chain Monte Carlo (MCMC) procedure, which allows for freedom in the shape of the posterior. In this work, a scanning-laser Doppler vibrometer is used to record the propagation of Lamb waves in a unidirectional-glass-fibre composite plate, and dispersion curve data for various propagation angles are extracted. Using these measured dispersion curve data, the MCMC sampling procedure is performed to provide a Bayesian approach to determining the dispersion curve information for an arbitrary plate. The distribution of the material properties at each angle is discussed, including the inferred confidence in the predicted parameters. The percentage errors of the estimated values for the parameters were 10–15 points larger when using the most likely estimates, as opposed to calculating from the posterior distributions, highlighting the advantages of using a probabilistic approach

CcrZ is a pneumococcal spatiotemporal cell cycle regulator that interacts with FtsZ and controls DNA replication by modulating the activity of DnaA.

Most bacteria replicate and segregate their DNA concomitantly while growing, before cell division takes place. How bacteria synchronize these different cell cycle events to ensure faithful chromosome inheritance by daughter cells is poorly understood. Here, we identify Cell Cycle Regulator protein interacting with FtsZ (CcrZ) as a conserved and essential protein in pneumococci and related Firmicutes such as Bacillus subtilis and Staphylococcus aureus. CcrZ couples cell division with DNA replication by controlling the activity of the master initiator of DNA replication, DnaA. The absence of CcrZ causes mis-timed and reduced initiation of DNA replication, which subsequently results in aberrant cell division. We show that CcrZ from Streptococcus pneumoniae interacts directly with the cytoskeleton protein FtsZ, which places CcrZ in the middle of the newborn cell where the DnaA-bound origin is positioned. This work uncovers a mechanism for control of the bacterial cell cycle in which CcrZ controls DnaA activity to ensure that the chromosome is replicated at the right time during the cell cycle

Vehicle Detection and Tracking Techniques: A Concise Review

Vehicle detection and tracking applications play an important role for civilian and military applications such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic analysis and vehicle categorizing objectives and may be implemented under different environments changes. In this review, we present a concise overview of image processing methods and analysis tools which used in building these previous mentioned applications that involved developing traffic surveillance systems. More precisely and in contrast with other reviews, we classified the processing methods under three categories for more clarification to explain the traffic systems