Tool For Quantification Research Articles

ExoPLOT: Representation of alternative splicing in human tissues and developmental stages with transposed element (TE) involvement

Advances in RNA high-throughput sequencing and large-scale functional assays yield new insights into the multifaceted activities of transposed elements (TE) and many other previously undiscovered sequence elements. Currently, no tool for easy access, analysis, quantification, and visualization of alternatively spliced exons across multiple tissues or developmental stages is available. Also, analysis pipelines demand computational skills or hardware requirements, which often are hard to meet by wet-lab scientists. We developed ExoPLOT to enable simplified access to massive RNA high throughput sequencing datasets to facilitate the analysis of alternative splicing across many biological samples. To demonstrate the functonality of ExoPLOT, we analyzed the contributon of exonized TEs to human coding sequences (CDS). mRNA splice variants containing the TE-derived exon were quantified and compared to expression levels of TE-free splice variants. For analysis, we utilized 313 human cerebrum, cerebellum, heart, kidney, liver, ovary, and testis transcriptomes, representing various pre- and postnatal developmental stages. ExoPLOT visualizes the relative expression levels of alternative transcripts, e.g., caused by the insertion of new TE-derived exons, across different developmental stages of and among multiple tissues. This tool also provides a unique link between evolution and function during exonization (gain of a new exon) and exaptation (recruitment/co-optation) of a new exon. As input for analysis, we derived a database of 1151 repeat-masked, exonized TEs, representing all prominent families of transposons in the human genome and the collection of human consensus coding sequences (CCDS). ExoPLOT screened preprocessed RNA high-throughput sequencing datasets from seven human tissues to quantify and visualize the dynamics in RNA splicing for these 1151 TE-derived exons during the entire human organ development. In addition, we successfully mapped and analyzed 993 recently described exonized sequences from the human frontal cortex onto these 313 transcriptome libraries. ExoPLOT's approach to preprocessing RNA deep sequencing datasets facilitates alternative splicing analysis and significantly reduces processing times. In addition, ExoPLOT's design allows studying alternative RNA isoforms other than TE-derived in a customized – coordinate-based manner and is available at http://retrogenomics3.uni-muenster.de:3838/exz-plot-d/.

Cost-Oriented Prediction Intervals: On Bridging the Gap Between Forecasting and Decision

As an efficient tool for uncertainty quantification of renewable energy forecasting, prediction intervals (PIs) provide essential prognosis to power system operator. Merely improving the statistical quality of PIs with respect to calibration and sharpness cannot always contribute to the operational value for specific decision-making issue. In this paper, the cost-oriented prediction intervals are firstly proposed to achieve the joint improvement of forecasting quality and decision performance. In order to bridge the gap between forecasting and decision, a novel cost-oriented machine learning (COML) framework is established, which unifies nonparametric renewable power PI construction and decision-making. Formulated as a bilevel programming model, the COML minimizes the operational costs of decision-making process by adaptively adjusting the quantile proportion pair of PIs resulting from extreme learning machine based quantile regression. The hierarchical optimization model of the COML is equivalently simplified as a single level nonlinear programming problem. Then an enhanced branch-and-contract algorithm with innovative bounds contraction strategy is devised to efficiently capture the optimum of the single level problem with bilinear nonconvexity. Numerical experiments based on actual wind farm data simulate the online forecasting and decision process for wind power offering. Comprehensive comparisons verify the substantial superiority of the proposed COML methodology in terms of forecasting quality, operational value, as well as computational efficiency for practical application.

External load in male professional volleyball: A systematic review

Introduction. The objectives of this systematic review were to identify the volleyball external loads values in the literature and to verify the applicability of different means of quantification and monitoring of these variables during training sessions and matches. Material and methods: This systematic review was produced following the PRISMA statement recommendations, and the search for publications was carried out in the databases PubMed/NCBI, SportDiscus via EBSCOhost, SciELO. 12 studies meet the criteria and were included in this review. Results: The most used tool for quantification, monitoring and evaluation of external loads are video recording and manual or semi-automatic counting of jumps and distance covered and, more recently, the use of inertial measurements unit. The middle blocker has the highest high jump load, outside hitters jump closer to the maximum more often and setters have a high demand of medium height jumps. Conclusions: Match and training jump loads seem to be similar, and sessions that involve block or attack have a higher jump load. In professional male volleyball, training is planned with variation in training loads according to the period of the sea-son and according to the days of the week before and after games.

Uncertainty quantification of radionuclide migration in fractured granite

Deep geological disposal is a widely accepted approach for safe management and long-term disposal of high-level radioactive waste (HLW). However, high uncertainty associated with subsurface properties of fractured rocks is a significant obstacle to practical safety assessment of HLW disposal. In this study, we develop an integrated statistical framework for uncertainty quantification of radionuclide migration related to the geological disposal of HLW. We employ a response surface methodology integrated with Monte Carlo simulations of radionuclide migration in fractured granite to perform global sensitivity and statistical analysis by coupling the uncertainty quantification tool, PSUADE, and radionuclide migration simulator, FRACPIPE. FRACPIPE is a semi-analytical simulator that models solute transport in fractures and matrix slabs with an arbitrary-length decay chain and an arbitrary time-varying influent concentration history by considering a variety of transport mechanisms. The statistical risk metrics include nuclide breakthrough (BT) time, total release dose, single nuclide release dose, and single nuclide flux. The global sensitivity analysis identifies fracture aperture, matrix diffusion coefficient, hydraulic gradient, and dispersivity as the most sensitive parameters. Considering the uncertainty ranges for independent variables (e.g., dispersivity, hydraulic gradient, fracture spacing, fracture aperture, sorption distribution coefficient, matrix diffusion, and matrix porosity), we apply post-processing results of Monte Carlo simulations to conduct statistical analysis of the risk metrics. Under this studied condition, the average BT time for radionuclides at 1000 m is between 85 and 331 thousand years. This study provides valuable insights into the impact of input parameter (independent variables) uncertainty and sensitivity on radionuclide migration behavior in an HLW repository.

Comparison of Direct Versus Image Based Foot Posture Index in School Going Children

Introduction: The Foot Posture Index-6 (FPI-6) is a simple quantification tool to assess static foot alignment in children. Palpation of the foot is required for assessment of one of the six criteria that comprise the FPI-6; the remaining five criteria may be evaluated using still-frame photographs. Using only the image-based criteria may allow evaluation of foot posture without going in person for physical checkup. The purpose of the current study was to compare this image based criteria of FPI with direct observation FPI in school going children. Methods/Procedure: 38 participants were assessed from different schools of Ahmedabad for the study. Children were evaluated for FPI and scoring of five criteria were done. Three photos with a camera of the medial longitudinal arch, posterior ankle and the talonavicular joint approximately 45° from the posterior calcaneus for both right and left feet were taken. Then assessment of the five image-based criteria of the FPI-6 for both feet were done and scoring was done. Results: As this is a comparison of two methods, Bland Altman analysis has been performed and bias is almost negligible (mean bias for Lt. FPI – 0.16 and for Rt. FPI 0.03) so both methods are equivalent. Conclusion: Image based FPI evaluation can be done with caution to assess foot posture evaluation in school going children which can be very useful in current pandemic situation. Keywords: Image based Foot Posture Index, Foot Posture Index, School Going Children

Grey matter atrophy in patients with benign multiple sclerosis.

BackgroundBrain atrophy appears during the progression of multiple sclerosis (MS) and is associated with the disability caused by the disease.MethodsWe investigated global and regional grey matter (GM) and white matter (WM) volumes, WM lesion load, and corpus callosum index (CCI), in benign relapsing‐remitting MS (BRRMS, n = 35) with and without any treatment and compared those to aggressive relapsing‐remitting MS (ARRMS, n = 46). Structures were analyzed by using an automated MRI quantification tool (cNeuro®).ResultsThe total brain and cerebral WM volumes were larger in BRRMS than in ARRMS (p = .014, p = .017 respectively). In BRRMS, total brain volumes, regional GM volumes, and CCI were found similar whether or not disease‐modifying treatment (DMT) was used. The total (p = .033), as well as subcortical (p = .046) and deep WM (p = .041) lesion load volumes were larger in BRRMS patients without DMT. Cortical GM volumes did not differ between BRRMS and ARRMS, but the volumes of total brain tissue (p = .014) and thalami (p = .003) were larger in patients with BRRMS compared to ARRMS. A positive correlation was found between CCI and whole‐brain volume in both BRRMS (r = .73, p < .001) and ARRMS (r = .80, p < .01).ConclusionsThalamic volume is the most prominent measure to differentiate BRRMS and ARRMS. Validation of automated quantification of CCI provides an additional applicable MRI biomarker to detect brain atrophy in MS.

Construction of a Porcine Reproductive and Respiratory Syndrome Virus with Nanoluc Luciferase Reporter: a Stable and Highly Efficient Tool for Viral Quantification Both In Vitro and In Vivo.

ABSTRACTPorcine reproductive and respiratory syndrome virus (PRRSV) is one of the most economically important pathogens for the global pork industry, characterized for its genetic variation and unsatisfied heterological protection from vaccines. A high-throughput screening platform for developing anti-PRRSV therapies is urgently needed. Here, an 11-amino-acid subunit HiBiT derived from NanoLuc luciferase was inserted into the PRRSV genome at four loci of the Nsp2 coding region or as an additional TRS2 driven open reading frame (ORF) between the ORF7 and 3′-untranscribed region (3′-UTR), respectively, and five recombinant viruses with luciferase activity were successfully rescued. The virological characteristics of the representative virus RvJX-Nsp2325-HiBiT were investigated. In vitro, it displayed similar growth kinetics as the parental virus and keeps the luciferase activity and genetic stability after eight rounds of serial passages. The concept-proof test confirmed that RvJX-Nsp2325-HiBiT can be easily used to evaluate the efficacy of antiviral reagents by detecting the reduction of luciferase activity, showing a consistent trend with infectious titers, as well as to set a novel convenient virus neutralization assay based on the intensity of luciferase activity. Last, the viral proliferation, virulence, validity, and HiBiT stability were further investigated in pig inoculation study, showing that the luciferase activity can be directly detected in the tissue samples or indirectly from the MARC-145 cells inoculated with sera from RvJX-Nsp2325-HiBiT-inoculated pigs. Taken together, the results indicate that the HiBiT-tagged virus is a convenient and stable tool for evaluating viral propagation both in vitro and in vivo, which can provide a high-efficient platform for screening and evaluating anti-PRRSV therapies.IMPORTANCE Luciferase reporter tagged virus is crucial to viral quantification in the study of viral replication, pathogenesis and exploring antiviral reagents. It is urgently needed for PRRSV academia to construct a stable, fast, and high-throughput reporting system, which can be used both in vitro and in vivo. Here, an 11-amino-acid luciferase subunit was successfully inserted into the PRRSV genome; the feasibility, genetic stability, and efficiency for viral quantification both in vitro and in vivo were characterized; and the results demonstrated it has greatly improved the convenience and efficiency for screening the anti-PRRSV reagents. Furthermore, a novel luciferase-based virus neutralization assay was successfully set, which can eliminate the step of sample gradient dilution and greatly improve the convenience and throughput of neutralizing antibody testing. Predictably, it will greatly facilitate the screening and evaluating anti-PRRSV therapies, as well as the mechanistic study of its replication and pathogenesis in the future.

Simulating electron-excited energy dispersive X-ray spectra with the NIST DTSA-II open-source software platform.

NIST DTSA-II is a free, open access, and fully-documented comprehensive software platform for electron-excited X-ray microanalysis with energy dispersive spectrometry (EDS), including tools for quantification, measurement optimization, and spectrum simulation. EDS simulation utilizes a Monte Carlo electron trajectory simulation that includes characteristic and continuum X-ray generation, self-absorption, EDS window absorption, and energy-to-charge conversion leading to peak broadening. Spectra are simulated on an absolute basis considering electron dose and spectrometer parameters. Simulated and measured spectra agree within ± 25% relative for K-shell and L-shell characteristic X-ray peaks from 1 to 11 keV, while the predicted M-shell intensity was found to exceed the measured value by a factor of 1.4-2.2 from 1 to 3 keV. The X-ray continuum (bremsstrahlung) intensity agreed within ± 10% over the photon energy range from 1 to 10 keV for elements from boron to bismuth. Simulated spectra can be used to develop analytical strategy, such as assessing detection of trace constituents.

Dual gradient echo in-phase and out of phase sequences in assessment of hepatic iron overload in patients with beta-thalassemia, would be better?

PurposeTo evaluate the diagnostic accuracy of the dual gradient-echo (GRE) in- and out-of-phase sequences as a quantitative tool for hepatic iron overload in comparison with MRI R2* relaxometry in paediatric patients with beta-thalassemia. MethodSixty-three patients with beta-thalassemia major (transfusion-dependent) or beta-thalassemia intermedia (transfusion- and non-transfusion-dependent) were referred from the paediatric department (haematology unit) to the radiology department at a university hospital. The paediatrician conducted a clinical examination for the studied group, assessed their laboratory data, conducted R2* relaxometry and dual gradient echo sequences to calculate R2* and relative signal intensity index at the axial mid-section of the liver, and studied their correlation. A 1.5 Tesla MR scanner was used (Achieva; Philips Medical Systems, the Netherlands). Data were fed to the computer and analysed using the IBM SPSS software package version 20.0 (Armonk, NY: IBM Corp). The Kolmogorov-Smirnov test was used to verify the normality of distribution. The significance of the results was determined at the 5% level. The Chi-square, Fisher’s exact correction, Pearson coefficient, and Bland–Altman tests were used. ResultsDual gradient-echo in- and out-of-phase sequences using visual assessment accurately assessed 93.65% of our patient group with hepatic iron overload. A significant correlation was found between the relative signal intensity index and hepatic MRI R2* relaxometry (p < 0.001, r = 0.861). ConclusionsDual gradient-echo in and out-of-phase sequences are good imaging tools for hepatic iron detection and quantification. These sequences showed good correlation with R2* relaxometry (r = 0.861, p < 0.001).

MP-Net: Deep learning-based segmentation for fluorescence microscopy images of microplastics isolated from clams.

Environmental monitoring of microplastics (MP) contamination has become an area of great research interest, given potential hazards associated with human ingestion of MP. In this context, determination of MP concentration is essential. However, cheap, rapid, and accurate quantification of MP remains a challenge to this date. This study proposes a deep learning-based image segmentation method that properly distinguishes fluorescent MP from other elements in a given microscopy image. A total of nine different deep learning models, six of which are based on U-Net, were investigated. These models were trained using at least 20,000 patches sampled from 99 fluorescence microscopy images of MP and their corresponding binary masks. MP-Net, which is derived from U-Net, was found to be the best performing model, exhibiting the highest mean F1-score (0.736) and mean IoU value (0.617). Test-time augmentation (using brightness, contrast, and HSV) was applied to MP-Net for robust learning. However, compared to the results obtained without augmentation, no clear improvement in predictive performance could be observed. Recovery assessment for both spiked and real images showed that, compared to already existing tools for MP quantification, the MP quantities predicted by MP-Net are those closest to the ground truth. This observation suggests that MP-Net allows creating masks that more accurately reflect the quantitative presence of fluorescent MP in microscopy images. Finally, MAP (Microplastics Annotation Package) is introduced, an integrated software environment for automated MP quantification, offering support for MP-Net, already existing MP analysis tools like MP-VAT, manual annotation, and model fine-tuning.

Comparison of fluorescence lifetime and multispectral imaging for quantitative multiplexing in biological tissue.

Fluorescence lifetime (FLT) multiplexing and multispectral imaging (MSI) are both frequently employed for in vitro and ex vivo biological studies. In vivo applications of MSI for deep seated fluorophores require consideration of diffusive light propagation in biological tissue. We have previously shown that a well-known redshift of fluorescence spectra in diffusive medium induces a fluorophore cross-talk, which cannot be accounted for even with known optical properties of the medium. In contrast, FLT measurements remain largely unaffected by light propagation in tissue, enabling zero cross-talk and accurate relative quantification. While a fully quantitative estimation of fluorophore concentrations requires depth resolved tomographic imaging, this is often not possible due to the difficulty of estimating tissue optical properties and modelling light propagation in complex tissue geometries. Here, we experimentally investigate the performance of planar (non-tomographic) MSI and FLT multiplexing for the quantitative recovery of multiple near-infrared fluorophores embedded in 4-8 mm thick tissue. We show that FLT multiplexing provides a superior quantification accuracy (error < 10%) compared to MSI (error = 20-107%) in tissue. The error rates for MSI increased with tissue thickness and can be directly attributed to the spectral redshift induced cross-talk between emission spectra. Our data indicate that planar FLT multiplexing can provide high quantification accuracy in thick biological tissue without a need for optical property estimation, thereby offering an important validation tool for rapid quantification of fluorophore concentrations in bulk tissue.

Timelapse viability assay to detect division and death of primary multiple myeloma cells in response to drug treatments with single cell resolution.

Heterogeneity among cancer cells and in the tumor microenvironment (TME) is thought to be a significant contributor to the heterogeneity of clinical therapy response observed between patients and can evolve over time. A primary example of this is multiple myeloma (MM), a generally incurable cancer where such heterogeneity contributes to the persistent evolution of drug resistance. However, there is a paucity of functional assays for studying this heterogeneity in patient samples or for assessing the influence of the patient TME on therapy response. Indeed, the population-averaged data provided by traditional drug response assays and the large number of cells required for screening remain significant hurdles to advancement. To address these hurdles, we developed a suite of accessible technologies for quantifying functional drug response to a panel of therapies in ex vivo three-dimensional culture using small quantities of a patient's own cancer and TME components. This suite includes tools for label-free single-cell identification and quantification of both cell division and death events with a standard brightfield microscope, an open-source software package for objective image analysis and feasible data management of multi-day timelapse experiments, and a new approach to fluorescent detection of cell death that is compatible with long-term imaging of primary cells. These new tools and capabilities are used to enable sensitive, objective, functional characterization of primary MM cell therapy response in the presence of TME components, laying the foundation for future studies and efforts to enable predictive assessment drug efficacy for individual patients.

Uncertainty Quantification in Support of Severe Accident Analysis Code User Confidence Using MELCOR-DAKOTA

Abstract Uncertainty and Sensitivity analysis methods are often used in severe accident analysis for validating the complex physical models employed in the system codes that simulate such scenarios. This is necessitated by the large uncertainties associated with the physical models and boundary conditions employed to simulate severe accident scenarios. Uncertainty analysis involves primarily the estimation of the distribution of a key figure-of-merit (FOM) following the propagation of uncertainty in selected input parameters through a system code. The input parameters are sampled within defined ranges based on assigned probability distribution functions (PDFs) for the required number of code runs/realizations using stochastic sampling techniques. Input parameter selection is based on their importance to the key FOM, which is determined by the parameter identification and ranking table (PIRT). Sensitivity analysis investigates the contribution of each uncertain input parameter to the uncertainty of the selected FOM. In this study, the integrated severe accident analysis code MELCOR was coupled with a design analysis kit for optimization and terascale applications (DAKOTA), an optimization and uncertainty quantification tool in order to investigate the effect of input parameter uncertainty on hydrogen generation. The methodology developed was applied to the Fukushima Daiichi unit 1 nuclear power plant (NPP) accident scenario, which was modeled in another study. The results show that there is approximately 22.46% uncertainty in the amount of hydrogen generated as estimated by a single MELCOR run given the uncertainty in selected input parameters. The sensitivity analysis results also reveal that MELCOR input parameters: COR_SC 1141(melt flow rate per unit width at breakthrough candling), COR_ZP (porosity of fuel debris beds), and COR_EDR (characteristic debris size in the core region) contributed most significantly to the uncertainty in hydrogen generation.

Quantification of nicotinic acid, kynurenine, and kynurenine acid in poultry meat by validated liquid chromatography-single quadrupole mass spectrometry method

Kynurenine pathway utilizes tryptophan to generate the biologically important metabolites (including kynurenine and kynurenic acid) associated with different pathologies. NAD+ is the final product of this pathway, but it can be also produced from nicotinic acid (niacin) in the salvage pathway. There is small number of literature available and great desire for analytical tools for quantification of these metabolites in foods. We developed and validated the chromatography/mass spectrometry-based method for the simultaneous determination of nicotinic acid, kynurenine, and kynurenic acid in raw poultry (chicken and turkey) meat. Using this method, the raw meat from different body parts (breast, drumstick, thigh, mid joint wing, back, neck) of chicken and turkey were analyzed. Nicotinic acid from 0.06 to 46.01 mg/kg was found in tested chicken and turkey meat samples. Most samples (∼84%) contained quantifiable amounts of kynurenine (0.013–0.274 mg/kg), but kynurenic acid (0.004–0.138 mg/kg) was determined in only ∼3% of poultry meat.

Unbiased analysis of mouse brain endothelial networks from two- or three-dimensional fluorescence images.

.Significance: A growing body of research supports the significant role of cerebrovascular abnormalities in neurological disorders. As these insights develop, standardized tools for unbiased and high-throughput quantification of cerebrovascular structure are needed.Aim: We provide a detailed protocol for performing immunofluorescent labeling of mouse brain vessels, using thin () or thick (50 to ) tissue sections, followed respectively by two- or three-dimensional (2D or 3D) unbiased quantification of vessel density, branching, and tortuosity using digital image processing algorithms.Approach: Mouse brain sections were immunofluorescently labeled using a highly selective antibody raised against mouse Cluster of Differentiation-31 (CD31), and 2D or 3D microscopy images of the mouse brain vasculature were obtained using optical sectioning. An open-source toolbox, called Pyvane, was developed for analyzing the imaged vascular networks. The toolbox can be used to identify the vasculature, generate the medial axes of blood vessels, represent the vascular network as a graph, and calculate relevant measurements regarding vascular morphology.Results: Using Pyvane, vascular parameters such as endothelial network density, number of branching points, and tortuosity are quantified from 2D and 3D immunofluorescence micrographs.Conclusions: The steps described in this protocol are simple to follow and allow for reproducible and unbiased analysis of mouse brain vascular structure. Such a procedure can be applied to the broader field of vascular biology.

Proposing a chemometric Normalized Difference Phyllodulcin Index (cNDPI) for phyllodulcin synthesis estimation

The assessment and evaluation of plant traits is a key factor in development of plant based products and exploitation of renewable resources. In this context, phyllodulcin (PD) and its precursor hydrangenol (HG), both dihydroisocoumarins (DHC) only present in Hydrangea macrophylla subsp. serrata (also known as H. serrata (Thunb.) Ser.), are of interest for food industry and medicine. Besides PD content and plant growth, the efficient conversion of PD from HG is an important factor in plant selection. For this a chemometric Normalized Difference Phyllodulcin Index (cNDPI) is proposed as a reliable descriptor for PD biosynthesis efficiency from HG. The cNDPI was tested for robustness and resilience over the course of 1291 measurements in 12 experiments using the H. macrophylla subsp. serrata cultivars ‘Amagi Amacha’, ‘Oamacha’ and ‘Odoriko Amacha’. The cNDPI significantly differentiates these three cultivars with an accuracy of 94.4%. Observations on an F1 population from a cross ‘Odoriko Amacha’ x ‘Yae Amacha’ reveal that cNDPI values are stable over different environments and are not affected by absolute HG or PD contents which are mainly genetically determined. In conclusion, the cNDPI provides a simple tool for quantification of the HG into PD conversion, regardless of absolute DHC amounts or environmental effects. The cNDPI could be used to evaluate efficiency of PD conversion in tea-hortensias.

Application of Parallel Reaction Monitoring in 15N Labeled Samples for Quantification.

Accurate relative quantification is critical in proteomic studies. The incorporation of stable isotope 15N to plant-expressed proteins in vivo is a powerful tool for accurate quantification with a major advantage of reducing preparative and analytical variabilities. However, 15N labeling quantification has several challenges. Less identifications are often observed in the heavy-labeled samples because of incomplete labeling, resulting in missing values in reciprocal labeling experiments. Inaccurate quantification can happen when there is contamination from co-eluting peptides or chemical noise in the MS1 survey scan. These drawbacks in quantification can be more pronounced in less abundant but biologically interesting proteins, which often have very few identified peptides. Here, we demonstrate the application of parallel reaction monitoring (PRM) to 15N labeled samples on a high resolution, high mass accuracy Orbitrap mass spectrometer to achieve reliable quantification even of low abundance proteins in samples.

MO571: Novel Insights Into Uremic Toxins in CKD: Rapid Detection of Microbiota-Derived Indoxyl Sulfate in CKD Patients

Abstract BACKGROUND AND AIMS Emerging evidence suggest that uremic toxins are involved in the progression of chronic kidney disease (CKD). Indoxyl Sulfate (IxS) is one of the most studied since resulted as strong predictor of cardiovascular disease (CVD) and linked with the prognosis of patients with CKD. Indeed, blood levels of IxS increase during CKD stages reaching a maximum in patients with end-stage renal disease (ESRD). Additionally, hemodialysis is able to eliminate only the blood-free fraction as the IxS albumin bond hides its removal. As a result, patients on dialysis show the highest IxS levels resulting in worsening condition, enhancement of CVD-related complication. Thus, IxS rapid quantification could offers clinicians an alternative method for monitoring CKD progression and address the dialysis effectiveness. Liquid chromatography/tandem mass spectrometry (LC-MS/MS) is commonly used for IxS detection and require dedicated equipment and skills, long analysis times and costly analytical instruments. To address these issues, the development of a simple and rapid analytical method for IxS detection is needed. Here, we present a study focused on the development and validation of a rapid and simple method of IxS determination based on colorimetric array-based analysis exploitable in clinical practice for CKD patients. METHOD A total of 20 blood samples (10 healthy people and 10 CKD patients) were used to assess the feasibility of our colorimetric assay. IxS from serum sample was quantified at time of inclusion by LC-MS/MS. Next, from the same samples, we used a derivatization reaction for splitting the serum IxS into indigo-blue. This method enables the rapid quantification of IxS-derived indigo by a smart spectrophotometer at λ = 420–450 nm. Analyte concentration was instantly calculated from absorbance/concentration calibration curve of indigo-blue. Finally, a correlation between the two methods were carried out for the colorimetric analysis validation. RESULTS To accurately determine concentrations of IxS in the low µM range for practical diagnostic purposes, a wide linear calibration curve of was successfully established. The calibration curve ranged from ‘0 µM to 330 µM’ for indigo blue. The regression equation between absorbance and concentration was y = 0.0003 x + 0.0192, and the correlation coefficient (r2) was 0.9941. The feasibility was confirmed by detecting IxS in serum samples from healthy people and CKD patient. A positive correlation was observed between IxS quantified by LC-MS/MS and IxS-derived indigo quantified by spectrophotometry (P &amp;lt; 0 001, r = 0.088). Furthermore, the results of colorimetric analysis were compared with those obtained using LC–MS/MS, and no significant difference was found between them (Fig. 2). This result indicated that the colorimetric detection of IxS is comparable to LC–MS/MS, but it is simpler and faster for CKD monitoring in clinical practice. CONCLUSION The aim of our study was to develop and validate a quantitative method for the rapid estimation of serum IxS employable to monitor CKD progression in clinical setting, identify patient at risk and help further dialysis therapies aimed to lowering blood uremic toxins. Conventionally, LC-MS/MS is the most used diagnostic tool for the IxS quantification due to high sensitivity and accuracy. However, the great rigorousness analysis together with the requirement of real-time results hinders its employment in the clinical practice. Our results provide a low-cost, easy-to-operate, label-free, rapid and sensitive method for IxS determination for application in tracking IxS in blood and studying CKD progression.

VIGIL: A Python tool for automatized probabilistic VolcanIc Gas dIspersion modeLling

Probabilistic volcanic hazard assessment is a standard methodology based on running a deterministic hazard quantification tool multiple times to explore the full range of uncertainty in the input parameters and boundary conditions, in order to probabilistically quantify the variability of outputs accounting for such uncertainties. Nowadays, different volcanic hazards are quantified by means of this approach. Among these, volcanic gas emission is particularly relevant given the threat posed to human health if concentrations and exposure times exceed certain thresholds. There are different types of gas emissions but two main scenarios can be recognized: hot buoyant gas emissions from fumaroles and the ground and dense gas emissions feeding density currents that can occur, e.g., in limnic eruptions. Simulation tools are available to model the evolution of critical gas concentrations over an area of interest. Moreover, in order to perform probabilistic hazard assessments of volcanic gases, simulations should account for the natural variability associated to aspects such as seasonal and daily wind conditions, localized or diffuse source locations, and gas fluxes. Here we present VIGIL (automatized probabilistic VolcanIc Gas dIspersion modeLling), a new Python tool designed for managing the entire simulation workflow involved in single and probabilistic applications of gas dispersion modelling. VIGIL is able to manage the whole process from meteorological data processing, needed to run gas dispersion in both the dilute and dense gas flow scenarios, to the post processing of models’ outputs. Two application examples are presented to show some of the modelling capabilities offered by VIGIL.

Statistical parametrization of cell cytoskeleton reveals lung cancer cytoskeletal phenotype with partial EMT signature

Epithelial–mesenchymal Transition (EMT) is a multi-step process that involves cytoskeletal rearrangement. Here, developing and using an image quantification tool, Statistical Parametrization of Cell Cytoskeleton (SPOCC), we have identified an intermediate EMT state with a specific cytoskeletal signature. We have been able to partition EMT into two steps: (1) initial formation of transverse arcs and dorsal stress fibers and (2) their subsequent conversion to ventral stress fibers with a concurrent alignment of fibers. Using the Orientational Order Parameter (OOP) as a figure of merit, we have been able to track EMT progression in live cells as well as characterize and quantify their cytoskeletal response to drugs. SPOCC has improved throughput and is non-destructive, making it a viable candidate for studying a broad range of biological processes. Further, owing to the increased stiffness (and by inference invasiveness) of the intermediate EMT phenotype compared to mesenchymal cells, our work can be instrumental in aiding the search for future treatment strategies that combat metastasis by specifically targeting the fiber alignment process.