Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimising the benefits of spectral x-ray imaging in material decomposition

The extra energy information provided by spectral x-ray imaging using novel photon counting x-ray detectors may allow for improved decomposition of materials compared to conventional and dual-energy imaging. The information content of spectral x-ray images, however, depends on how the photons are grouped together. This thesis deals with the theoretical aspect of optimising material discrimination in spectral x-ray imaging. A novel theoretical model was developed to map the confidence region of material thicknesses to determine the uncertainties in thickness quantification. Given the thickness uncertainties, photon counts per pixel can be optimised for material quantification in the most dose efficient manner. Minimisation of the uncertainties enables the optimisation of energy bins for material discrimination. Using Monte Carlo simulations based on the BEAMnrc package, material decomposition of up to 3 materials was performed on projection images, which led to the validation of the theoretical model. With the inclusion of scattered radiation, the theoretical optima of bin border energies were accurate to within 2 keV. For the simulated photon counts, excellent agreement was achieved between the theoretical and the BEAMnrc models regarding the signal-to-noise ratio in a decomposed image, particularly for the decomposition of two materials. Finally, this thesis examined the implementation of the Medipix detector. The equalisation of pixel sensitivity variations and the processing of photon counting projection images were studied. Measurements using the Medipix detector demonstrated promising results in the charge summing and the spectroscopic modes of acquisition, even though the spectroscopic performance of the detector was relatively limited due to electronic issues known to degrade the equalisation process. To conclude, the theoretical model is sufficient in providing guidelines for scanning parameters in spectral x-ray imaging and may be applied on spectral projection measurements using e.g. the redesigned MedipixRX detector with improved spectroscopic performance, when it becomes available.
Aiding Oral Squamous Cell Carcinoma diagnosis using Deep learning ConvMixer network

In recent years, Oral squamous cell carcinoma (OSCC) has become one of the world's most prevalent cancers, and it is becoming more prevalent in many populations. The high incidence rate, late diagnosis, and inadequate treatment planning continue to be major concerns. Despite the enhancement in the applications of deep learning algorithms for the medical field, late diagnosis, and approaches toward precision medicine for OSCC patients remain a challenge. Due to a lack of datasets and trained models with low computational costs, the diagnosis, an important cornerstone, is still done manually by oncologists. Although Convolutional neural networks (CNNs) have become the dominant architecture for vision tasks for plenty of years, recent experiments show that Transformer-based models, most noticeably the Vision Transformer (ViT), may out-compete them in some settings. Therefore, in this research, a method called ConvMixer, which combines great features from CNNs and patches based on ViT was applied to an original very small dataset of only 1224 images in total for 2 classes, Normal epithelium of the oral cavity (Normal) and OSCC, 696 slides for 400x magnification and 528 slides for 100x magnification. However, the proposed models with small parameters and data augmentation performed magnificently, with 400x magnification (Accuracy: 99.81% - F1score: 99.87%) and 100x magnification (Accuracy: 99.62% - F1score: 99.77%).
Accurate 3D Positron Range Correction Method for Heterogeneous Material Densities in PET

Objective: The positron range is a fundamental, detector-independent physical limitation to special resolution in positron emission tomography (PET) as it causes a significant blurring of the reconstructed PET images. A major challenge for positron range correction methods is to provide accurate range kernels that inherently incorporate the generally inhomogeneous stopping power, especially at tissue boundaries. In this work, we propose a novel approach to generate accurate three-dimensional (3-D) blurring kernels both in homogenous and heterogeneous media to improve PET spatial resolution. Approach: In the proposed approach, positron energy deposition was approximately tracked along straight paths, depending on the positron stopping power of the underlying material. The positron stopping power was derived from the attenuation coefficient of 511keV gamma photons according to the available PET attenuation maps. Thus, the history of energy deposition is taken into account within the range of kernels. Special emphasis was placed on facilitating the very fast computation of the positron annihilation probability in each voxel. Results: Positron path distributions of 18F in low-density polyurethane were in high agreement with Geant4 simulation at an annihilation probability larger than 10-2~10-3 of the maximum annihilation probability. The Geant4 simulation was further validated with measured 18F depth profiles in these polyurethane phantoms. The tissue boundary of water with cortical bone and lung was correctly modeled. Residual artifacts from the numerical computations were in the range of 1%. The calculated annihilation probability in voxels shows an overall difference of less than 20% compared to the Geant4 simulation. Significance: The proposed method significantly improves spatial resolution for non-standard isotopes by providing accurate range kernels, even in the case of significant tissue inhomogeneities.
Maximizing the impact of limited vaccine supply under different epidemic conditions: a two-city monkeypox modelling analysis

BACKGROUND. In the current global monkeypox outbreak, many jurisdictions have been faced with limited vaccine supply, motivating interest in efficient allocation. We sought to explore optimal vaccine allocation between two linked transmission networks over a short-term time horizon, across a range of epidemic conditions. METHODS. We constructed a deterministic compartmental SVEIR model of monkeypox transmission. We parameterized the model to reflect two representative, weakly connected GBMSM sexual networks (cities) in Ontario. We simulated roll-out of 5000 vaccine doses over 15 days, starting 60 days after epidemic seeding with 10 imported cases. Within this model, we varied: the relative city (network) sizes, epidemic potentials (R0), between-city mixing, and distribution of imported/seed cases between cities. In each context (combination of varied factors), we then identified the "optimal" allocation of doses between cities - resulting in the fewest cumulative infections by day 120. RESULTS. Under our modelling assumptions, we found that a fixed supply of vaccines could generally avert more infections over short-term time horizons when prioritized to: a larger transmission network, a network with more initial infections, and/or a network with greater R0. Greater between-city mixing decreased the influence of initial seed cases, and increased the influence of city R0 on optimal allocation. Under mixed conditions (e.g. fewer seed cases but greater R0), optimal allocation saw doses shared between cities, suggesting that proximity-based and risk-based vaccine prioritization can work in combination to minimize transmission. INTERPRETATION. Prioritization of limited vaccine supply based on network-level risk factors can help minimize transmission during an emerging epidemic. Such prioritization should be grounded in an understanding of context-specific drivers of risk, and should acknowledge the potential connectedness of multiple transmission networks.
A co-designed website (FindWays) to improve mental health literacy of parents of children with mental health problems: Protocol for a pilot randomised controlled trial

Background Mental health problems, such as behavioural and emotional problems, are prevalent in children. These problems can have long lasting, detrimental effects on the child, their parents and society. Most children with a mental health problem do not receive professional help. Those that do get help can face long wait times. While waiting, parents want to learn how they can help their child. To address this need, we co-designed a new website to help parents find ways of helping their childs mental health problem while waiting to get specialist help. Objectives To assess the acceptability and feasibility of a new co-designed website, FindWays, through a pilot randomised controlled trial. The protocol is registered with ISRCTN (ISRCTN64605513). Methods This study will recruit up to 60 parents of children aged two-twelve years old referred to a paediatrician for behavioural and/or emotional problems. Participants will be randomly allocated by computer generated number sequence to either the intervention or control group. Intervention group participants will receive access to the FindWays website to help them manage their childs mental health problem while they wait to see the paediatrician. Acceptability and feasibility will be assessed over the 4-month intervention through mixed methods including: recruitment, adherence, retention, net promoter score (quantitative measures) and semi-structured interviews to gain an in-depth understanding of parents experience and potential adverse effects (qualitative measure). Secondary outcomes measured by parent survey at 4-months post randomisation include child mental health, parent mental health, impact of the childs mental health problem on their functioning and family, and health service use and associated costs. Results Recruitment commenced June 2022 with publication expected in October 2023. Conclusion This study will provide novel data on the acceptability and feasibility of a new website co-designed with parents to help them find ways of managing their childs behaviour and emotions. Ethics approval granted by The Royal Childrens Hospital Human Research Ethics Committee - HREC/75854/RCHM-2021 on 22/4/2022.
A hybrid guided neighborhood search for the disjunctively constrained knapsack problem

In this paper, we investigate the use of a hybrid guided neighborhood search for solving the disjunctively constrained knapsack problem. The studied problem may be viewed as a combination of two NP-hard combinatorial optimization problems: the weighted-independent set and the classical binary knapsack. The proposed algorithm is a hybrid approach that combines both deterministic and random local searches. The deterministic local search is based on a descent method, where both building and exploring procedures are alternatively used for improving the solution at hand. In order to escape from a local optima, a random local search strategy is introduced which is based on a modified ant colony optimization system. During the search process, the ant colony optimization system tries to diversify and to enhance the solutions using some informations collected from the previous iterations. Finally, the proposed algorithm is computationally analyzed on a set of benchmark instances available in the literature. The provided results are compared to those realized by both the Cplex solver and a recent algorithm of the literature. The computational part shows that the obtained results improve most existing solution values.
Decoding molecular recognition of inhibitors targeting HDAC2 via molecular dynamics simulations and configurational entropy estimation

Molecular recognition by enzymes is a complicated process involving thermodynamic energies governing protein-ligand interactions. In order to aid the estimation of inhibitory activity of compounds targeting an enzyme, several computational methods can be employed to dissect this intermolecular contact. Herein, we report a structural dynamics investigation of an epigenetic enzyme HDAC2 in differentiating its binding to various inhibitors within the sub-sites of its active site. Molecular dynamics (MD) simulation was employed to elucidate the intermolecular interactions as well as the dynamics behavior of ligand binding. MD trajectories of five distinct HDAC2-inhibitor complexes reveal that compounds lacking adequate contacts with the opening rim of the active site possess high fluctuation along the cap portion, thus weakening the overall affinity. Key intermolecular interactions determining the effective binding of inhibitors include hydrogen bonds with Gly154, Asp181, and Tyr308; hydrophobic interactions between Phe155/Phe210 and the linker region; and a pi-stacking with Arg39 at the foot pocket. Decomposition of the binding free energy calculated per-residue by MM/PBSA also indicates that the interactions within the internal foot pocket, especially with residues Met35, Leu144, Gly305, and Gly306, can contribute significantly to the ligand binding. Additionally, configurational entropy of the binding was estimated and compared to the scale of the binding free energy in order to assess its contribution to the binding and to differentiate various ligand partners. It was found that the levels of entropic contribution are comparable among a set of structurally similar carbamide ligands, while it is greatly different for the set of unrelated ligands, ranging from 2.75 to 16.38 kcal/mol for the five inhibitors examined. These findings exemplify the importance of assessing molecular dynamics as well as estimating the entropic contribution in evaluating the ligand binding mechanism.
A new kind of tunable multi-channel wavelength demultiplexer based on multilayer MIM plasmonic nanodisk resonators

A new kind of tunable multi-channel wavelength demultiplexer (WDM) based on metal-insulator-metal (MIM) plasmonic nanodisk resonators with a metal block is proposed. The transmission properties of such structure are simulated by the Finite-Difference Time-Domain (FDTD) method, and the eignwavelengths of the disc resonator are calculated theoretically. It is found that the transmission characteristics of the filter can be easily adjusted by changing the geometrical parameters of the metal block of the nanodisk. The multi-channel WDM structure consisting of a plasmonic waveguide and several nanodisk resonators with metal block, by changing the parameter of metal block of nanodisk resonators, the filter shows the resonant mode filter function. Basing on this characteristic, a three-port wavelength demultiplexer is designed, which can separate resonant modes inside the nanodisk with high transmission up to 60%. It can find important potential applications in highly integrated optical circuits.
Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations

Abstract Uncertainties in ocean-mixing parameterizations are primary sources for ocean and climate modeling biases. Due to lack of process understanding, traditional physics-driven parameterizations perform unsatisfactorily in the tropics. Recent advances in the deep-learning method and the new availability of long-term turbulence measurements provide an opportunity to explore data-driven approaches to parameterizing oceanic vertical-mixing processes. Here, we describe a novel parameterization based on an artificial neural network trained using a decadal-long time record of hydrographic and turbulence observations in the tropical Pacific. This data-driven parameterization achieves higher accuracy than current parameterizations, demonstrating good generalization ability under physical constraints. When integrated into an ocean model, our parameterization facilitates improved simulations in both ocean-only and coupled modeling. As a novel application of machine learning to the geophysical fluid, these results show the feasibility of using limited observations and well-understood physical constraints to construct a physics-informed deep-learning parameterization for improved climate simulations.
Observations and Perspectives on Adaptive Immunity to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

Abstract Since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic began 2 years ago, the scientific community has swiftly worked to understand the transmission, pathogenesis, and immune response of this virus to implement public health policies and ultimately project an end to the pandemic. In this perspective, we present our work identifying SARS-CoV-2 epitopes to quantify T-cell responses and review how T cells may help protect against severe disease. We examine our prior studies which demonstrate durable humoral and cell-mediated memory in natural infection and vaccination. We discuss how SARS-CoV-2–specific T cells from either natural infection or vaccination can recognize emerging variants of concern, suggesting that the currently approved vaccines may be sufficient. We also discuss how pre-existing cross-reactive T cells promote rapid development of immune memory to SARS-CoV-2. We finally posit how identifying SARS-CoV-2 epitopes can help us develop a pan-coronavirus vaccine to prepare for future pandemics.
Spatial patterns of Borrelia burgdorferi, Borrelia miyamotoi and Anaplasma phagocytophilum detected in Ixodes spp. ticks from Canadian companion animals, 2019–2020

Increasing temperatures due to climate change have contributed to a northward range expansion of Ixodes scapularis ticks in Canada. These ticks harbour pathogens of public and animal health significance, including Borrelia burgdorferi and Anaplasma phagocytophilum, which cause Lyme disease and anaplasmosis, respectively, in humans, dogs and horses, and Borrelia miyamotoi, which causes a flu‐like relapsing fever in humans. To address the risks associated with these vector‐borne zoonotic diseases, continuous tick surveillance is advised. This study examined spatial patterns of B. burgdorferi, B. miyamotoi and A. phagocytophilum from ticks submitted through a national study on ticks of companion animals. From 1 April 2019 to 31 March 2020, we received a total of 1541 eligible submissions from 94 veterinary clinics across Canada. Individual and pooled samples of a maximum of either 5 I. scapularis, I. pacificus or I. angustus samples from the same animal and of the same life stage were screened using real‐time PCR targeting genes 23S rRNA for Borrelia spp. and msp2 for A. phagocytophilum. Confirmatory testing was conducted on all 23S rRNA positive samples using a duplex assay for ospA and flaB to differentiate B. burgdorferi and B. miyamotoi, respectively. Prevalence estimates were highest (>20%) for B. burgdorferi in southwestern Manitoba, eastern Ontario, southwestern Quebec, New Brunswick and Nova Scotia. Estimates of B. miyamotoi and A. phagocytophilum were much lower (<5%), except for higher A. phagocytophilum (>5%) estimates for southern Manitoba, eastern Ontario and Prince Edward Island. Findings from this study, combined with other surveillance approaches, can be used to guide veterinary and public health approaches for ticks and tick‐borne diseases.
Three new cardenolides from the fruits of Cascabela thevetia (L.) Lippold and their cytotoxic activities

Abstract Phytochemical investigations on the fruits of Cascabela thevetia (L.) Lippold led to obtain three new cardenolides (1–3) and five known analogues (4–7). Their structures were elucidated by means of UV, IR, HR-ESI-MS, 1D and 2D NMR spectroscopic data analysis. Compounds 1 and 2 represent the first examples of naturally occurring cardenolides with 19-nor-5(10)-ene group and α-l-3-demethyl-thevetose, respectively. Compound 3 is a rare C-nor-D-homocardenolide in nature. All isolated cardenolides (1–7) were evaluated for their cytotoxic activities against four human cancer cell lines (MCF-7, HCT-116, HeLa and HepG2), and the results indicated the compounds with sugar units (1, 2, 4, and 5) exhibited stronger cytotoxic activities with IC50 values ranging between 0.022 and 0.308 μM. GRAPHICAL ABSTRACT
Fully Automated Mouse Echocardiography Analysis Using Deep Convolutional Neural Networks.

Echocardiography (echo) is a translationally relevant ultrasound imaging modality widely used to assess cardiac structure and function in preclinical models of heart failure (HF) during research and drug development. Though echo is a very valuable tool, the image analysis is a time consuming, resource demanding process, and is susceptible to inter-reader variability. Recent advancements in deep learning have enabled researchers to automate image processing and reduce analysis time and inter-reader variability in the field of medical imaging. In the present study, we developed a fully automated tool - Mouse Echo Neural Net (MENN) - for the analysis of both long axis brightness (B)-mode and short axis motion (M)-mode images of the left ventricle. MENN is a series of fully convolutional neural networks that were trained and validated using manually segmented B-mode and M-mode echo images of the left ventricle. The segmented images were then used to compute cardiac structural and functional metrics. The performance of MENN was further validated in two preclinical models of HF. MENN achieved excellent correlations (Pearson's r = 0.85 to 0.99) and good to excellent agreement between automated and manual analyses. Further inter-reader variability analysis showed that MENN has better agreements with an expert analyst than both a trained analyst and a novice. Notably, the use of MENN reduced manual analysis time by >92%. In conclusion, we developed an automated echocardiography analysis tool that allows for fast and accurate analysis of B-mode and M-mode mouse echo data and mitigates the issue of inter-reader variability in manual analysis.
Optimal Bidding Strategy of Transactive Agents in Local Energy Markets

Expanding the electricity market into the retail domain calls for inexpensive mass-produced smart devices that enable enable small customers to customers to participate in local energy transactions by managing the energy production/consumption and submitting buy/sell bids to the market. In this context, this paper presents a mathematically proven practical approach for bidding of an autonomous smart transactive agent in local energy markets. To reach this goal, behaviors of both risk-neutral and risk-averse agents selling energy to the market are modeled taking into account expected profit and risk criteria. Subsequently, an optimal multi-step quantity-price bidding strategies of risk-neutral and risk-averse agents are extracted. In this context, this paper: 1) introduces the effective metrics and criteria for evaluating a bidding strategy; 2) provides all theorems and lemmas required for reaching an optimal bidding strategy for either a risk-neutral or risk-averse agent; and 3) evaluates and presents the developed approach for different market environments. The developed methodology is shown to be effective in practical applications especially for local markets.