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Estimating coastal tsunami impact for early-warning or long-term hazard analysis requires the calculation of inundation metrics such as flow-depth or momentum flux. Both applications require the simulation of large numbers of scenarios to capture both the aleatory variability and the epistemic tsunami uncertainty. A computationally demanding step in simulating inundation is solving the non-linear shallow water (NLSW) equations on meshes with sufficiently high resolution to represent the local elevation accurately enough to capture the physics governing the flow. This computational expense is particularly challenging in the context of Tsunami Early Warning where strict time constraints apply. A machine learning (ML) model that predicts inundation maps from offshore simulation results with acceptable accuracy, trained on an acceptably small training set of full simulations, could replace the computationally expensive NLSW part of the simulations for vast numbers of scenarios and predict inundation rapidly and with reduced computational demands. We consider the application of an encoder–decoder based neural network to predict high-resolution inundation maps based only on more cheaply calculated simulated time-series at a limited number of offshore locations. The network needs to be trained using input offshore time-series and the corresponding inundation maps from previously calculated full simulations. We develop and evaluate the ML model on a comprehensive set of inundation simulations for the coast of eastern Sicily for tens of thousands of subduction earthquake sources in the Mediterranean Sea. We find good performance for this case study even using relatively small training sets (order of hundreds) provided that appropriate choices are made in the specification of model parameters, the specification of the loss function and the selection of training events. The uncertainty in the prediction for any given location decreases with the number of training events that inundate that location, with a good range of flow depths needed for accurate predictions. This means that care is needed to ensure that rarer high-inundation scenarios are well-represented in the training sets. The importance of applying regularization techniques increases as the size of the training sets decreases. The computational gain of the proposed methodology depends on the number of complete simulations needed to train the neural network, ranging between 164 and 4196 scenarios in this study. The cost of training the network is small in comparison with the cost of the numerical simulations and, for an ensemble of around 28 000 scenarios, this represents a 6- to 170-fold reduction in computing costs. [ABSTRACT FROM AUTHOR]

Bovine serum albumin (BSA) is widely used as an additive in polymerase chain reaction (PCR)-based microfluidic devices to passivate reactors and alleviate nucleic-acid amplification. BSA is available commercially in two types: either acetylated or non-acetylated. A survey of literature indicates that both types of BSA are used in PCR-based microfluidic devices. Our study results reveal that the use of acetylated BSA in PCR micro-devices leads to differential inhibition of PCR, compared to non-acetylated BSA. This result is noticed for the first time, and the differential inhibition generally goes un-noticed, as compared to complete PCR inhibition. [ABSTRACT FROM AUTHOR]

Abstract: We report disposable microfluidic devices harboring an array of unsealed reactors, which are pre-loaded with different primer pairs for simultaneous (parallel) real-time PCR-based detection of multiple waterborne pathogens. The PCR mixture loading among an array of reactors and subsequent isolation of the reactors was solely realized by a single step capillary-based flow scheme, which eliminates the use of pumps, valves and liquid handling instruments. We incorporated a localized thermal cycling scheme to minimize evaporative loss of PCR mixture in unsealed microreactors, which greatly reduces the complexity of the microfabrication and fluidic operation process, in cases, where valving or sealing of the reactors for PCR thermal cycling is required. Experiments were performed to determine the optimal microreactor design parameters, so as to perform bubble-free PCR with minimal evaporative loss in unsealed reactors. The potential of the microfluidic device was successfully demonstrated by specifically detecting genomic DNA of waterborne pathogens from a pool of genomic DNA template. [Copyright &y& Elsevier]

Abstract: Electrochemistry-based detection methods hold great potential towards development of hand-held nucleic-acid analyses instruments. In this work, we demonstrate the implementation of in situ electrochemical (EC) detection method in a microfluidic flow-through EC-qPCR (FTEC-qPCR) device, where both the amplification of the target nucleic-acid sequence and subsequent EC detection of the PCR amplicon are realized simultaneously at selected PCR cycles in the same device. The FTEC-qPCR device utilizes methylene blue (MB), an electroactive DNA intercalator, for electrochemical signal measurements in the presence of PCR reagent components. Our EC detection method is advantageous, when compared to other existing EC methods for PCR amplicon analysis, since FTEC-qPCR does not require probe-modified electrodes, or asymmetric PCR, or solid-phase PCR. Key technical issues related to surface passivation, electrochemical measurement, PCR inhibition by metal electrode, bubble-free PCR, were investigated. By controlling the concentration of MB and the exposure of PCR mixture to the bare metal electrode, we successfully demonstrated electrochemical measurement of MB in solution-phase, symmetric PCR by amplifying a fragment of lambda phage DNA. The threshold cycle (C t) values for both the electrochemical and fluorescence-based assays decreased linearly with the increase of the input target quantity. The sensitivity of EC-based detection of PCR products is comparable to the sensitivity of an optical fluorescence detection system. [Copyright &y& Elsevier]

We developed an integrated microfluidic flow-through EC-PCR (EC-PCR) microdevice for the concurrent DNA amplification, PCR products EC detection and PCR products quantification instead of the current available fluorescence detection scheme. The microfluidic flow-through EC-PCR microdevice was fabricated with the state-of-the-art microfabrication technology, by bonding a bottom glass substrate having a microelectrode array to a top glass cover having the microchannels made of PDMS material. Both the amplification of the target DNA sequence and the subsequent EC detection of the PCR products were carried out concurrently on the integrated device by real-time monitoring. The underlying principle of the microfluidic flow-through EC-PCR method was based on the changes of current signal of methylene blue (MB), which worked as an electrochemically active species DNA intercalator in the PCR mixture, during the amplification process at the extension phase. The results shown in this work indicated that the nucleic acid analysis could be performed in a fast thermal cycling and true real-time quantitative electrochemical detection. The signal variation trends of the EC detection and the fluorescence detection were the same in our verification measurements for both methods, which suggested that the EC detection method was feasible for this application. [ABSTRACT FROM AUTHOR]

Abstract: A simple and reliable microfluidic method to load and seal a two-dimensional matrix of nanoliter wells, preloaded with primer pairs, for performing PCR without well-to-well cross-contamination was developed. With a vacuum aided microfluidics the distribution of sample mixture into 100 wells was achieved within 0.2s, and all the wells were fluidically isolated within 0.5s. The wells were subsequently sealed with poly-(dimethylsiloxane) (PDMS) prepolymer to prevent evaporation of PCR mixture and primer cross-contamination. The performance of the PDMS matrix chip was successfully tested by simultaneous (parallel) real-time quantitative PCR amplification of 10 different gene targets against cDNA template from human hepatocellular carcinoma, in 120nl volume using EvaGreen fluorescent dye. Negligible cross-contamination between primers in different wells was confirmed with the PCR result, and by investigating the diffusion of the primers. The microfluidic channel design, chip structure, vacuum level, and operation schedule were optimized to achieve successful vacuum aided microfluidics for PCR mixture loading without air trapping. The microfluidics and the well matrix chip platform are suitable for both low- and high-density well applications including PCR related applications as well as immunoassays and cell-based assays. [Copyright &y& Elsevier]

Cell–cell communication and physical interactions play a vital role in cancer initiation, homeostasis, progression, and immune response. Here, we report a system that combines live capture of different cell types, co-incubation, time-lapse imaging, and gene expression profiling of doublets using a microfluidic integrated fluidic circuit that enables measurement of physical distances between cells and the associated transcriptional profiles due to cell–cell interactions. We track the temporal variations in natural killer—triple-negative breast cancer cell distances and compare them with terminal cellular transcriptome profiles. The results show the time-bound activities of regulatory modules and allude to the existence of transcriptional memory. Our experimental and bioinformatic approaches serve as a proof of concept for interrogating live-cell interactions at doublet resolution. Together, our findings highlight the use of our approach across different cancers and cell types. A microfluidic system combines the capture, co-incubation, time-lapse imaging, and gene expression profiling of doublets to measure the distances between cells and associated transcriptional profiles due to cell–cell interactions. [ABSTRACT FROM AUTHOR]

Multimodal analysis of circulating tumour cells (CTCs) has the potential to provide remarkable insight for cancer development and metastasis. CTCs and CTC clusters investigation using single-cell analysis, enables researchers to gain crucial information on metastatic mechanisms and the genomic alterations responsible for drug resistance, empowering treatment, and management of cancer. Despite a plethora of CTC isolation technologies, careful attention to the strengths and weaknesses of each method should be considered in order to isolate these rare cells. Here, we provide an overview of cutting-edge technologies used for single-cell isolation and analysis of CTCs. Additionally, we highlight the biological features, clinical application, and the therapeutic potential of CTCs and CTC clusters using single-cell analysis platforms for cancer management. In-depth characterisation of circulating tumour cells (CTCs) has shown promise in diagnosis and management of cancer patients in a noninvasive manner. However, rarity of CTCs poses critical challenges for isolating and analysing them. CTC analysis provides insights into tumour heterogeneity beyond genomic aberrations that are not found in circulating tumour DNA (ctDNA). Single-cell molecular analysis of CTCs offers a new prognostic approach to identification of targeted therapy resistance mechanisms. Choice of technology for isolation and analysis of CTCs mainly depends on cell-loss, study cost per CTC, and workflow complexity. Current technologies suffer from high analysis costs and cell-loss during handling of the low number of CTCs. Additionally, different physical and biological features of CTC clusters often lead to difficulties in analysing them via current commercial platforms. [ABSTRACT FROM AUTHOR]

Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships but require efficient methods for cell capture and mRNA sequencing. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths, the limitations of shallow sequencing have not been investigated directly. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (∼50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In the developing cortex, we identify diverse cell types, including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells. [ABSTRACT FROM AUTHOR]

Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells. Conflicts of Interest The authors declare no conflict of interest. References 1 Iyer A., Gupta K., Sharma S., Hari K., Lee Y.F., Ramalingam N., Yap Y.S., West J., Bhagat A.A., Subramani B.V. Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells. [Extracted from the article]

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic system for label-free enrichment of CTCs. [ABSTRACT FROM AUTHOR]