We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can ...exploit the full detector granularity, while natively managing the event sparsity and arbitrarily complex detector geometries. We introduce two distance-weighted graph network architectures, dubbed
GarNet
and
GravNet
layers, and apply them to a typical particle reconstruction task. The performance of the new architectures is evaluated on a data set of simulated particle interactions on a toy model of a highly granular calorimeter, loosely inspired by the endcap calorimeter to be installed in the CMS detector for the High-Luminosity LHC phase. We study the clustering of energy depositions, which is the basis for calorimetric particle reconstruction, and provide a quantitative comparison to alternative approaches. The proposed algorithms provide an interesting alternative to existing methods, offering equally performing or less resource-demanding solutions with less underlying assumptions on the detector geometry and, consequently, the possibility to generalize to other detectors.
Elastomers are employed in the field of acoustics as sound enhancers and making tissue‐mimicking phantoms for ultrasound imaging due to their good viscoelastic properties. Polyglycerol sebacate (PGS) ...is a relatively new biocompatible, biodegradable elastomer with low elastic modulus prepared via two‐step melt condensation reaction. The first step yields a soluble pre‐polymer while the second results in a cured insoluble tough elastomer. The elasticity of polymer is dependent upon curing time. Attractive aspects of pre‐PGS to be used as ultrasound contrast agent lie in its excellent viscoelastic properties, that is, low elastic modulus, near to lipids. This can give it acoustic properties of lipid microbubbles while keeping a stable response of polymeric microbubbles. In this study, pre‐PGS was synthesized via the melt‐condensation method and characterized by FTIR, NMR, TGA, melt flow index, contact angle, titration, and uniaxial compressive testing. Viscosity was measured by the Ostwald technique. These viscoelastic measurements were used to predict the acoustic response of pre‐PGS shell‐based microbubbles by running equations in MATLAB. Pre‐PGS showed an elastic modulus of 1.26 MPa and thermal stability up to 100°C. PGS showed much more elasticity than commercially available Sonovue ultrasound contrast agent, also it has low viscosity (0.016 Pa s) and density (1186 kg/m), due to which it has a low damping coefficient (4.32 × 106) compared to Sonovue (7.63 × 107), giving better oscillations in an acoustic field with higher scattering cross‐sectional area than standard Sonovue microbubbles. Based on these simulations pre‐PGS with 50%–60% degree of esterification showed excellent viscoelastic properties which make it an ideal material for microbubble ultrasound contrast agent.
Polyglycerol sebacate (PGS) is a biocompatible elastomer prepared via a two‐step melt condensation reaction. The mechanical properties of the cured polymer have been extensively explored for tissue engineering applications; however, properties of pre‐polymer (pre‐PGS) remained limited. The study focuses on the rheological characterization of pre‐PGS and its potential as an ultrasound contrast agent predicted via its oscillatory response in a simulated acoustic field.
According to WHO 2019, Hepatocellular carcinoma (HCC) is the fourth highest cause of cancer death worldwide. More precise diagnostic models are needed to enhance early HCC and cirrhosis quick ...diagnosis, treatment, and survival. Breath biomarkers known as volatile organic compounds (VOCs) in exhaled air can be used to make rapid, precise, and painless diagnoses. Gas chromatography and mass spectrometry (GCMS) are utilized to diagnose HCC and cirrhosis VOCs. In this investigation, metabolically generated VOCs in breath samples (n = 35) of HCC, (n = 35) cirrhotic, and (n = 30) controls were detected via GCMS and SPME. Moreover, this study also aims to identify diagnostic VOCs for distinction among HCC and cirrhosis liver conditions, which are most closely related, and cause misleading during diagnosis. However, using gas chromatography-mass spectrometry (GC-MS) to quantify volatile organic compounds (VOCs) is time-consuming and error-prone since it requires an expert. To verify GC-MS data analysis, we present an in-house R-based array of machine learning models that applies deep learning pattern recognition to automatically discover VOCs from raw data, without human intervention. All-machine learning diagnostic model offers 80% sensitivity, 90% specificity, and 95% accuracy, with an AUC of 0.9586. Our results demonstrated the validity and utility of GCMS-SMPE in combination with innovative ML models for early detection of HCC and cirrhosis-specific VOCs considered as potential diagnostic breath biomarkers and showed differentiation among HCC and cirrhosis. With these useful insights, we can build handheld e-nose sensors to detect HCC and cirrhosis through breath analysis and this unique approach can help in diagnosis by reducing integration time and costs without compromising accuracy or consistency.
Cleantech: Prospects and Challenges Shakeel, Shah Rukh
Journal of innovation management (Porto),
08/2021, Letnik:
9, Številka:
2
Journal Article
Recenzirano
Odprti dostop
The issue of climate change, greenhouse gas emissions, global warming, and their effect on nature and the ecosystem has raised serious concerns. The desire to sustain economic growth and development ...while keeping a check on the environmental footprints is one of the leading challenges the contemporary world is currently facing. To ensure sustained growth, there is a need for technologies and solutions that has the potential to meet industrial needs without compromising the environment. Cleantech offers a possibility to address these needs in a sustainable and environmentally friendly manner. Cleantech, being an umbrella term, is often confused and misunderstood, in terms of its definition and scope. This study seeks to explore what cleantech actually is, how this sector came into prominence, what are the driving factors behind its surge, and what kind of socio-economic, technical, and regulatory prerequisites are necessary for the advancement of this sector.
Display omitted
Drug delivery to the posterior eye is limited by epithelial and mucosal barriers limiting the topical administration of drugs leading to invasive modes of repeated long-term painful ...administration of drugs. Several constructs of liposomes have been prepared to counter this challenge yet are often limited by size and surface charge resulting in poor encapsulation efficiency, low retention time, and poor permeability. In the present study, chitosan coated liposomes (CCL) were prepared to address these challenges. Conventional liposomes encapsulating Triamcinolone Acetonide (TA) were compared with their chitosan coated counterpart for drug loading and release studies. CCL showed a higher encapsulation efficiency (74%), and a highly positive surface charge (+41.1Mv), increased retention time and sustained release. Choroidal neovascularization (CNV) rat models were generated to assess the efficiency of CCLs as nanocarriers in drug delivery. Significant amount of TA was found to be present and retaining in the eye after fifteen days of treatment with CCL, as shown by HPLC analysis. The results showed successful penetration of the construct via corneal mucosal barrier and its accumulation in vitreous body. The analysis shows that this chitosan based liposomal construct can be employed as a potential topical delivery system for treating posterior segment diseases.
Current thrombolytic therapies for deep venous thrombosis are limited due to the wide side effect profile. Contrast mediated sonothrombolysis is a promising approach for thrombus treatment. The ...current study examines the effectiveness of
in vitro
streptokinase (SK) loaded phase-change nanodroplet (PCND) mediated sonothrombolysis at 7 MHz for the diagnosis of deep venous thrombosis. Lecithin shell and perfluorohexane core nanodroplets were prepared
via
the thin-film hydration method and morphologically characterized. Sonothrombolysis was performed at 7 MHz at different mechanical indexes of samples
i.e.
, only sonothrombolysis, PCND mediated sonothrombolysis, sonothrombolysis with SK and SK loaded PCND mediated sonothrombolysis. Thrombolysis efficacy was assessed by measuring clot weight changes during 30 min US exposure, recording the mean gray intensity from the US images of the clot by computer software ImageJ, and spectrophotometric quantification of the hemoglobin in the clot lysate. In 15 minutes of sonothrombolysis performed at high mechanical index (0.9 and 1.2), SK loaded PCNDs showed a 48.61% and 74.29% reduction of mean gray intensity. At 0.9 and 1.2 MI, 86% and 92% weight loss was noted for SK-loaded PCNDs in confidence with spectrophotometric results. A significant difference (
P
< 0.05) was noted for SK-loaded PCND mediated sonothrombolysis compared to other groups. Loading of SK inside the PCNDs enhanced the efficacy of sonothrombolysis. An increase in MI and time also increased the efficacy of sonothrombolysis. This
in vitro
study showed the potential use of SK-loaded perfluorohexane core PCNDs as sonothrombolytic agents for deep venous thrombosis.
Contrast enhanced sonothrombolysis using streptokinase loaded phase change nano-droplets.
Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, ...insights may be less important than the production of an accurate predictive model for future use. In that case, users are more interested in generating of diverse and robust predictive models, verifying their performance on holdout data, and selecting the most suitable model for their usage scenario. In this paper, we consider the concept of Exploratory Model Analysis (EMA), which is defined as the process of discovering and selecting relevant models that can be used to make predictions on a data source. We delineate the differences between EMA and the well‐known term exploratory data analysis in terms of the desired outcome of the analytic process: insights into the data or a set of deployable models. The contributions of this work are a visual analytics system workflow for EMA, a user study, and two use cases validating the effectiveness of the workflow. We found that our system workflow enabled users to generate complex models, to assess them for various qualities, and to select the most relevant model for their task.
Recently, dual-mode imaging systems merging magnetic resonance imaging (MRI) and ultrasound (US) have been developed. Designing a dual-mode contrast agent is complex due to different mechanisms of ...enhancement. Herein, we describe novel phase change nanodroplets (PCNDs) with perfluoropentane encapsulated in a pre-polyglycerol sebacate (pre-PGS) shell loaded with polyethylene glycol (PEG)-coated iron oxide nanoparticles as having a dual-mode contrast agent effect. Iron oxide nanoparticles were prepared via the chemical co-precipitation method and PCNDs were prepared via the solvent displacement technique. PCNDs showed excellent enhancement in the in vitro US much more than Sonovue® microbubbles. Furthermore, they caused a susceptibility effect resulting in a reduction of signal intensity on MRI. An increase in the concentration of nanoparticles caused an increase in the MR contrast effect but a reduction in US intensity. The concentration of nanoparticles in a shell of PCNDs was optimized to obtain a dual-mode contrast effect. Biocompatibility, hemocompatibility, and immunogenicity assays showed that PCNDs were safe and non-immunogenic. Another finding was the dual-mode potential of unloaded PCNDs as T1 MR and US contrast agents. Results suggest the excellent potential of these PCNDs for use as dual-mode contrast agents for both MRI and US.
Technology-assisted clinical diagnosis has gained tremendous importance in modern day healthcare systems. To this end, multimodal medical image fusion has gained great attention from the research ...community. There are several fusion algorithms that merge Computed Tomography (CT) and Magnetic Resonance Images (MRI) to extract detailed information, which is used to enhance clinical diagnosis. However, these algorithms exhibit several limitations, such as blurred edges during decomposition, excessive information loss that gives rise to false structural artifacts, and high spatial distortion due to inadequate contrast. To resolve these issues, this paper proposes a novel algorithm, namely Convolutional Sparse Image Decomposition (CSID), that fuses CT and MR images. CSID uses contrast stretching and the spatial gradient method to identify edges in source images and employs cartoon-texture decomposition, which creates an overcomplete dictionary. Moreover, this work proposes a modified convolutional sparse coding method and employs improved decision maps and the fusion rule to obtain the final fused image. Simulation results using six datasets of multimodal images demonstrate that CSID achieves superior performance, in terms of visual quality and enriched information extraction, in comparison with eminent image fusion algorithms.
PDF
