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Generalizing a construction presented in [3], we show that the orbit space of $B_2$ less the image of coordinate lines under the quotient map is equipped with two Dubrovin-Frobenius manifold structures which are related respectively to the defocusing and the focusing nonlinear Schr\"odinger (NLS) equations. Motivated by this example, we study the case of $B_n$ and we show that the defocusing case can be generalized to arbitrary $n$ leading to a Dubrovin-Frobenius manifold structure on the orbit space of the group. The construction is based on the existence of a non-degenerate and non-constant invariant bilinear form that plays the role of the Euclidean metric in the Dubrovin-Saito standard setting. Up to $n=4$ the prepotentials we get coincide with those associated with constrained KP equations discussed in [20]., Comment: 37 pages, 1 figure

Organ-on-chip (OOC) devices are miniaturized devices replacing animal models in drug discovery and toxicology studies. The majority of OOC devices are made from polydimethylsiloxane (PDMS), an elastomer widely used in microfluidic prototyping, but posing a number of challenges to experimentalists, including leaching of uncured oligomers and uncontrolled absorption of small compounds. Here we assess the suitability of polylactic acid (PLA) as a replacement material to PDMS for microfluidic cell culture and OOC applications. We changed the wettability of PLA substrates and demonstrated the functionalization method to be stable over a time period of at least 9 months. We successfully cultured human cells on PLA substrates and devices, without coating. We demonstrated that PLA does not absorb small molecules, is transparent (92% transparency), and has low autofluorescence. As a proof of concept of its manufacturability, biocompatibility, and transparency, we performed a cell tracking experiment of prostate cancer cells in a PLA device for advanced cell culture.

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.

Pain is an alert state of the human body that can be conveyed to the external world through different modalities. A possible communication channel for human pain is represented by facial expressions, whose role in social interactions has been well established. In this work, the link between pain and transfer entropy (TE), passing through facial expressions, is investigated. A new approach to the vision-based measurement (VBM) of pain is presented, which is based on TE among the time-series of facial landmarks positions. The system is composed of three main blocks: A VBM block for the automatic landmarking and the generation of the time-series from the video-sequences; a second block for the evaluation of TE; and finally a classification model based on machine learning algorithms for pain assessment. A public database of video sequences of patients experiencing pain in a controlled scenario was used for the characterization of the system in terms of accuracy and precision. Different uncertainty contributions related to realistic signals interruptions and fluctuations were modeled and propagated to provide a comprehensive evaluation of the proposed measurement system. The obtained results indicate that TE-based approaches can provide great benefits in automatic pain assessment, opening new perspectives for remote management of patients.

Organ-on-chip and tumor-on-chip microfluidic cell cultures represent a fast-growing research field for modelling organ functions and diseases, for drug development, and for promising applications in personalized medicine. Still, one of the bottlenecks of this technology is the analysis of the huge amount of bio-images acquired in these dynamic 3D microenvironments, a task that we propose to achieve by exploiting the interdisciplinary contributions of computer science and electronic engineering. In this work, we apply this strategy to the study of oncolytic vaccinia virus (OVV), an emerging agent in cancer immunotherapy. Infection and killing of cancer cells by OVV were recapitulated and directly imaged in tumor-on-chip. By developing and applying appropriate image analysis strategies and advanced automatic algorithms, we uncovered synergistic cooperation of OVV and immune cells to kill cancer cells. Moreover, we observed that the kinetics of immune cells were modified in presence of OVV and that these immune modulations varied during the course of infection. A correlation between cancer cell infection and cancer-immune interaction time was pointed out, strongly supporting a cause-effect relationship between infection of cancer cells and their recognition by the immune cells. These results shed new light on the mode of action of OVV, and suggest new clinical avenues for immunotherapy developments.

Motivation In fluorescence microscopy, single-molecule localization microscopy (SMLM) techniques aim at localizing with high-precision high-density fluorescent molecules by stochastically activating and imaging small subsets of blinking emitters. Super resolution plays an important role in this field since it allows to go beyond the intrinsic light diffraction limit. Results In this work, we propose a deep learning-based algorithm for precise molecule localization of high-density frames acquired by SMLM techniques whose ℓ2-based loss function is regularized by non-negative and ℓ0-based constraints. The ℓ0 is relaxed through its continuous exact ℓ0 (CEL0) counterpart. The arising approach, named DeepCEL0, is parameter-free, more flexible, faster and provides more precise molecule localization maps if compared to the other state-of-the-art methods. We validate our approach on both simulated and real fluorescence microscopy data. Availability and implementation DeepCEL0 code is freely accessible at https://github.com/sedaboni/DeepCEL0.

Organs On a Chip (OOCs) represent a sophisticated approach for exploring biological mechanisms and developing therapeutic agents. In conjunction with high-quality time-lapse microscopy (TLM), OOCs allow for the visualization of reconstituted complex biological processes, such as multi-cell-type migration and cell–cell interactions. In this context, increasing the frame rate is desirable to reconstruct accurately cell-interaction dynamics. However, a trade-off between high resolution and carried information content is required to reduce the overall data volume. Moreover, high frame rates increase photobleaching and phototoxicity. As a possible solution for these problems, we report a new hybrid-imaging paradigm based on the integration of OOC/TLMs with a Multi-scale Generative Adversarial Network (GAN) predicting interleaved video frames with the aim to provide high-throughput videos. We tested the performance of the predictive capability of GAN on synthetic videos, as well as on real OOC experiments dealing with tumor–immune cell interactions. The proposed approach offers the possibility to acquire a reduced number of high-quality TLM images without any major loss of information on the phenomena under investigation.

This monograph is a review of Poisson geometry, Lie groupoids and Lie algebroids theory and they relations. We present a summary of the main known results in this areas and as a return of the striking connection of this topics, we present the geometric approach to Conns linearization theorem present in [Crainic and Fernandes 2011]. Este trabalho é uma revisão bibliografica nas areas de geometria de Poisson e teoria de grupoides e algebroides de Lie. Como resultado final desta interação apresentamos a abordagem geometrica para o teorema de linearização de Conn para estruturas de Poisson apresentada em [Crainic and Fernandes 2011].

Facial expression is an important component of the pain measurement process, which can be utilized by clinicians in patients with impaired communicative or cognitive abilities. However, given the complex nature of the pain and the absence of a standard reference, accurate measurement of pain in hospital settings is a difficult practice. In this paper, we present a vision-based measurement system aimed at automatically measuring the pain intensity values through the analysis of the facial expression, based on deep-learning strategies. For the scope of providing a metrological characterization of the system, we designed a benchmark for the estimation of the measurement precision via multiexpert judgments. As a major contribution of this paper, we investigated the interobserver and intraobserver variability as sources of uncertainty at the calibration level in order to provide a robust pain estimation via an automatic framework. Reproducibility and standard reference uncertainty (due to interobserver and intraobserver variability, respectively) have been estimated, modeled, and propagated through the proposed platform using the Guide to the expression of Uncertainty in Measurement (GUM)-compliant procedures. Numerical results, provided in terms of average values and confidence intervals of the accuracy of classification, indicate that such a system can facilitate decision-making in healthcare. Moreover, the proposed benchmark offers new hints to incorporate, rationally, subjective expert judgments within automatic measurement systems.

Vision-based measurement (VBM) systems are powerful tool to extract quantitative information by acquiring video sequence or static images. When a VBM is applied to the evaluation of nominal properties, such as image characteristics, the term VBM is substituted with vision-based evaluation (VBE) by extending the framework of operation unit to a new concept of evaluation unit (EU) for the image analysis and machine learning phases. To this regard, deep learning (DL) approaches have gained an exponential interest in the research and industrial community, thanks to incredible flexibility toward visual words and the possibility to abandon subjective feature extraction procedures. From such an explosiveness of applications, it emerges the need to conduct studies on the capability of DL strategies to deal with uncertainty contributions, i.e., definitional uncertainty related to the measurand and reference uncertainty that may occur during the calibration process. In order to present a benchmark platform to analyze the effect of the major sources of uncertainties estimated, we use here an atomic force microscopy (AFM) imaging scenario for the evaluation of the effect of nanoparticles exposure on human cells in the laboratory. These studies are nowadays fundamental in toxicity analysis for monitoring the health conditions of workers and for protecting people from atherosclerosis disease. The performance of the proposed VBE-DL system to recognize cell alterations from the AFM images is related to three different sources of uncertainty and a critical analysis of the results achieved is provided.

Combining microfluidics technology with machine learning represents an innovative approach to conduct massive quantitative cell behavior study and implement smart decision-making systems in support of clinical diagnostics. The spleen plays a key-role in rare hereditary hemolytic anemia (RHHA), being the organ responsible for the premature removal of defective red blood cells (RBCs). The goal is to adapt the physiological spleen filtering strategy for in vitro study and monitoring of blood diseases through RBCs shape analysis. Then, a microfluidic spleen-like filtering unit and video data analysis are combined for the characterization of RBCs in RHHA. A filtering unit is designed to measure deformability by maintaining fixed the RBC orientation to study its capacity of restoring the original shape after crossing microconstrictions. Two cooperative learning approaches are used for the analysis: the majority voting scheme, in which the most voted label for all the cell images is the class assigned to the entire video; and the maximum sum of scores to decide the maximally scored class to assign. The proposed platform shows the capability to discriminate healthy controls and patients with an average efficiency of 91%, but also to distinguish between RHHA subtypes, with an efficiency of 82%.

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 (SR) without requiring any training. The proposed Recursive Deep Prior Video (RDPV) 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 (TV) 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. Achieved results are compared with several state-of-the-art trained deep learning SR algorithms showing outstanding performances., Paper submitted to a peer-reviewed journal

We reconsider the rational Calogero–Moser system from the point of view of bi-Hamiltonian geometry. By using geometrical tools of the latter, we explicitly construct set(s) of spectral canonical coordinates, that is, complete sets of Darboux coordinates defined by the eigenvalues and the eigenvectors of the Lax matrix.

Pain is a health problem of vast dimension that requires prompt diagnosis. However, the evaluation criteria currently used in clinical settings are based on self-rating scales, which are subjective and prone to error. This may cause administering of improper analgesic treatments, either in dosage or in duration. For the scope of providing objective measures of pain and help devising effective therapeutic choices, we designed a personalized platform for sensing and monitoring pain. As input to the platform, visual, speech and physiological clues expressed by the patient during the pain experience are non-invasively measured, then sent to a cloud server to be integrated and analyzed. In this work, preliminary results obtained with the vision-based block of the platform are presented. Because the pain perceived by each individual is highly subjective, once the data are collected from each subject, quantitative descriptors are extracted and personalized selection of relevant features is performed to train a subject-specific regression model. Video sequences of genuine pain felt were tested and weighted accuracy values of 0.81 (0.19) and 0.84 (0.10) were obtained, respectively, for three-class and two-class scenarios, showing benefits and drawbacks of the proposed personalized platform.

Understanding the interactions between immune and cancer cells occurring within the tumor microenvironment is a prerequisite for successful and personalized anti-cancer therapies. Microfluidic devices, coupled to advanced microscopy systems and automated analytical tools, can represent an innovative approach for high-throughput investigations on immune cell-cancer interactions. In order to study such interactions and to evaluate how therapeutic agents can affect this crosstalk, we employed two ad hoc fabricated microfluidic platforms reproducing advanced 2D or 3D tumor immune microenvironments. In the first type of chip, we confronted the capacity of tumor cells embedded in Matrigel containing one drug or Matrigel containing a combination of two drugs to attract differentially immune cells, by fluorescence microscopy analyses. In the second chip, we investigated the migratory/interaction response of naïve immune cells to danger signals emanated from tumor cells treated with an immunogenic drug, by time-lapse microscopy and automated tracking analysis. We demonstrate that microfluidic platforms and their associated high-throughput computed analyses can represent versatile and smart systems to: (i) monitor and quantify the recruitment and interactions of the immune cells with cancer in a controlled environment, (ii) evaluate the immunogenic effects of anti-cancer therapeutic agents and (iii) evaluate the immunogenic efficacy of combinatorial regimens with respect to single agents.

Understanding the interactions between immune and cancer cells occurring within the tumor microenvironment is a prerequisite for successful and personalized anti-cancer therapies. Microfluidic devices, coupled to advanced microscopy systems and automated analytical tools, can represent an innovative approach for high-throughput investigations on immune cell-cancer interactions. In order to study such interactions and to evaluate how therapeutic agents can affect this crosstalk, we employed two ad hoc fabricated microfluidic platforms reproducing advanced 2D or 3D tumor immune microenvironments. In the first type of chip, we confronted the capacity of tumor cells embedded in Matrigel containing one drug or Matrigel containing a combination of two drugs to attract differentially immune cells, by fluorescence microscopy analyses. In the second chip, we investigated the migratory/interaction response of naive immune cells to danger signals emanated from tumor cells treated with an immunogenic drug, by time-lapse microscopy and automated tracking analysis. We demonstrate that microfluidic platforms and their associated high-throughput computed analyses can represent versatile and smart systems to: (i) monitor and quantify the recruitment and interactions of the immune cells with cancer in a controlled environment, (ii) evaluate the immunogenic effects of anti-cancer therapeutic agents and (iii) evaluate the immunogenic efficacy of combinatorial regimens with respect to single agents.

In the first part of this letter it will be shown that the post-Lie Magnus expansion can be interpreted as a crossed morphism between two (local) Lie group. The second part will be devoted to present two combinatorial methods, both based on special tubings on planar trees, to compute the coefficients of this remarkable formal series., Comment: 25 pages, many pictures

International audience; Time-continuous emotion estimation (e. g., arousal and valence) from spontaneous speech expressions has recently drawn increasing commercial attention. However, real-life applications of emotion recognition technology require challenging conditions, such as noise from recording devices and background environments. In this work, we introduce a novel personalized emotion prediction model validated in different noisy environments. It is performed by a three-level noise reduction algorithm: (i) data downsampling, (ii) feature synchronization, and (iii) a modified version of graph total variation. The approach has been validated on the broadly used RECOLA database with different types of noises, including convolutive and additive noise with different SNRs. The process of feature synchronization improves the concordance correlation coefficient (CCC) absolute values by 0.271 on average for arousal and 0.137 for valence. The proposed denoising approach further improves the values by 0.101 for arousal and 0.086 for valence. Finally, the proposed model considerably improves the CCC values on raw data and all types of noisy data and outperforms the standard denoising methods

Several studies demonstrate that since emotions are spontaneously manifested through different measurable quantities (e.g. vocal and facial expressions), this makes possible a sort of automatic estimation of emotion from objective measurements. However, the reliability of such estimations is strongly influenced by the availability of the different sensor modalities used to monitor the affective status of a subject, and furthermore the extraction of objective parameters is sometime thwarted in a noisy and disturbed environment. This paper introduces a personalized emotion estimation based on a heterogeneous array of physical sensors for the measurement of vocal, facial, and physiological (electro-cardiogram and electro-dermal) activities. As a proof of concept, changes in the levels of both emotion reactiveness and pleasantness are estimated under critical operative conditions. The estimator model takes advantage from the time-varying selection of the most relevant non-spurious sensors features and the adaptation of the k-nearest neighbour paradigm to the continuous identification of the most affine model templates. The model, once trained, demonstrated to autonomously embed new sensorial input and adapt to unwanted/unpredicted sensor noise or emotion alteration. The proposed approach has been successfully tested on the RECOLA database, a multi-sensorial corpus of spontaneous emotional interactions in French.

In this note we further explore the properties of universal enveloping algebras associated to a post-Lie algebra. Emphasizing the role of the Magnus expansion, we analyze the properties of group like-elements belonging to (suitable completions of) those Hopf algebras. Of particular interest is the case of post-Lie algebras defined in terms of solutions of modified classical Yang–Baxter equations. In this setting we will study factorization properties of the aforementioned group-like elements.

Localization of malignant sites of asymmetry in mammograms.Combination of Tabar masking procedures.Use of correlation-based structural similarity descriptors.Database-independent validation strategy. Background and objectivesThe analysis of patterns of asymmetry between the left and right mammograms of a patient can provide meaningful insights into the presence of an underlying tumor in its early stage. However, the identification of breast cancer by investigating bilateral asymmetry is difficult to perform due to the indistinct and borderline nature of the asymmetric signs as they appear on mammograms. MethodsIn this study, to increase the positive-predictive value of asymmetry in mammographic screening, a novel computerized approach for the automatic localization of malignant sites of asymmetry in mammograms is proposed. The sites of anatomical correspondence between the right and left regions of each radiographic projection were extracted by means of two bilateral masking procedures, inspired by radiologists criteria in interpreting mammograms and based on the use of detected landmarking structures. Relative variations of spatial patterns of intensity values and of orientations of directional components within each site were quantified by combining multidirectional Gabor filters and indices of structural similarity. The localization of the sites of malignant asymmetry was performed by coupling two quadratic discriminant analysis classifiers, one for each masking procedure, that assigned the likelihood of malignancy to each site of correspondence. ResultsThe performance of the proposed method was assessed on 94 mammographic images from two publicly available databases and containing at least one asymmetric site. Sensitivity, specificity and balanced accuracy levels of 0.83 (0.09), 0.75 (0.06), and 0.79 (0.04), respectively were obtained in the classification of malignant asymmetric sites vs benign/normal sites using cross-validation. In addition, a further blind test on a dataset of Full Field Digital Mammograms achieved levels of sensitivity, specificity, and balanced accuracy of 0.86, 0.65, and 0.75, respectively. ConclusionsThe achieved performance indicates that the proposed system is effective in localizing sites of malignant asymmetry and it is expected to improve computer-aided diagnosis of breast cancer.

The increasing interest for microfluidic devices in medicine and biology has opened the way to new time-lapse microscopy era where the amount of images and their acquisition time will become crucial. In this optic, new data analysis algorithms have to be developed in order to extract novel features of cell behavior and cell–cell interactions. In this brief article, we emphasize the potential strength of a new paradigm arising in the integration of microfluidic devices (i.e., organ on chip), time-lapse microscopy analysis, and machine learning approaches. Some snapshots of previous case studies in the context of immunotherapy are included as proof of concepts of the proposed strategies while a visionary description concludes the work foreseeing future research and applicative scenarios.

A major challenge in cancer research is the complexity of the tumor microenvironment and the necessity to take into account the host immunological setting. An innovative way to address these problems is to exploit the emerging technology of organ-on-chip to achieve co-cultures in microfluidic devises that recapitulate ex vivo the tumor ecosystem. We generated tumors-on-chip integrating four cell populations (cancer, immune, endothelial cells and fibroblasts) in a 3D collagen matrix, reconstituting HER2 breast cancer ecosystem. By time-lapse microscopy, differential live staining and a novel automated analysis method, we visualized, and quantified the complex dynamics of this ecosystem, in absence or in 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 (≳ 60 min), recapitulating an anti-tumoral ADCC (antibody-dependent cell-mediated cytotoxicity) immune response. Cancer-associated fibroblasts (CAFs) on the contrary inhibited cancer-immune interactions, antagonizing the action of the trastuzumab. These observations constitute a proof-of-concept that tumors-on-chip are novel powerful platforms that can be exploited to study ex vivo immunocompetent tumor microenvironments, to characterize ecosystem-level responses to anti-cancer drugs, and to dissect the roles of the various cellular components of the stroma.

This paper introduces mcc4mcc, the Model Checker Collection for the Model Checking Contest, a tool that wraps multiple model checking solutions, and applies the most appropriate one based on the characteristics of the model it is given. It leverages machine learning algorithms to carry out this selection, based on the results gathered from the 2017 edition of the Model Checking Contest, an annual event in which multiple tools compete to verify different properties on a large variety of models. Our approach brings two important contributions. First, our tool offers the opportunity to further investigate on the relation between model characteristics and verification techniques. Second, it lays out the groundwork for a unified way to distribute model checking software using virtual containers.

In these notes we review and further explore the Lie enveloping algebra of a post-Lie algebra. From a Hopf algebra point of view, one of the central results, which will be recalled in detail, is the existence of second Hopf algebra structure. By comparing group-like elements in suitable completions of these two Hopf algebras, we derive a particular map which we dub post-Lie Magnus expansion. These results are then considered in the case of Semenov-Tian-Shansky’s double Lie algebra, where a post-Lie algebra is defined in terms of solutions of modified classical Yang–Baxter equation. In this context, we prove a factorization theorem for group-like elements. An explicit exponential solution of the corresponding Lie bracket flow is presented, which is based on the aforementioned post-Lie Magnus expansion.

In these notes we review and further explore the Lie enveloping algebra of a post-Lie algebra. From a Hopf algebra point of view, one of the central results, which will be recalled in detail, is the existence of a second Hopf algebra structure. By comparing group-like elements in suitable completions of these two Hopf algebras, we derive a particular map which we dub post-Lie Magnus expansion. These results are then considered in the case of Semenov-Tian-Shansky's double Lie algebra, where a post-Lie algebra is defined in terms of solutions of modified classical Yang-Baxter equation. In this context, we prove a factorization theorem for group-like elements. An explicit exponential solution of the corresponding Lie bracket flow is presented, which is based on the aforementioned post-Lie Magnus expansion.

Atomic force microscopy is gaining interest as a technique to quantitatively study biological samples in native environment. However, the measuring principles behind may cause the presence of different sources of artifacts and image degradations. In this work, we present an AFM image analysis tool able to recognize morphological alterations in human leukemia cells after 3 h incubation with the antioxidant cerium oxide nanoparticles. To demonstrate the robustness of the approach to a particular artifact called contrast reversal (CR) effect, consisting in an unexpected switching between repulsive and attractive tip regime during scanning, we present a technique to artificially inject the artefact on the image and then apply the tool. Maximum area under the ROC (AUC) curve results of 0.91 (0.11) in the discrimination between exposed and unexposed cells confirm the validity of the approach and its applicability in AFM-based cell studies.

In these notes we review and further explore the Lie enveloping algebra of a post-Lie algebra. From a Hopf algebra point of view, one of the central results, which will be recalled in detail, is the existence of a second Hopf algebra structure. By comparing group-like elements in suitable completions of these two Hopf algebras, we derive a particular map which we dub post-Lie Magnus expansion. These results are then considered in the case of Semenov-Tian-Shansky's double Lie algebra, where a post-Lie algebra is defined in terms of solutions of modified classical Yang-Baxter equation. In this context, we prove a factorization theorem for group-like elements. An explicit exponential solution of the corresponding Lie bracket flow is presented, which is based on the aforementioned post-Lie Magnus expansion., Comment: 49 pages, no-figures, review article

Speech emotion recognition (SER) is a challenging framework in demanding human machine interaction systems. Standard approaches based on the categorical model of emotions reach low performance, probably due to the modelization of emotions as distinct and independent affective states. Starting from the recently investigated assumption on the dimensional circumplex model of emotions, SER systems are structured as the prediction of valence and arousal on a continuous scale in a two-dimensional domain. In this study, we propose the use of a PLS regression model, optimized according to specific features selection procedures and trained on the Italian speech corpus EMOVO, suggesting a way to automatically label the corpus in terms of arousal and valence. New speech features related to the speech amplitude modulation, caused by the slowly-varying articulatory motion, and standard features extracted from the pitch contour, have been included in the regression model. An average value for the coefficient of determination R2 of 0.72 (maximum value of 0.95 for fear and minimum of 0.60 for sadness) is obtained for the female model and a value for R2 of 0.81 (maximum value of 0.89 for anger and minimum value of 0.71 for joy) is obtained for the male model, over the seven primary emotions (including the neutral state).

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. : 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. Keywords: tumor microenvironment, organ-on-chip, tumor-on-chip, trastuzumab, HER2+ breast cancer, cancer-associated fibroblasts, live cell imaging, microfluidics, pre-clinical models, immunotherapy

Automatic detection of the nipple in mammograms is an important step in computerized systems that combine multiview information for accurate detection and diagnosis of breast cancer. Locating the nipple is a difficult task owing to variations in image quality, presence of noise, and distortion and displacement of the breast tissue due to compression. In this work, we propose a novel Hessian-based method to locate automatically the nipple in screen-film and full-field digital mammograms (FFDMs). The method includes detection of a plausible nipple/retroareolar area in a mammogram using geometrical constraints, analysis of the gradient vector field by mean and Gaussian curvature measurements, and local shape-based conditions. The proposed procedure was tested on 566 mammographic images consisting of 372 randomly selected scanned films from two public databases (mini-MIAS and DDSM), and 194 digital mammograms acquired with a GE Senographe 2000D FFDM system. A radiologist independently marked the centers of the nipples for evaluation of the results. The average error obtained was 6.7 mm (22 pixels) with reference to the center of the nipple as identified by the radiologist. Only two out of the 566 detected nipples (0.35 %) had an error larger than 50 mm. The method was also directly compared with two other techniques for the detection of the nipple. The results indicate that the proposed method outperforms other algorithms presented in the literature and can be used to identify accurately the nipple on various types of mammographic images.

Receiving Operating Curve (ROC) analysis is a powerful and statistical accepted method to assess the performance of a diagnostic test. ROC curve plots true positive rate against false positive rate, evaluated on a certain population. Instrumental and model uncertainty contributions can strongly affect the performance of the ROC analysis especially in the evaluation of performance metrics such as Area Under ROC (AUC) and Optimal Operating Points. Supplement 2 reports detailed instructions to handle and propagate uncertainty through a Multiple Input Multiple Output system, in case of correlate output variables, such as TPR and FPR. After a detailed revision of the existing literature, the present paper describes and applies a novel methodology, totally framed in the GUM and its supplements, to represent and propagate the uncertainty contributions estimated in a medical context, throughout the ROC analysis, providing new concepts such as ROC confidence region and Optimal Operating Region.

International audience; Research on automatic emotion recognition from speech has recently focused on the prediction of time-continuous dimensions (e. g., arousal and valence) of spontaneous and realistic expressions of emotion, as found in real-life interactions. However, the automatic prediction of such emotions poses several challenges, such as the subjectivity found in the definition of a gold-standard from a pool of raters and the issue of data scarcity in training models. In this work, we introduce a novel emotion recognition system, based on ensembles of single-speaker-regression-models. The estimation of emotion is provided by combining a subset of the initial pool of single-speaker-regression-models selecting those that are most concordant among them. The proposed approach allows the addition or removal of speakers from the ensemble without the necessity to rebuild the entire recognition system. The simplicity of this aggregation strategy, coupled with the flexibility assured by the modular architecture, and the promising results observed on the RECOLA database highlight the potential implications of the proposed method in a real-life scenario and in particular in web-based applications.

PurposeThe purpose of this paper is to prove the validity of the front‐tracking variant of the lattice Boltzmann method (LBM) to simulate free surface hydraulic flows (i.e. dam break flows).Design/methodology/approachIn this paper, an algorithm for free surface simulations with the LBM method is presented. The method is chosen for its computational efficiency and ability to deal with complex geometries. The LBM is combined to a surface‐tracking technique applied to a fixed Eulerian mesh in order to simulate free surface flows.FindingsThe numerical method is then validated against two typical cases of environmental‐hydraulic interest (i.e. dam break) by comparing LBM results with experimental data available in literature. The results show that the model is able to reproduce the observed water levels and the wave fronts with reasonable accuracy in the whole period of the transient simulations, thus highlighting that the present method may be a promising tool for practical dam break analyses.Originality/valueEven if the main philosophy of the proposed method is equal to the volume of fluid technique usually coupled to Navier‐Stokes models, no additional differential equation is needed to determine the relative volume fraction of the two phases, or phase fraction, in each computational cell, as the free‐surface tracking is automatically performed. This results in a method very simple to be coded with high computational efficiency. The results presented in this paper are the first, to the best of the authors' knowledge, in the field of hydraulic engineering.

At present, mammography is the most effective examination for an early diagnosis of breast cancer. Nevertheless, the detection of cancer signs in mammograms is a difficult procedure owing to the great number of non-pathological structures which are also present in the image. Recent statistics show that in current breast cancer screenings 10%-25% of the tumors are missed by the radiologists. For this reason, a lot of research is currently being done to develop systems for Computer Aided Detection (CADe). Probably, some causes of the false-negative screening examinations are that tumoral masses have varying dimension and irregular shape, their borders are often ill-defined and their contrast is very low, thus making difficult the discrimination from parenchymal structures. Therefore, in a CADe system a preliminary segmentation procedure has to be implemented in order to separate the mass from the background tissue. In this way, various characteristics of the segmented mass can be evaluated and used in a classification step to discriminate benign and malignant cases. In this paper, we describe an effective algorithm for massive lesions segmentation based on a region-growing technique and we provide full details the performance evaluation procedure used in this specific context.