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Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, we formulated a novel paradigm in which we considered the individual cells as a sort of sensitive element of a sensor, which exploits the camera as a transducer returning the movement of the cell as an output signal. In this way, cell movement allows us to retrieve information about the chemical composition of the close environment. To optimally exploit this information, in this work, we introduce a new setting, in which a cell trajectory is divided into sub-tracks, each one characterized by a specific motion kind. Hence, we considered all the sub-tracks of the single-cell trajectory as the signals of a virtual array of cell motility-based sensors. The kinematics of each sub-track is quantified and used for a classification task. To investigate the potential of the proposed approach, we have compared the achieved performances with those obtained by using a single-trajectory paradigm with the scope to evaluate the chemotherapy treatment effects on prostate cancer cells. Novel pattern recognition algorithms have been applied to the descriptors extracted at a sub-track level by implementing features, as well as samples selection (a good teacher learning approach) for model construction. The experimental results have put in evidence that the performances are higher when a further cluster majority role has been considered, by emulating a sort of sensor fusion procedure. All of these results highlighted the high strength of the proposed approach, and straightforwardly prefigure its use in lab-on-chip or organ-on-chip applications, where the cell motility analysis can be massively applied using time-lapse microscopy images.

Aflatoxins are fungal metabolites extensively produced by many different fungal species that may contaminate a wide range of agricultural food products. They have been studied extensively because of being associated with various chronic and acute diseases, especially immunosuppression and cancer, and their presence in food is strictly monitored and regulated worldwide. Aflatoxin detection and measurement relies mainly on chemical methods usually based on chromatography approaches, and recently developed immunochemical based assays that have advantages but also limitations, since these are expensive and destructive techniques. Nondestructive, optical approaches are recently being developed to assess presence of contamination in a cost and time effective way, maintaining acceptable accuracy and reproducibility. In this paper are presented the results obtained with a simple portable device for nondestructive detection of aflatoxins in almonds. The presented approach is based on the analysis of fluorescence spectra of slurried almonds under 375 nm wavelength excitation. Experiments were conducted with almonds contaminated in the range of 2.7–320.2 ng/g total aflatoxins B (AFB1 + AFB2) as determined by High Performance Liquid Chromatography with Fluorescence Detection (HPLC/FLD). After applying pre-processing steps, spectral analysis was carried out using a binary classification model based on Support Vector Machine (SVM) algorithm. A majority vote procedure was then performed on the classification results. In this way we could achieve, as best result, a classification accuracy of 94% (and false negative rate 5%) with a threshold set at 6.4 ng/g. These results illustrate the feasibility of such approach in the great challenge of aflatoxin detection for food and feed safety.

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

Modern optical sensors coupled with time-lapse microscopy devices and dedicated software tools allow the miniaturization of laboratories for biological experiments leading to the Organ-On-Chip (OoC) framework. OoCs allow performing massive measurements on a large number of cells under the assumption of reproducibility conditions, permitting to investigate the cell dynamics in terms of motility and shape changes over time. In this work, we present the OoC platform used in a preliminary study of the Rare Haemolytic Anaemia (RHA) disease, a group of rare diseases characterized by haemolysis, which is the premature loss of red blood cells (RBCs). Preliminary results demonstrate the effectiveness of shape measurement for the diagnosis of RHA.

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 performance of an Unsupervised Online feature Selection (UOS) algorithm was investigated for the selection of training features of multispectral images acquired from a dairy product (vanilla cream) stored under isothermal conditions. The selected features were further used as input in a support vector machine (SVM) model with linear kernel for the determination of the microbiological quality of vanilla cream. Model training (n = 65) was based on two batches of cream samples provided directly by the manufacturer and stored at different isothermal conditions (4, 8, 12, and 15 &deg, C), whereas model testing (n = 132) and validation (n = 48) were based on real life conditions by analyzing samples from different retail outlets as well as expired samples from the market. Qualitative analysis was performed for the discrimination of cream samples in two microbiological quality classes based on the values of total viable counts [TVC &le, 2.0 log CFU/g (fresh samples) and TVC &ge, 6.0 log CFU/g (spoiled samples)]. Results exhibited good performance with an overall accuracy of classification for the two classes of 91.7% for model validation. Further on, the model was extended to include the samples in the TVC range 2&ndash, 6 log CFU/g, using 1 log step to define the microbiological quality of classes in order to assess the potential of the model to estimate increasing microbial populations. Results demonstrated that high rates of correct classification could be obtained in the range of 2&ndash, 5 log CFU/g, whereas the percentage of erroneous classification increased in the TVC class (5,6) that was close to the spoilage level of the product. Overall, the results of this study demonstrated that the UOS algorithm in tandem with spectral data acquired from multispectral imaging could be a promising method for real-time assessment of the microbiological quality of vanilla cream samples.

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.

Background and objective: Full-field digital mammography (FFDM) is the predominant breast cancer screening exam used. However, with the emergence of digital breast tomosynthesis (DBT) the radiologists could improve early recognition of breast cancer signs. In this scenario, the detection of architectural distortion (AD) is still a challenging task. ADs are very subtle contraction of the breast parenchyma that could represent the earliest manifestation of cancer, assessing at present 50% of missed cases. Methods: This paper proposes a new paradigm to detect AD in DBT exams by a cross-cutting approach exploiting the 3-dimensionality of the imaging modality. After locating AD candidates in each DBT slice, the suspicious spots are tracked in cross-slice direction and then characterized in terms of neighboring texture. In this approach, which mimics radiologist's scrolling down over zoomed slices, we reduce the amount of uninformative signs collected in DBT exams by preserving the large variability of AD appearance. Results: Using 37 sets of DBT slices containing at least one AD locus indicated by a radiologist, the proposed methodology reaches an AUC of 0.84, with only one false negative exam at sensitivity of 0.9. Conclusions: The results show that the proposed algorithm can be a promising tool for the automatic detection of AD locii. Future work will address the extension of the dataset of DBT slices as well the improvement of algorithm performance toward the application in the clinical practice.

Objective: The ability of cells to collectively move is essential in various biological contexts including cancer metastasis. In this paper, we propose an automatic video analysis tool to correlate the cell movement inhibition with replication block induced by dose-dependent chemotherapy administration. Methods: The novel approach combines individual and collective cell kinematic analysis performed over time-lapse microscopy video frames. Cells are first localized and tracked, and then kinematic descriptors are extracted for each track. Selective track identification is performed assuming diversified cell roles within the same cluster (spontaneously forming groups of cells), and finally individual results are grouped exploiting consensus of coordinated motility within cell clusters. Results: Recognition performance of three different experimental conditions (no drug, 0.5–5 μ M merged in the same condition, and 50 μ M) reached an average accuracy value of 88% over 958 different tracks collected in 36 clusters of diverse dimensions in eight independent experiments. Conclusion: An extensive application of this methodology could give a different point of view of the cancer mechanisms.

Aflatoxin detection currently relies mainly on chemical methods usually based on chromatography approaches, and recently developed immunochemical based assays that are fairly accurate, however, they are time-consuming, expensive and destructive. Non-destructive, optical approaches are recently being developed in order to assess the presence of contamination in a cost and time-effective way, maintaining high levels of accuracy and reproducibility, but are usually based on the benchtop and expensive instruments. Here we will present the evolution of results of the analysis of fluorescence spectra of contaminated almond samples during the development of an optical multi-sensor device in the framework of PhasmaFOOD project. The aim of the project was to develop a low cost and portable instrument comprising multispectral and imaging capabilities, conjugated with a cloud reference database and analysis toolbox for food features analysis. One of the use cases of the project is the fast, reliable and non-destructive detection of mycotoxin (in particular aflatoxin) contamination in food products. For this use case, we used in particular fluorescence spectroscopy and different approaches to data analysis. After the first feasibility tests in the range of mg/g contamination range with a simple and effective analysis that led to highly reliable results, the work was focused on the detection limits with samples (almond) in the range of 0-291 ng/g acquired with a simple portable device and excitation light at 365 nm wavelength. An ad hoc processing strategy based on a feature selection steps coupled with a nonlinear classifier has been developed and test with two different datasets collected one month from the other another. The system performances have been evaluated training the classification model with one dataset and testing its with the other. The results have shown an accuracy higher than 80% with a threshold lower than 10ppb as contamination level.

In recent years, applications like Apple’s Siri or Microsoft’s Cortana have created the illusion that one can actually “chat” with a machine. However, a perfectly natural human-machine interaction is far from real as none of these tools can empathize. This issue has raised an increasing interest in speech emotion recognition systems, as the possibility to detect the emotional state of the speaker. This possibility seems relevant to a broad number of domains, ranging from man-machine interfaces to those of diagnostics. With this in mind, in the present work, we explored the possibility of applying a precision approach to the development of a statistical learning algorithm aimed at classifying samples of speech produced by children with developmental disorders(DD) and typically developing(TD) children. Under the assumption that acoustic features of vocal production could not be efficiently used as a direct marker of DD, we propose to apply the Emotional Modulation function(EMF) concept, rather than running analyses on acoustic features per se to identify the different classes. The novel paradigm was applied to the French Child Pathological & Emotional Speech Database obtaining a final accuracy of 0.79, with maximum performance reached in recognizing language impairment (0.92) and autism disorder (0.82).

In this paper we discuss the applicability of numerical descriptors and statistical physics concepts to characterize complex biological systems observed at microscopic level through organ on chip approach. To this end, we employ data collected on a microfluidic platform in which leukocytes can move through suitably built channels toward their target. Leukocyte behavior is recorded by standard time lapse imaging. In particular, we analyze three groups of human peripheral blood mononuclear cells (PBMC): heterozygous mutants (in which only one copy of the FPR1 gene is normal), homozygous mutants (in which both alleles encoding FPR1 are loss-of-function variants) and cells from ‘wild type’ donors (with normal expression of FPR1). We characterize the migration of these cells providing a quantitative confirmation of the essential role of FPR1 in cancer chemotherapy response. Indeed wild type PBMC perform biased random walks toward chemotherapy-treated cancer cells establishing persistent interactions with them. Conversely, heterozygous mutants present a weaker bias in their motion and homozygous mutants perform rather uncorrelated random walks, both failing to engage with their targets. We next focus on wild type cells and study the interactions of leukocytes with cancerous cells developing a novel heuristic procedure, inspired by Lyapunov stability in dynamical systems.

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.

This paper proposes a method to reduce the number of false-positives (FP) in a computer-aided detection (CAD) scheme for automated detection of architectural distortion (AD) in digital mammography. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automated detection of AD in breast images. The usual approach is automatically detect possible sites of AD in a mammographic image (segmentation step) and then use a classifier to eliminate the false-positives and identify the suspicious regions (classification step). This paper focus on the optimization of the segmentation step to reduce the number of FPs that is used as input to the classifier. The proposal is to use statistical measurements to score the segmented regions and then apply a threshold to select a small quantity of regions that should be submitted to the classification step, improving the detection performance of a CAD scheme. We evaluated 12 image features to score and select suspicious regions of 74 clinical Full-Field Digital Mammography (FFDM). All images in this dataset contained at least one region with AD previously marked by an expert radiologist. The results showed that the proposed method can reduce the false positives of the segmentation step of the CAD scheme from 43.4 false positives (FP) per image to 34.5 FP per image, without increasing the number of false negatives.

Engineered nanomaterials play an even more relevant role in nanotechnology advances. However, care must be taken due to their suspected detrimental effects on human cells. Such alterations can be monitored through Atomic Force Microscopy (AFM) equipment and image digitalization. With the purpose to depict a metrological compliant scenario, a novel vision-based evaluation system is proposed with an evaluation unit based on a deep learning architecture. Inspired by the recent trends in trying to extend the standard concept of quantities to nominal properties and measurement to evaluation, we proposed here a platform for the evaluation of morphological alterations in AFM images of human cells exposed to different concentrations of carbon nanotubes. Results reveal the feasibility to automatically investigate such alterations with the aim to improve occupational medicine protocols and cells cataloguing procedures.

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.

Nowadays, visual-based measurement (VBM) systems offer new possibilities of investigation for researchers and clinicians, using noninvasive–nondestructive approaches. In this paper, we present an atomic force microscopy (AFM)-based VBM for the study of cell-cerium oxide nanoparticle interactions. To provide a metrological characterization of the results obtained and with the aim to compare different strategies, we modeled four artifacts effects occurring in AFM acquisition within the random process theory and implemented a Monte Carlo simulation to repeatedly inject such variability in the original image. Empirical cumulative distribution function, confidence intervals, and average representative values, following Supplement 1 guidelines, were estimated for the final scores assigned by the operations unit to each cell. Area under the roc curve and accuracy of classification for two different machine learning approaches were compared in a metrological compliant methodology. Results clearly demonstrate the robustness of the presented VBM system and quantify the uncertainty expected for such kind of results.