1,721,022 research outputs found
Software to assess the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans
No full textSolitary pulmonary nodules are common, often incidental findings on chest CT scans. The investigation of pulmonary nodules is time-consuming and often leads to protracted follow-up with ongoing radiological surveillance, however, clinical calculators that assess the risk of the nodule being malignant exist to help in the stratification of patients. Furthermore recent advances in interventional pulmonology include the ability to both navigate to nodules and also to perform autofluorescence microendoscopy. In this study we assessed the efficacy of incorporating additional information from label-free fibre-based optical endomicroscopy of the nodule on assessing risk of malignancy. Using image analysis and machine learning approaches, we find that this information does not yield any gain in predictive performance in a cohort of patients.This package contains relevant data and scripts for reproducing the results
presented in the article 'Assessing the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans' by Seth et al. Refer to README for more detailed information
Software and data for endoscopic sensing of alveolar pH
No full textPreviously unobtainable measurements of alveolar pH were obtained using an endoscope-deployable optrode. The pH sensing was achieved using functionalized gold nanoshell sensors and surface enhanced Raman spectroscopy (SERS). The optrode consisted of an asymmetric dual-core optical fiber designed for spatially separating the optical pump delivery and signal collection, in order to circumvent the unwanted Raman signal generated within the fiber. Using this approach, we demonstrate a ~100-fold increase in SERS signal-to-fiber background ratio, and demonstrate multiple site pH sensing with a measurement accuracy of ±0.07 pH units in the respiratory acini of an ex vivo ovine lung model. We also demonstrate that alveolar pH changes in response to ventilation
Hidden in plain light: high-resolution time-resolved fluorescence modelling of lung cancer
No full textFibre-optic fluorescence lifetime-based devices are advanced spectroscopy techniques that can measure tissue autofluorescence (AF). The optical information AF offers provides insights into the tissue’s metabolic and structural composition, as well as its surrounding environment. Therefore, these devices can be used to interrogate tissue. Conventional fluorescence lifetime-based devices typically measure the AF of tissue from broad emission channels. Or where multiple high-resolution channels are measured, the individual decay traces are often averaged into a single channel. Our research uses a novel in-house device, the Extensively Parallel Time-Resolved Fluorescence Spectroscopy (EP-TRFS) device which simultaneously measures high-resolution spectral and temporal fluorescence. We investigate device specific factors within the data collection, such as the instrument response function and sample specific factors such as photobleaching (Chapter 2).
We next present the paper titled “Simultaneous Spectral Temporal Modelling for a Time-Resolved Fluorescence Emission Spectrum”, based on the Multichannel Fluorescence Lifetime Estimation (MuFLE) model (Chapter 3). MuFLE is an efficient computational model developed to explore the unique multi-channel spectroscopy data, simultaneously estimating the emission spectra and the spectral fluorescence lifetime in single and multi-exponential modes. We show the effectiveness of this approach in estimating the emission and spectral fluorescence lifetime of reference samples, and in un-mixing mixed reference samples.
We then present our initial findings of MuFLE when applied to ex vivo lung tissue data, presented in the paper titled “Fibre-optic based Exploration of Lung Cancer Autofluorescence using Spectral Fluorescence Lifetime”, exploring the spectral information single-exponential fluorescence lifetime estimation provides (Chapter 4). The study demonstrates the sensitivity of the spectral fluorescence lifetime shape to the relative concentration of underlying fluorophores, independent of their environment. This study then explores the properties of the spectral fluorescence lifetime in paired ex vivo lung tissue deemed either abnormal or normal by pathologists.
When used in a multi-exponential mode, we finally show the performance of MuFLE in un-mixing endogenous fluorophores simultaneously in both the spectral and temporal domains of ex vivo lung samples from both non-cancerous and cancerous tissue (Chapter 5). We validate the presence of specific un-mixed endogenous fluorophores, using a commercial FLIM setup of paired samples. We also validate the spectral and temporal profile of the endogenous fluorophores when measured benchside and with the expected values estimated in the literature. The identification of the fluorescence molecules responsible for AF changes in ex vivo samples, enable endogenous fluorophore specific label-free tracking. This, in turn, enhances our ability to assess individual fluorescence components contributing to the overall AF variation between non-cancerous and cancerous tissue in vivo.
In conclusion, we have developed and validated the results from a high-resolution fluorescence device, in combination with novel analytical models. This innovative approach when applied to lung tissue diagnosis enables us to gain a deeper understanding of the individual fluorescence components that contribute to the total AF of a tissue sample. By performing a comprehensive assessment of the AF, identifying the underlying sources of the individual signals, and their relative contributions becomes possible, better distinguishing fluorescence changes between cancerous and non-cancerous tissue. Consequently, the integration of this device and MuFLE, if applied in vivo, can potentially facilitate the instantaneous measurement of specific molecular and environmental properties associated with individual endogenous fluorophores in tissue. This approach can provide novel insights, offering a comprehensive understanding of the molecular dynamics in in vivo tissue, label-free and in real-time
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Morphology optimization of ordered chromatography stationary phases: a workflow designed by machine learning and computational fluid dynamics
Full text linkChromatography is a significant separation and analysis method widely used in analytical chemistry and biochemistry. A chromatographic system contains a mobile phase and a stationary phase, where the commonly utilized stationary phase is constructed by randomly packed particles. It has been verified by experiments that the usage of ordered structure for the stationary phase, would improve separation performance. However, due to the long time required for experiments and simulation procedures, there were only a few types of ordered structures evaluated for the chromatography system. This work is to investigate the correlation between column separation performance and ordered morphologies, for figuring out ordered structures with optimal chromatographic performance. Machine learning (ML) technology was applied in this work for finding optimal structures. This method was firstly employed for packing quality analysis of around 25000 experiments of pre-packed columns manufactured for a period of over 10 years. The capability of the ML model was validated to offer predictions of column performance with mean absolute percentage error (MAPE) equal to around 10% for reduced height equivalent to a theoretical plate (ℎ) and 7% for peak asymmetry (Aₛ).Also, the model quantitatively indicated that column backbones were the most influential factor for pre-packed column quality. This work proved the capability of ML to evaluate and predict column performance. To generate large amounts of ordered structures, an algorithm was developed in two-dimensional (2D) chromatography. 2D morphologies were considered as a combination of discrete elements, where each element can be defined as a portion of the mobile phase or the stationary phase.
Then these discrete elements were transformed into numbers 0 and 1 (corresponding to the mobile phase and stationary phase) in the matrix, in which case they can be controlled and altered easily. The total number of possible topologies as well as the demand for computing resources increase exponentially. Thus three types of constraints (principal pathway, symmetry constraint, and porosity constraint) were implemented into the algorithm to reduce the number of generated topologies. The reduction capability of constraints was evaluated and 97% of possible topologies were reduced. These constraints served as a strong method to reduce the time and required computing resources to examine the possible ordered morphologies. The column separation performance was investigated by computational fluid dynamic (CFD) simulations. A large amount of 2D ordered pillar arrays and some discrete unit cells generated by algorithm and constraints were simulated. Based on the simulations of discrete unit cells, it was proven that the chromatographic performance was strongly affected by the homogeneity velocity profiles within the chromatography system.
Structures, such as square and hexagon pillar arrays, having homogenous fluid profiles tended to provide high separation efficiency. In such case, pore-throat ratio, a commonly used parameter in stratigraphy analysis, was proposed for the homogeneity analysis of chromatographic bed due to the strong correlation with column performance and easiness in the determination by experiments. The practicality of pore-throat ratio was proven by the analysis of 2-dimensional (2D) pillar arrays and 3-dimensional (3D) triply periodic minimum surface (TPMS) monoliths. ML methods were then applied to the data set of 2D pillar arrays. The accuracy of the ML model was validated for the prediction of van Deemter curves (smaller than 10% MAPE in general). For seeking the optimal morphologies of 2D pillar arrays, a reinforcement learning system was developed. The particle shape, particle size, and particle radial stretching were selected as the design freedom parameters of optimal pillar arrays. Six example morphologies were suggested by the reinforcement learning system, whose performances were all verified by CFD simulations. The CFD model was further developed with film diffusion, pore diffusion, and adsorption/desorption models considered, to investigate the solute behavior in the porous stationary phase. Considering pore diffusion and adsorption/desorption equilibrium, a comprehensive version of governing equations for different conditions is established with a definition of effective Peclet number. For different model conditions, the corresponding effective Peclet number can be written as a combination of three parts, the original Peclet number Pₑ, the length scale of solid phase and fluid phase (L/Lₛ)² and the system coefficient yₐ. With the definition of yₐ, , the model considering pore diffusion can also be utilized for simulations of pore diffusion and adsorption isotherms. Based on the simulations, it is confirmed that the optimized structures, suggested by machine learning, still maintain the performance superiority for the beds with the porous stationary phase. Overall, this work demonstrates, for the first time, the seeking process of optimal morphologies in chromatography by CFD simulations and ML. The 2D morphologies suggested by the ML tool were validated to have high purification performance.
This method is required to be further developed for the 3D morphologies and porous stationary systems in the future. Nevertheless, the presented approach proves that it is possible to search optimal structures of chromatography columns by CFD modeling and ML. It is expected in the future, the purification performance of chromatography will be further enhanced with optimized ordered structures found by ML
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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