Active Ultrasound Pattern Injection System (AUSPIS) for Interventional Tool Guidance

Accurate tool tracking is a crucial task that directly affects the safety and effectiveness of many interventional medical procedures. Compared to CT and MRI, ultrasound-based tool tracking has many advantages, including low cost, safety, mobility and ease of use. However, surgical tools are poorly visualized in conventional ultrasound images, thus preventing effective tool tracking and guidance. Existing tracking methods have not yet provided a solution that effectively solves the tool visualization and mid-plane localization accuracy problem and fully meets the clinical requirements. In this paper, we present an active ultrasound tracking and guiding system for interventional tools. The main principle of this system is to establish a bi-directional ultrasound communication between the interventional tool and US imaging machine within the tissue. This method enables the interventional tool to generate an active ultrasound field over the original imaging ultrasound signals. By controlling the timing and amplitude of the active ultrasound field, a virtual pattern can be directly injected into the US machine B mode display. In this work, we introduce the time and frequency modulation, mid-plane detection, and arbitrary pattern injection methods. The implementation of these methods further improves the target visualization and guiding accuracy, and expands the system application beyond simple tool tracking. We performed ex vitro and in vivo experiments, showing significant improvements of tool visualization and accurate localization using different US imaging platforms. An ultrasound image mid-plane detection accuracy of ±0.3 mm and a detectable tissue depth over 8.5 cm was achieved in the experiment. The system performance is tested under different configurations and system parameters. We also report the first experiment of arbitrary pattern injection to the B mode image and its application in accurate tool tracking.     

Real-time tumor tracking with an artificial neural networks-based method: A feasibility study

The purpose of this study was to develop and assess the performance of a tumor tracking method designed for application in radiation therapy. This motion compensation strategy is currently applied clinically only in conventional photon radiotherapy but not in particle therapy, as greater accuracy in dose delivery is required.We proposed a tracking method that exploits artificial neural networks to estimate the internal tumor trajectory as a function of external surrogate signals. The developed algorithm was tested by means of a retrospective clinical data analysis in 20 patients, who were treated with state of the art infra-red motion tracking for photon radiotherapy, which is used as a benchmark. Integration into a hardware platform for motion tracking in particle therapy was performed and then tested on a moving phantom, specifically developed for this purpose.Clinical data show that a median tracking error reduction up to 0.7 mm can be achieved with respect to state of the art technologies. The phantom study demonstrates that a real-time tumor position estimation is feasible when the external signals are acquired at 60 Hz.The results of this work show that neural networks can be considered a valuable tool for the implementation of high accuracy real-time tumor tracking methodologies.     

Quantifying Intracellular Particle Flows by DIC Object Tracking

Label-free imaging techniques such as differential interference contrast (DIC) allow the observation of cells and large subcellular structures in their native, unperturbed states with minimal exposure to light. The development of robust computational image-analysis routines is vital to quantitative label-free imaging. The reliability of quantitative analysis of time-series microscopy data based on single-particle tracking relies on accurately detecting objects as distinct from the background, i.e., segmentation. Typical approaches to segmenting DIC images either involve converting images to those resembling phase contrast, mimicking the optics of DIC object formation, or using the morphological properties of objects. Here, we describe MATLAB based, single-particle tracking tool with a GUI for mobility analysis of objects from in vitro and in vivo DIC time-series microscopy. The tool integrates contrast enhancement with multiple modified Gaussian filters, automated threshold detection for segmentation and minimal distance-based two-dimensional single-particle tracking. We compare the relative performance of multiple filters and demonstrate the utility of the tool for DIC object tracking (DICOT). We quantify subcellular dynamics of a time series of Caenorhabditis elegans embryos in the one-celled stage by detecting birefringent yolk granules in the cytoplasm with high precision. The resulting two-dimensional map of oscillatory dynamics of granules quantifies the cytoplasmic flows driven by anaphasic spindle oscillations. The frequency of oscillations across the anterior-posterior (A-P) and transverse axes of the embryo correspond well with the reported frequency of spindle oscillations. We validate the quantitative accuracy of our method by tracking the in vitro diffusive mobility of micron-sized beads in glycerol solutions. Estimates of the diffusion coefficients of the granules are used to measure the viscosity of a dilution series of glycerol. Thus, our computational method is likely to be useful for both intracellular mobility and in vitro microrheology.     

Non-bias-limited tracking of spherical particles, enabling nanometer resolution at low magnification

We present a three-dimensional tracking routine for nondiffraction-limited particles, which significantly reduces pixel bias. Our technique allows for increased resolution compared to that of previous methods, especially at low magnification or at high signal/noise ratio. This enables tracking with nanometer accuracy in a wide field of view and tracking of many particles. To reduce bias induced by pixelation, the tracking algorithm uses interpolation of the image on a circular grid to determine the x-, y-, and z-positions. We evaluate the proposed algorithm by tracking simulated images and compare it to well-known center-of-mass and cross-correlation methods. The final resolution of the described method improves up to an order of magnitude in three dimensions compared to conventional tracking methods. We show that errors in x,y-tracking can seriously affect z-tracking if interpolation is not used. We validate our results with experimental data obtained for conditions matching those used in the simulations. Finally, we show that the increased performance of the proposed algorithm uniquely enables it to extract accurate data for the persistence length and end-to-end distance of 107 DNA tethers in a single experiment.     

Fundamentals of the logarithmic measure for revealing multimodal diffusion

We develop a theoretical foundation for a time-series analysis method suitable for revealing the spectrum of diffusion coefficients in mixed Brownian systems, for which no prior knowledge of particle distinction is required. This method is directly relevant for particle tracking in biological systems, in which diffusion processes are often nonuniform. We transform Brownian data onto the logarithmic domain, in which the coefficients for individual modes of diffusion appear as distinct spectral peaks in the probability density. We refer to the method as the logarithmic measure of diffusion, or simply as the logarithmic measure. We provide a general protocol for deriving analytical expressions for the probability densities on the logarithmic domain. The protocol is applicable for any number of spatial dimensions with any number of diffusive states. The analytical form can be fitted to data to reveal multiple diffusive modes. We validate the theoretical distributions and benchmark the accuracy and sensitivity of the method by extracting multimodal diffusion coefficients from two-dimensional Brownian simulations of polydisperse filament bundles. Bundling the filaments allows us to control the system nonuniformity and hence quantify the sensitivity of the method. By exploiting the anisotropy of the simulated filaments, we generalize the logarithmic measure to rotational diffusion. By fitting the analytical forms to simulation data, we confirm the method’s theoretical foundation. An error analysis in the single-mode regime shows that the proposed method is comparable in accuracy to the standard mean-squared displacement approach for evaluating diffusion coefficients. For the case of multimodal diffusion, we compare the logarithmic measure against other, more sophisticated methods, showing that both model selectivity and extraction accuracy are comparable for small data sets. Therefore, we suggest that the logarithmic measure, as a method for multimodal diffusion coefficient extraction, is ideally suited for small data sets, a condition often confronted in the experimental context. Finally, we critically discuss the proposed benefits of the method and its information content.     

Visualizing clinical evidence: citation networks for the incubation periods of respiratory viral infections

Simply by repetition, medical facts can become enshrined as truth even when there is little empirical evidence supporting them. We present an intuitive and clear visual design for tracking the citation history of a particular scientific fact over time. We apply this method to data from a previously published literature review on the incubation period of nine respiratory viral infections. The resulting citation networks reveal that the conventional wisdom about the incubation period for these diseases was based on a small fraction of available data and in one case, on no retrievable empirical evidence. Overall, 50% of all incubation period statements did not provide a source for their estimate and 65% of original sources for incubation period data were not incorporated into subsequent publications. More standardized and widely available methods for visualizing these histories of medical evidence are needed to ensure that conventional wisdom cannot stray too far from empirically supported knowledge.     

A solution to the problems of cytolysis assays with additional applications to other immunological and biochemical assays

After cellular immunoassays are compared with classical bioassays, conventional methods and consequent problems of data analysis for cytolysis assays are reviewed and a new solution is proposed. This solution incorporates new methods, called dose-response surface assays and analysis (DRSA), which estimate cytolytic activity coefficients on a surface in a three-dimensional space with two dose variables (killers and targets) and one response variable (counts). These new methods based on dose-response surfaces are demonstrated to be more informative and reliable than classical methods based on dose-response curves. In a test of the methods' robustness (sensitivity of parameter estimates to changes in the dose levels of the assay design), cytolytic activity coefficients estimated by DRSA varied by less than or equal to 30% over a reduction of three to four orders of magnitude in the dose levels. This remarkable robustness should be compared with the corresponding figures of as much as 500% over less than 1 order of magnitude for previously published results of coefficients estimated by conventional methods. DRSA is distinguished from replot-of-plots methods such as those used for enzyme inhibition assays in biochemistry, and is recommended as a more efficient method that should replace replot-of-plot methods now antiquated by the advent of microcomputers. DRSA can be applied to any experimental system that requires an activity coefficient to be estimated on a dose-response surface in a space of greater than or equal to 3 dimensions (greater than or equal to 2 dose variables and one response variable), regardless of the mathematical model and statistical estimators used to analyze the dose-response interaction. Finally, DRSA is compared with the methods known as response surface methodology (RSM), and is described as a new class of methods to be added to those that constitute RSM.     

Inferring Animal Densities from Tracking Data Using Markov Chains

The distributions and relative densities of species are keys to ecology. Large amounts of tracking data are being collected on a wide variety of animal species using several methods, especially electronic tags that record location. These tracking data are effectively used for many purposes, but generally provide biased measures of distribution, because the starts of the tracks are not randomly distributed among the locations used by the animals. We introduce a simple Markov-chain method that produces unbiased measures of relative density from tracking data. The density estimates can be over a geographical grid, and/or relative to environmental measures. The method assumes that the tracked animals are a random subset of the population in respect to how they move through the habitat cells, and that the movements of the animals among the habitat cells form a time-homogenous Markov chain. We illustrate the method using simulated data as well as real data on the movements of sperm whales. The simulations illustrate the bias introduced when the initial tracking locations are not randomly distributed, as well as the lack of bias when the Markov method is used. We believe that this method will be important in giving unbiased estimates of density from the growing corpus of animal tracking data.     

Fine scale biologging of an inshore marine animal

We compared the results of two biologging techniques used to study the foraging behaviour of a colony of small inshore predators, little penguins (Eudyptula minor). The first technique involved the use of satellite transmitters and diving loggers deployed on separate individuals, which has been the conventional method of tracking the movements and behaviour of this species for > 10 years. The second technique combined a diving logger and a global positioning system (GPS) logger deployed on the same individual, which is similar to the biologging methods presently being developed and used for many other species. We then considered the value of each technique as a conservation tool operating at the small scale (foraging area < 5000 ha and duration < 1 day).We found that the separately deployed satellite transmitters significantly underestimated the penguins' foraging area size. However, the size of the foraging area and other foraging parameters, such as total distance travelled, were influenced by the degree of GPS location sub-sampling. Furthermore, only the combined diving and GPS loggers could confidently describe the diving behaviour of the penguins in relation to the sea floor and identify that they were using small areas of conservation interest (shipping channel) inside their foraging area.Hence, the method employed to assess habitat use at fine scales can influence conservation measures that rely upon the data collected. We suggest that researchers fast-track their adoption of high resolution multi-loggers for increased data confidence when tracking animals at a fine scale, but also consider the potential effect of sampling rate on the calculation of parameters of interest.     

A novel validation algorithm allows for automated cell tracking and the extraction of biologically meaningful parameters

Automated microscopy is currently the only method to non-invasively and label-free observe complex multi-cellular processes, such as cell migration, cell cycle, and cell differentiation. Extracting biological information from a time-series of micrographs requires each cell to be recognized and followed through sequential microscopic snapshots. Although recent attempts to automatize this process resulted in ever improving cell detection rates, manual identification of identical cells is still the most reliable technique. However, its tedious and subjective nature prevented tracking from becoming a standardized tool for the investigation of cell cultures. Here, we present a novel method to accomplish automated cell tracking with a reliability comparable to manual tracking. Previously, automated cell tracking could not rival the reliability of manual tracking because, in contrast to the human way of solving this task, none of the algorithms had an independent quality control mechanism; they missed validation. Thus, instead of trying to improve the cell detection or tracking rates, we proceeded from the idea to automatically inspect the tracking results and accept only those of high trustworthiness, while rejecting all other results. This validation algorithm works independently of the quality of cell detection and tracking through a systematic search for tracking errors. It is based only on very general assumptions about the spatiotemporal contiguity of cell paths. While traditional tracking often aims to yield genealogic information about single cells, the natural outcome of a validated cell tracking algorithm turns out to be a set of complete, but often unconnected cell paths, i.e. records of cells from mitosis to mitosis. This is a consequence of the fact that the validation algorithm takes complete paths as the unit of rejection/acceptance. The resulting set of complete paths can be used to automatically extract important biological parameters with high reliability and statistical significance. These include the distribution of life/cycle times and cell areas, as well as of the symmetry of cell divisions and motion analyses. The new algorithm thus allows for the quantification and parameterization of cell culture with unprecedented accuracy. To evaluate our validation algorithm, two large reference data sets were manually created. These data sets comprise more than 320,000 unstained adult pancreatic stem cells from rat, including 2592 mitotic events. The reference data sets specify every cell position and shape, and assign each cell to the correct branch of its genealogic tree. We provide these reference data sets for free use by others as a benchmark for the future improvement of automated tracking methods.     

Spreadsheet Method for Evaluation of Biochemical Reaction Rate Coefficients and Their Uncertainties by Weighted Nonlinear Least-Squares Analysis of the Integrated Monod Equation

A convenient method for evaluation of biochemical reaction rate coefficients and their uncertainties is described. The motivation for developing this method was the complexity of existing statistical methods for analysis of biochemical rate equations, as well as the shortcomings of linear approaches, such as Lineweaver-Burk plots. The nonlinear least-squares method provides accurate estimates of the rate coefficients and their uncertainties from experimental data. Linearized methods that involve inversion of data are unreliable since several important assumptions of linear regression are violated. Furthermore, when linearized methods are used, there is no basis for calculation of the uncertainties in the rate coefficients. Uncertainty estimates are crucial to studies involving comparisons of rates for different organisms or environmental conditions. The spreadsheet method uses weighted least-squares analysis to determine the best-fit values of the rate coefficients for the integrated Monod equation. Although the integrated Monod equation is an implicit expression of substrate concentration, weighted least-squares analysis can be employed to calculate approximate differences in substrate concentration between model predictions and data. An iterative search routine in a spreadsheet program is utilized to search for the best-fit values of the coefficients by minimizing the sum of squared weighted errors. The uncertainties in the best-fit values of the rate coefficients are calculated by an approximate method that can also be implemented in a spreadsheet. The uncertainty method can be used to calculate single-parameter (coefficient) confidence intervals, degrees of correlation between parameters, and joint confidence regions for two or more parameters. Example sets of calculations are presented for acetate utilization by a methanogenic mixed culture and trichloroethylene cometabolism by a methane-oxidizing mixed culture. An additional advantage of application of this method to the integrated Monod equation compared with application of linearized methods is the economy of obtaining rate coefficients from a single batch experiment or a few batch experiments rather than having to obtain large numbers of initial rate measurements. However, when initial rate measurements are used, this method can still be used with greater reliability than linearized approaches.     

A neuromusculoskeletal tracking method for estimating individual muscle forces in human movement

A neuromusculoskeletal tracking (NMT) method was developed to estimate muscle forces from observed motion data. The NMT method combines skeletal motion tracking and optimal neuromuscular tracking to produce forward simulations of human movement quickly and accurately. The skeletal motion tracker calculates the joint torques needed to actuate a skeletal model and track observed segment angles and ground forces in a forward simulation of the motor task. The optimal neuromuscular tracker resolves the muscle redundancy problem dynamically and finds the muscle excitations (and muscle forces) needed to produce the joint torques calculated by the skeletal motion tracker. To evaluate the accuracy of the NMT method, kinematics and ground forces obtained from an optimal control (parameter optimization) solution for maximum-height jumping were contaminated with both random and systematic noise. These data served as input observations to the NMT method as well as an inverse dynamics analysis. The NMT solution was compared to the input observations, the original optimal solution, and a simulation driven by the inverse dynamics torques. The results show that, in contrast to inverse dynamics, the NMT method is able to produce an accurate forward simulation consistent with the optimal control solution. The NMT method also requires 3 orders-of-magnitude less CPU time than parameter optimization. The speed and accuracy of the NMT method make it a promising new tool for estimating muscle forces using experimentally obtained kinematics and ground force data.     

Intermolecular Overhauser effects in fluoroalcohol solutions of cyclo-alanylglycine

Interactions between the diketopiperazine cyclo-alanylglycine and four fluorinated alcohols in water-fluoroalcohol mixtures were examined by (1)H[(19)F] intermolecular nuclear Overhauser effects (NOE) experiments. The alcohols studied were trifluoroethanol, hexafluoroacetone trihydrate, 1,1,1,3,3,3-hexafluoroisopropanol and perfluoro-t-butanol. The experimental methods used permit detection of solvent-solute NOEs of 0.1% or less. Solute and solvent diffusion coefficients were determined and apparent molecular radii of the fluoroalcohols estimated. Using these data, it was shown that observed (1)H[(19)F] intermolecular NOEs are consistent with expectations based on theory. A method for extending conventional theory to take into account the shape of a solute and the exposure of its hydrogens to solvent is described. This approach gives reasonable agreement with experimental results, particularly if it is assumed that solute-solvent interactions take place in such a way that the fluorines of a fluoroalcohol are preferentially oriented toward the solute during solute-solvent encounters. The results support the suggestion that intermolecular (1)H[(19)F] NOEs may become a useful tool for studies of peptide and protein conformations in fluoroalcohol-water solvent mixtures.     

Tracking stem cell migration and survival in brain injury: Current approaches and future prospects

In recent years, stem cell-mediated therapies have gained considerable ground as potential treatments for a wide variety of brain pathologies including traumatic brain injury, stroke and neurodegenerative diseases. Despite extensive preclinical studies, many of these therapies have not been fully translated into viable clinical approaches. This is partly due to our inability to reliably track and monitor transplanted stem cells longitudinally over long periods of time in vivo. In this review, we discuss the predominant histological cell tracing methodologies, such as immunohistochemistry, and fluorescent cellular dyes and proteins, and compare them to emerging cellular imaging technologies. We show that advances in magnetic resonance imaging (MRI) have resulted in opportunities to use this technology to further our understanding of stem cell characteristics and behaviors in vivo. While MRI may not completely replace conventional cell tracking methods in pre-clinical, mechanistic work, it is clear that it has the potential to function as a powerful diagnostic tool for tracking stem cell migration and survival as well as for evaluating the efficacy of stem cell-mediated therapies.     

Application of multilevel models to morphometric data. Part 2. Correlations.

Multilevel organization of morphometric data (cells are "nested" within patients) requires special methods for studying correlations between karyometric features. The most distinct feature of these methods is that separate correlation (covariance) matrices are produced for every level in the hierarchy. In karyometric research, the cell-level (i.e., within-tumor) correlations seem to be of major interest. Beside their biological importance, these correlation coefficients (CC) are compulsory when dimensionality reduction is required. Using MLwiN, a dedicated program for multilevel modeling, we show how to use multivariate multilevel models (MMM) to obtain and interpret CC in each of the levels. A comparison with two usual, "single-level" statistics shows that MMM represent the only way to obtain correct cell-level correlation coefficients. The summary statistics method (take average values across each patient) produces patient-level CC only, and the "pooling" method (merge all cells together and ignore patients as units of analysis) yields incorrect CC at all. We conclude that multilevel modeling is an indispensable tool for studying correlations between morphometric variables.     

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of life and survival of breast cancer patients. In this paper a new approach for the detection of breast cancer is described, based on tracking the mammary architectural elements using diffusion tensor imaging (DTI). The paper focuses on the scanning protocols and image processing algorithms and software that were designed to fit the diffusion properties of the mammary fibroglandular tissue and its changes during malignant transformation. The final output yields pixel by pixel vector maps that track the architecture of the entire mammary ductal glandular trees and parametric maps of the diffusion tensor coefficients and anisotropy indices. The efficiency of the method to detect breast cancer was tested by scanning women volunteers including 68 patients with breast cancer confirmed by histopathology findings. Regions with cancer cells exhibited a marked reduction in the diffusion coefficients and in the maximal anisotropy index as compared to the normal breast tissue, providing an intrinsic contrast for delineating the boundaries of malignant growth. Overall, the sensitivity of the DTI parameters to detect breast cancer was found to be high, particularly in dense breasts, and comparable to the current standard breast MRI method that requires injection of a contrast agent. Thus, this method offers a completely non-invasive, safe and sensitive tool for breast cancer detection.     

Simulation of dynamic contrast-enhanced ultrasound sequences using example-based texture generation

Simulation of dynamic contrast-enhanced ultrasound sequences with known perfusion characteristics and speckle characteristics that are consistent with those observed in experimental data would provide a useful tool for the evaluation of new perfusion quantification and image-processing techniques. A framework is proposed to simulate such perfusion data. It is based on the use of an example-based texture generation method. The generated texture of noise is compared to experimental data in terms of its statistical distribution and spatial correlation. Results show that the example-based method generates data that are closer to the experimental data than those obtained using a conventional parametric simulation method (33 to 80% smaller Hellinger squared distance). This fast and simple method allows simulation of dynamic contrast-enhanced ultrasound data for complex perfusion patterns, and should be useful for the validation of registration, segmentation or perfusion quantification methods.     

Chemometric methods for simultaneous quantification of lactic,malic and fumaric acids

Lactic, fumaric and malic acids are commonly used in food and pharmaceutical industries. During microbial production of these compounds, it is important to determine their concentrations in the fermentation broth with a rapid and sensitive method. Spectrophotometry is commonly used. However, UV‐spectral overlap between these organic acids makes it difficult to determine each of them individually from the mixture. In order to overcome this problem, statistical methods, namely principal component regression (PCR) and partial least squares‐1 methods, were tested and compared with conventional HPLC techniques. The absorbance data matrix was obtained by measuring the absorbances of 21 ternary mixtures of lactic, fumaric and malic acids in a wavelength range of 210–260 nm. Calibration and validation were performed by using the data obtained in a mixture of these organic acids. The prediction abilities of the methods were tested by applying them to fermentation broths. The precision of the PCR method was better than that of the partial least squares‐1 method. In the PCR method, the correlation coefficients between actual and predicted concentrations of the organic acids were calculated as 0.970 for lactic acid and 0.996 for fumaric acid in fermentation broths. The concentration of malic acid was not detected due to its low concentration in samples. These results show that the PCR method can be applied for simultaneous determination of lactic, fumaric and malic acids in fermentation broths.     

High-throughput sequencing of core STR loci for forensic genetic investigations using the Roche Genome Sequencer FLX platform

The analysis and profiling of short tandem repeat (STR) loci is routinely used in forensic genetics. Current methods to investigate STR loci, including PCR-based standard fragment analyses and capillary electrophoresis, only provide amplicon lengths that are used to estimate the number of STR repeat units. These methods do not allow for the full resolution of STR base composition that sequencing approaches could provide. Here we present an STR profiling method based on the use of the Roche Genome Sequencer (GS) FLX to simultaneously sequence multiple core STR loci. Using this method in combination with a bioinformatic tool designed specifically to analyze sequence lengths and frequencies, we found that GS FLX STR sequence data are comparable to conventional capillary electrophoresis-based STR typing. Furthermore, we found DNA base substitutions and repeat sequence variations that would not have been identified using conventional STR typing.     

Styrene and ethylbenzene absorption in ionic liquids: comparing DFT affinity calculations with experimental data

Styrene is a widely used bulk chemical produced by dehydrogenation of ethylbenzene (EB). Purification of styrene to contain < 100 ppm EB is not cost-effective by conventional separation methods. One separation method is extractive distillation with an ionic liquid (IL) as a binding agent for one of the components, thereby lowering the vapour pressure of this component. In this study, using quantum density functional theory (DFT), we have simulated 22 IL anion–cation pairs, styrene and EB affinities to them, and ion-pair dimer affinities of the ILs. These are compared with experimental liquid–liquid equilibrium studies of M.T.G. Jongmans, B. Schuur, and A.B. de Haan, Ind. Eng. Chem. Res. 50 (2011), pp. 10800–10810. It is shown that experimental selectivity and distribution coefficients of styrene and EB in the ILs are related to computed gas phase anion–cation stabilisation energies and ion-pair–ion-pair dimer affinities. The inverse of molar volume is found to strongly correlate with the selectivity. The computational results also qualitatively correlate with molar volume, and consequently, it is possible to use DFT calculations as a qualitative prediction tool in screening of ILs for this separation process. This tool does not account for effects caused by long alkyl chains, as the length does not seem to affect dimer stabilisation energy beyond ethyl group.