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In the recent years, fuzzy variables (FVs) and random-fuzzy variables (RFVs) have been proposed to represent the measurement results with their associated uncertainty. However, up to now, the different authors do not yet agree in the mathematical way FVs should be composed together, so different approaches have been proposed. This paper compares these approaches to find their advantages and disadvantages and shows a new proposal that is supposed to hopefully overcome the disadvantages of the original approaches. [ABSTRACT FROM AUTHOR]

In this paper, we consider uncertainty handling and propagation by means of random fuzzy variables (RFVs) through a computer-aided-diagnosis (CADx) system for the early diagnosis of breast cancer. In particular, the denoising and the contrast enhancement of microcalcifications is specifically addressed, providing a novel methodology for separating the foreground and the background in the image to selectively process them. The whole system is then assessed by metrological aspects. In this context, we assume that the uncertainty associated to each pixel of the image has both a random and a non-negligible systematic contribution. Consequently, a preliminary noise variance estimation is performed on the original image, and then, using suitable operators working on RFVs, the uncertainty propagation is evaluated through the whole system. Finally, we compare our results with those obtained by a Monte Carlo method. [ABSTRACT FROM AUTHOR]

A fuzzy logic system (FLS) can be completely described through its behavioral function. If the analytical expression of this function is known, then the system synthesis consists in the realization of a structure implementing it in the most convenient way. So, the problem of the FLS design can be considered as a function approximation problem. Many methods have been proposed in literature to minimize a certain error parameter. Nevertheless, sometimes the specifications of the problem need some further analytical properties of the approximating function. This paper analyzes multiple-input-single-output (MISO) FLSs with polynomial membership functions inspecting the case of two input variables. In particular, it will be shown what characteristics such systems should have in order to respect the constraints imposed on the derivatives of the implemented function, such as their continuity up to a desired order and even their value in the grid paints of the input space. Furthermore, we prove that these systems can uniformly approximate, under certain conditions, any multivariate function with a desired approximation accuracy both on the target function and its partial derivatives. Proper illustrative examples will show the behavior of these FLSs comparing their performance with other methods proposed in literature. [ABSTRACT FROM AUTHOR]

Live-cell microscopy routinely provides massive amounts of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell-cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on- chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer-associated fibroblasts directly inhibit cancer cell apoptosis, independently from anticancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications. [ABSTRACT FROM AUTHOR]

A novel optically induced dielectrophoresis (ODEP) system that can operate under flow conditions is designed for automatic trapping of cells and subsequent induction of 2D multi‐frequency cell trajectories. Like in a "ping‐pong" match, two virtual electrode barriers operate in an alternate mode with varying frequencies of the input voltage. The so‐derived cell motions are characterized via time‐lapse microscopy, cell tracking, and state‐of‐the‐art machine learning algorithms, like the wavelet scattering transform (WST). As a cell‐electrokinetic fingerprint, the dynamic of variation of the cell displacements happening, over time, is quantified in response to different frequency values of the induced electric field. When tested on two biological scenarios in the cancer domain, the proposed approach discriminates cellular dielectric phenotypes obtained, respectively, at different early phases of drug‐induced apoptosis in prostate cancer (PC3) cells and for differential expression of the lectine‐like oxidized low‐density lipoprotein receptor‐1 (LOX‐1) transcript levels in human colorectal adenocarcinoma (DLD‐1) cells. The results demonstrate increased discrimination of the proposed system and pose an additional basis for making ODEP‐based assays addressing cancer heterogeneity for precision medicine and pharmacological research. [ABSTRACT FROM AUTHOR]

Summary A major challenge in cancer research is the complexity of the tumor microenvironment, which includes the host immunological setting. Inspired by the emerging technology of organ-on-chip, we achieved 3D co-cultures in microfluidic devices (integrating four cell populations: cancer, immune, endothelial, and fibroblasts) to reconstitute ex vivo a human tumor ecosystem (HER2+ breast cancer). We visualized and quantified the complex dynamics of this tumor-on-chip, in the absence or in the presence of the drug trastuzumab (Herceptin), a targeted antibody therapy directed against the HER2 receptor. We uncovered the capacity of the drug trastuzumab to specifically promote long cancer-immune interactions (>50 min), recapitulating an anti-tumoral ADCC (antibody-dependent cell-mediated cytotoxicity) immune response. Cancer-associated fibroblasts (CAFs) antagonized the effects of trastuzumab. These observations constitute a proof of concept that tumors-on-chip are powerful platforms to study ex vivo immunocompetent tumor microenvironments, to characterize ecosystem-level drug responses, and to dissect the roles of stromal components. Graphical Abstract Highlights • A complex HER2+ breast cancer ecosystem is reconstituted and quantitatively described • The effects of the drug trastuzumab (Herceptin) are directly visualized ex vivo • Trastuzumab promotes long cancer-immune interactions and an ADCC immune response • Trastuzumab and CAFs have antagonist immunomodulation effects Inspired by the emerging technology of tumor-on-chip, Nguyen et al. reconstituted ex vivo a human tumor microenvironment (HER2+ breast cancer), characterized the ecosystem-level responses to the drug trastuzumab (Herceptin), and dissected the roles of stromal components (immune cells and fibroblasts), demonstrating the power of immunocompetent tumors-on-chip for preclinical drug studies. [ABSTRACT FROM AUTHOR]

Microfluidic devices are increasingly widespread in the literature, being applied to numerous exciting applications, from chemical research to Point-of-Care devices, passing through drug development and clinical scenarios. Setting up these microenvironments, however, introduces the necessity of locally controlling the variables involved in the phenomena under investigation. For this reason, the literature has deeply explored the possibility of introducing sensing elements to investigate the physical quantities and the biochemical concentration inside microfluidic devices. Biosensors, particularly, are well known for their high accuracy, selectivity, and responsiveness. However, their signals could be challenging to interpret and must be carefully analysed to carry out the correct information. In addition, proper data analysis has been demonstrated even to increase biosensors' mentioned qualities. To this regard, machine learning algorithms are undoubtedly among the most suitable approaches to undertake this job, automatically learning from data and highlighting biosensor signals' characteristics at best. Interestingly, it was also demonstrated to benefit microfluidic devices themselves, in a new paradigm that the literature is starting to name "intelligent microfluidics", ideally closing this benefic interaction among these disciplines. This review aims to demonstrate the advantages of the triad paradigm microfluidics-biosensors-machine learning, which is still little used but has a great perspective. After briefly describing the single entities, the different sections will demonstrate the benefits of the dual interactions, highlighting the applications where the reviewed triad paradigm was employed. [ABSTRACT FROM AUTHOR]

Due to its simple and cost-effective manufacturing process, Laser-Induced Graphene (LIG) has opened up possibilities for intriguing electronic applications in various contexts, including Lab-On-Chip scenarios. This attractive material can be produced on multiple organic substrates, but the most common are PolyImide sheets, which are generally less versatile than other plastic substrates. Moreover, they are opaque to multiple wavelengths in visible light, limiting their applicability in experiments that require a completely transparent material. For this reason, we developed a new and cheap transfer protocol, which exploits the partial solubility of thermoplastic substrates in a solvent to make them infiltrate the LIG, peeling it off from PolyImide. After focusing on transferring LIG on Poly Methyl Metacrylate substrates, the protocol has been optimized to minimize the transferred LIG (t-LIG) sheet resistance. The obtained material is then characterized by its morphology, electrode capabilities, and biocompatibility. Finally, t-LIG was simulated and used to apply an electric field in a Lab-On-Chip designed for microparticle manipulation by dielectrophoresis. The obtained results demonstrated the great potential of t-LIG in Lab-On-Chip and biological experiments, suggesting new exciting applications that rely on both optical and electrical investigations. [Display omitted] • A wet transfer technique is proposed to deliver LIG geometries on several types of plastic substrates. • The obtained material is characterized electrically and morphologically. • The cytocompatibility of the material was also investigated. • A Lab-On-Chip was designed and realized for dielectrophoresis applications. [ABSTRACT FROM AUTHOR]

In this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer's disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships. [ABSTRACT FROM AUTHOR]

• A novel deep prior algorithm based on a recursive strategy has been developed for enhancing spatial resolution of videos recorded by light time-lapse microscopy. • The performance has been further increased by combining the proposed method with handcrafted regularizers. • To validate the proposed approach, artificial generated videos and real experimental videos have been analyzed. • The unsupervised proposed method has been compared with the most competing deep learning-based super resolution algorithms revealing very good performances. [Display omitted] Biological experiments based on organ-on-chips (OOCs) exploit light Time-Lapse Microscopy (TLM) for a direct observation of cell movement that is an observable signature of underlying biological processes. A high spatial resolution is essential to capture cell dynamics and interactions from recorded experiments by TLM. Unfortunately, due to physical and cost limitations, acquiring high resolution videos is not always possible. To overcome the problem, we present here a new deep learning-based algorithm that extends the well-known Deep Image Prior (DIP) to TLM Video Super Resolution without requiring any training. The proposed Recursive Deep Prior Video method introduces some novelties. The weights of the DIP network architecture are initialized for each of the frames according to a new recursive updating rule combined with an efficient early stopping criterion. Moreover, the DIP loss function is penalized by two different Total Variation-based terms. The method has been validated on synthetic, i.e., artificially generated, as well as real videos from OOC experiments related to tumor-immune interaction. The achieved results are compared with several state-of-the-art trained deep learning Super Resolution algorithms showing outstanding performances. [ABSTRACT FROM AUTHOR]

The dynamic contrast-enhanced MR imaging plays a crucial role in evaluating the effectiveness of neoadjuvant chemotherapy (NAC) even since its early stage through the prediction of the final pathological complete response (pCR). In this study, we proposed a transfer learning approach to predict if a patient achieved pCR (pCR) or did not (non-pCR) by exploiting, separately or in combination, pre-treatment and early-treatment exams from I-SPY1 TRIAL public database. First, low-level features, i.e., related to local structure of the image, were automatically extracted by a pre-trained convolutional neural network (CNN) overcoming manual feature extraction. Next, an optimal set of most stable features was detected and then used to design an SVM classifier. A first subset of patients, called fine-tuning dataset (30 pCR; 78 non-pCR), was used to perform the optimal choice of features. A second subset not involved in the feature selection process was employed as an independent test (7 pCR; 19 non-pCR) to validate the model. By combining the optimal features extracted from both pre-treatment and early-treatment exams with some clinical features, i.e., ER, PgR, HER2 and molecular subtype, an accuracy of 91.4% and 92.3%, and an AUC value of 0.93 and 0.90, were returned on the fine-tuning dataset and the independent test, respectively. Overall, the low-level CNN features have an important role in the early evaluation of the NAC efficacy by predicting pCR. The proposed model represents a first effort towards the development of a clinical support tool for an early prediction of pCR to NAC. [ABSTRACT FROM AUTHOR]