A general framework for the evaluation of shock-capturing schemes

We introduce a standardized procedure for benchmarking shock-capturing schemes which is intended to go beyond traditional case-by-case analysis, by setting objective metrics for cross-comparison of flow solvers. The main idea is that use of shock-capturing schemes yields both distributed errors associated with propagation of wave-like disturbances in smooth flow regions, and localized errors at shocks where nonlinear numerical mechanisms are most active. Our standardized error evaluation framework relies on previous methods of analysis for the propagation error with associated cost/error mapping, and on novel analysis of the shock-capturing error based on a model scalar problem. Amplitude and phase errors are identified for a number of classical shock-capturing schemes with different order of accuracy. Whereas all schemes are found to be, as expected, first-order accurate at shocks, quantitative differences are found to be significant, and we find that certain schemes in wide use (e.g. high-order WENO schemes) may yield substantial over-amplification of incoming disturbances at shocks. Illustrative calculations are also shown for the 1D Euler equations, which support sufficient generality of the analysis, although nonlinearity suggests caution in straightforward extrapolation to other flow cases

Red blood cell as an adaptive optofluidic microlens

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Encoding multiple holograms for speckle-noise reduction in optical display.

In digital holography (DH) a mixture of speckle and incoherent additive noise, which appears in numerical as well as in optical reconstruction, typically degrades the information of the object wavefront. Several methods have been proposed in order to suppress the noise contributions during recording or even during the reconstruction steps. Many of them are based on the incoherent combination of multiple holographic reconstructions achieving remarkable improvement, but only in the numerical reconstruction i.e. visualization on a pc monitor. So far, it has not been shown the direct synthesis of a digital hologram which provides the denoised optical reconstruction. Here, we propose a new effective method for encoding in a single complex wavefront the contribution of multiple incoherent reconstructions, thus allowing to obtain a single synthetic digital hologram that show significant speckle-reduction when optically projected by a Spatial Light Modulator (SLM)

Optical signature of erythrocytes by light scattering in microfluidic flows

A camera-based light scattering approach coupled with a viscoelasticity-induced cell migration technique has been used to characterize the morphological properties of erythrocytes in microfluidic flows. We have obtained the light scattering profiles (LSPs) of individual living cells in microfluidic flows over a wide angular range and matched them with scattering simulations to characterize their morphological properties. The viscoelasticity-induced 3D cell alignment in microfluidic flows has been investigated by bright-field and holographic microscopy tracking, where the latter technique has been used to obtain precise cell alignment profiles in-flow. Such information allows variable cell probability control in microfluidic flows at very low viscoelastic polymer concentrations, obtaining cell measurements that are almost physiological. Our results confirm the possibility of precise, label-free analysis of individual living erythrocytes in microfluidic flows

AI-aided holographic flow cytometry for label-free identification of ovarian cancer cells in the presence of unbalanced datasets

Liquid biopsy is a valuable emerging alternative to tissue biopsy with great potential in the noninvasive early diagnostics of cancer. Liquid biopsy based on single cell analysis can be a powerful approach to identify circulating tumor cells (CTCs) in the bloodstream and could provide new opportunities to be implemented in routine screening programs. Since CTCs are very rare, the accurate classification based on high-throughput and highly informative microscopy methods should minimize the false negative rates. Here, we show that holographic flow cytometry is a valuable instrument to obtain quantitative phase-contrast maps as input data for artificial intelligence (AI)-based classifiers. We tackle the problem of discriminating between A2780 ovarian cancer cells and THP1 monocyte cells based on the phase-contrast images obtained in flow cytometry mode. We compare conventional machine learning analysis and deep learning architectures in the non-ideal case of having a dataset with unbalanced populations for the AI training step. The results show the capacity of AI-aided holographic flow cytometry to discriminate between the two cell lines and highlight the important role played by the phase-contrast signature of the cells to guarantee accurate classification

Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry

To efficiently tackle certain tumor types, finding new biomarkers for rapid and complete phenotyping of cancer cells is highly demanded. This is especially the case for the most common pediatric solid tumor of the sympathetic nervous system, namely, neuroblastoma (NB). Liquid biopsy is in principle a very promising tool for this purpose, but usually enrichment and isolation of circulating tumor cells in such patients remain difficult due to the unavailability of universal NB cell-specific surface markers. Here, we show that rapid screening and phenotyping of NB cells through stain-free biomarkers supported by artificial intelligence is a viable route for liquid biopsy. We demonstrate the concept through a flow cytometry based on label-free holographic quantitative phase-contrast microscopy empowered by machine learning. In detail, we exploit a hierarchical decision scheme where at first level NB cells are classified from monocytes with 97.9% accuracy. Then we demonstrate that different phenotypes are discriminated within NB class. Indeed, for each cell classified as NB its belonging to one of four NB sub-populations (i.e., CHP212, SKNBE2, SHSY5Y, and SKNSH) is evaluated thus achieving accuracy in the range 73.6%-89.1%. The achieved results solve the realistic problem related to the identification circulating tumor cell, i.e., the possibility to recognize and detect tumor cells morphologically similar to blood cells, which is the core issue in liquid biopsy based on stain-free microscopy. The presented approach operates at lab-on-chip scale and emulates real-world scenarios, thus representing a future route for liquid biopsy by exploiting intelligent biomedical imaging

Rete di Pronto Intervento satellitare

Nell’ambito dell’attività di ricerca e monitoraggio dell’INGV, riveste notevole importanza la Rete Sismica Nazionale RSN, quale struttura di sorveglianza sismica del territorio italiano. La rete, che ad oggi conta più di 240 stazioni uniformemente distribuite su tutto il territorio italiano, si avvale di una rete di pronto intervento (RPI) che ha l’obiettivo primario di incrementare, in zona epicentrale, il numero di stazioni, per una migliore localizzazione della sequenza sismica in corso. Per giungere a quest’obiettivo, l’intera struttura utilizza dei sistemi di trasmissione dati dedicati, che permettono di trasmettere i dati in tempo reale presso il centro di acquisizione di Roma, affinché possano essere utilizzati ai fini del processo di localizzazione.PublishedTrieste, Italy1.1. TTC - Monitoraggio sismico del territorio nazionaleope