A simple and robust event-detection algorithm for single-cell impedance cytometry

Microfluidic impedance cytometry is emerging as a powerful label-free technique for the characterization of single biological cells. In order to increase the sensitivity and the specificity of the technique, suited digital signal processing methods are required to extract meaningful information from measured impedance data. In this study, a simple and robust event-detection algorithm for impedance cytometry is presented. Since a differential measuring scheme is generally adopted, the signal recorded when a cell passes through the sensing region of the device exhibits a typical odd-symmetric pattern. This feature is exploited twice by the proposed algorithm: first, a preliminary segmentation, based on the correlation of the data stream with the simplest odd-symmetric template, is performed; then, the quality of detected events is established by evaluating their E2O index, that is, a measure of the ratio between their even and odd parts. A thorough performance analysis is reported, showing the robustness of the algorithm with respect to parameter choice and noise level. In terms of sensitivity and positive predictive value, an overall performance of 94.9% and 98.5%, respectively, was achieved on two datasets relevant to microfluidic chips with very different characteristics, considering three noise levels. The present algorithm can foster the role of impedance cytometry in single-cell analysis, which is the new frontier in "Omics.

Effective computational modeling of erythrocyte electro-deformation

Due to its crucial role in pathophysiology, erythrocyte deformability represents a subject of intense experimental and modeling research. Here a computational approach to electro-deformation for erythrocyte mechanical characterization is presented. Strong points of the proposed strategy are: (1) an accurate computation of the mechanical actions induced on the cell by the electric field, (2) a microstructurally-based continuum model of the erythrocyte mechanical behavior, (3) an original rotationfree shell finite element, especially suited to the application in hand. As proved by the numerical results, the developed tool is effective and sound, and can foster the role of electro-deformation in single-cell mechanical phenotyping

Static and dynamic thermal properties of construction components: A comparison in idealized and experimental conditions using lumped parameter models

The U values assumptions for construction components represent a significant source of uncertainty when estimating the energy performance of buildings. This uncertainty affects decision-making processes in multiple ways, from policy making to design of new and refurbished buildings. The correct estimation of both static (e.g. thermal transmittance) and dynamic thermal properties is crucial for quality assurance in building performance assessment. Further, while today many sophisticated simulators are available for building performance modelling, lumped parameter models can help reducing computational time for parametric simulation or optimization and enable inverse estimation of lumped thermal characteristics. A lumped parameter approach for construction components is proposed, for example, by the ISO 52016-1:2017 norm, introducing simplifications that are intrinsically dependent on component's stratigraphy. This approach complements ISO 13786:2017 norm method, which is limited to steady-state periodic temperature and heat flux boundary conditions. In this research we consider these two different approaches, detailed and lumped modelling, comparing them first in idealized conditions and then in experimental conditions to analyse the robustness of methods

Planning smart cities. Comparison of two quantitative multicriteria methods applied to real case studies

Today, cities are facing many challenges such as pollution, resource consumption, gas emissions and social inequality. Many future city views have been developed to solve these issues such as the Smart City model. In literature several methods have been proposed to plan a Smart city, but, at the best of the authors’ knowledge, only a few of them have been really applied to the urban context. Most of them are indeed theoretical and qualitative approaches, providing scenarios that have not been applied to real cities/districts. Moreover, a comparison among the results of different quantitative planning models applied to real case studies is still missing. In this framework, the aim of the paper is to propose a new quantitative method based on a previous qualitative model developed by the same authors. The feasibility and validity of the method will be tested through the comparison with an existing AHP model and the application of both approaches on two real case studies, characterized by different territorial levels. Results of the analysis show that both methods are consistent, reliable and do provide similar results despite the differences in the application process

Single-cell microfluidic impedance cytometry: From raw signals to cell phenotypes using data analytics

The biophysical analysis of single-cells by microfluidic impedance cytometry is emerging as a label-free and high-throughput means to stratify the heterogeneity of cellular systems based on their electrophysiology. Emerging applications range from fundamental life-science and drug assessment research to point-of-care diagnostics and precision medicine. Recently, novel chip designs and data analytic strategies are laying the foundation for multiparametric cell characterization and subpopulation distinction, which are essential to understand biological function, follow disease progression and monitor cell behaviour in microsystems. In this tutorial review, we present a comparative survey of the approaches to elucidate cellular and subcellular features from impedance cytometry data, covering the related subjects of device design, data analytics (i.e., signal processing, dielectric modelling, population clustering), and phenotyping applications. We give special emphasis to the exciting recent developments of the technique (timeframe 2017-2020) and provide our perspective on future challenges and directions. Its synergistic application with microfluidic separation, sensor science and machine learning can form an essential tool-kit for label-free quantification and isolation of subpopulations to stratify heterogeneous biosystems

State update algorithm for associative elastic-plastic pressure-insensitive materials by incremental energy minimization

This work presents a new state update algorithm for small-strain associative elastic-plastic constitutive models, treating in a unified manner a wide class of deviatoric yield functions with linear or nonlinear strain-hardening. The algorithm is based on an incremental energy minimization approach, in the framework of generalized standard materials with convex free energy and dissipation potential. An efficient method for the computation of the latter, its gradient and its Hessian is provided, using Haigh-Westergaard stress invariants. Numerical results on a single material point loading history and finite element simulations are reported to prove the effectiveness and the versatility of the method. Its merit turns out to be complementary to the classical return map strategy, because no convergence difficulties arise if the stress is close to high curvature points of the yield surface

Developing an algorithm to assess the UV erythemal dose for outdoor workers Validation through direct measures

An algorithm has been developed to determine the annual dose of UV solar radiation for outdoor workers. The dose is indirectly assessed basing on satellite data, mean global irradiance values, workers' data obtained by means of a questionnaire and corrective coefficients provided by a mathematical model. The values obtained by the use of the algorithm are compared with those obtained by measurement records in different environments. Results demonstrated that the algorithm estimates the mean daily erythemal dose with good approximation

State update algorithm for associative elastic-plastic pressure-insensitive materials by incremental energy minimization

This work presents a new state update algorithm for small-strain associative elastic-plastic constitutive models, treating in a unified manner a wide class of deviatoric yield functions with linear or nonlinear strain-hardening. The algorithm is based on an incremental energy minimization approach, in the framework of generalized standard materials with convex free energy and dissipation potential. An efficient algorithm for the computation of the latter, its gradient and its Hessian is provided, using Haigh-Westergaard stress invariants. Numerical results on a single material point loading history and finite element simulations are reported to prove the effectiveness and the versatility of the method. Its merit turns out to be complementary to the classical return map strategy, because no convergence difficulties arise if the stress is close to high curvature points of the yield surface