MOTIVATION: The rise of transformer-based architectures has dramatically improved our ability to analyze natural language. However, the power and flexibility of these general-purpose models come at the cost of highly complex model architectures with billions of parameters that are not always needed. RESULTS: In this work, we present CSpace: a concise word embedding of biomedical concepts that outperforms all alternatives in terms of out-of-vocabulary ratio and semantic textual similarity task, and has comparable performance with respect to transformer-based alternatives in the sentence similarity task. This ability can serve as the foundation for semantic search by enabling efficient retrieval of conceptually related terms. Additionally, CSpace incorporates ontological identifiers (MeSH, NCBI gene and taxonomy IDs), enabling computationally efficient disease, gene or condition relatedness measurement, potentially unlocking previously unknown disease-condition associations. AVAILABILITY AND IMPLEMENTATION: Full and compressed models are available on Zenodo at https://doi.org/10.5281/zenodo.14781672, while training code, examples, interactive visualizations and experiments are available at https://doi.org/10.5281/zenodo.15125706 and on the GitHub repository.

In this paper, a logical-based mathematical model of the cellular pathways involved in the COVID-19 infection has been developed to study various drug treatments (single or in combination), in different illness scenarios, providing insights into their mechanisms of action. Drug simulations suggest that the effects of single drugs are limited, or depending on the scenario counterproductive, whereas better results appear combining different treatments. Specifically, the combination of the anti-inflammatory Baricitinib and the anti-viral Remdesivir showed significant benefits while a stronger efficacy emerged from the triple combination of Baricitinib, Remdesivir, and the corticosteroid Dexamethasone. Together with a sensitivity analysis, we performed an analysis of the mechanisms of the drugs to reveal their impact on molecular pathways.

MOTIVATION: Precision medicine is a promising field that proposes, in contrast to a one-size-fits-all approach, the tailoring of medical decisions, treatments or products. In this context, it is crucial to introduce innovative methods to stratify a population of patients on the basis of an accurate system-level knowledge of the disease. This is particularly important in very challenging conditions, where the use of standard statistical methods can be prevented by poor data availability or by the need of oversimplifying the processes regulating a complex disease. RESULTS: We define an innovative method for phenotype classification that combines experimental data and a mathematical description of the disease biology. The methodology exploits the mathematical model for inferring additional subject features relevant for the classification. Finally, the algorithm identifies the optimal number of clusters and classifies the samples on the basis of a subset of the features estimated during the model fit. We tested the algorithm in two test cases: an in silico case in the context of dyslipidemia, a complex disease for which a large population of patients has been generated, and a clinical test case, in the context of a lysosomal rare disorder, for which the amount of available data was limited. In both the scenarios, our methodology proved to be accurate and robust, and allowed the inference of an additional phenotype division that the experimental data did not show. AVAILABILITY AND IMPLEMENTATION: The code to reproduce the in silico results has been implemented in MATLAB v.2017b and it is available in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

With the recent rising application of mathematical models in the field of computational systems biology, the interest in sensitivity analysis methods had increased. The stochastic approach, based on chemical master equations, and the deterministic approach, based on ordinary differential equations (ODEs), are the two main approaches for analyzing mathematical models of biochemical systems. In this work, the performance of these approaches to compute sensitivity coefficients is explored in situations where stochastic and deterministic simulation can potentially provide different results (systems with unstable steady states, oscillators with population extinction and bistable systems). We consider two methods in the deterministic approach, namely the direct differential method and the finite difference method, and five methods in the stochastic approach, namely the Girsanov transformation, the independent random number method, the common random number method, the coupled finite difference method and the rejection-based finite difference method. The reviewed methods are compared in terms of sensitivity values and computational time to identify differences in outcome that can highlight conditions in which one approach performs better than the other.

With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these factors is explored when solving the problem of sample classification, by comparing the performances of five unsupervised algorithms: Multiple Canonical Correlation Analysis, Multiple Co-Inertia Analysis, Multiple Factor Analysis, Joint and Individual Variation Explained and Similarity Network Fusion. These methods were applied to three real data sets taken from literature and several ad hoc simulated scenarios to discuss classification performance in different conditions of noise and signal strength across the data types. The impact of experimental design, feature selection and parameter training has been also evaluated to unravel important conditions that can affect the accuracy of the result.

In this work, we propose a novel self-actuated analog front-end (AFE) for resonating sensors, realized in pseudo floating gate technology. The core of the analog front-end is based on a re-configurable circuit, which works as a SRAM cell when the resonating sensor must be actuated and behaves as a pseudo floating gate amplifier (PFGA) when the sensor response must be read-out. The timing for the actuation/readout cycle is provided by an inverter based control system, which is connected with the AFE in a positive feedback loop. The use of solely inverter logic gates to implement the whole system simplifies the design of the circuit, minimizes the occupied area, and makes possible to use the proposed system in low voltage applications (VDD<; 3.3V). The operation of the proposed circuit has been verified by simulations in AMS-350nm CMOS technology with a power supply of 3.3V. At the end of this paper, the effects of process, temperature and power supply (PVT) variations have been investigated, and a few solutions to compensate these side effects have been proposed. Finally, the average power consumption of the circuit has been measured to be 445μW, which is in the same order of other front-ends described in literature.

In this paper, we present a low power bump circuit for finding similarity between two voltages. The circuit is based on a voltage correlator that in combination with a differential pair achieves the low power behavior. The circuit has low power consumption and low sensitivity when the different between the two input is large, but as they approach each other the sensitivity increases. The proposed circuit is very simple in design and can have more than 4 orders of magnitude less power consumption than the normal bump circuit.

The announced availability of new high-performance head-mounted displays is exciting the imagination of the consumer market and suggesting that we may soon have more immersive game playing opportunities. Leveraging on the commercialization of such devices, the expectation to enjoy a new generation of virtual reality-based games relies also on the development of game interfaces suitable to allow players to enjoy an adequate sense of presence and immersion, while playing. This paper shows, with results from field trials with the Oculus Rift head mounted display, that a diegetic game interface may help to enhance the feeling of “being in the flow of the game”, even when it is used to convey to players contextual gaming information (e.g., a game configuration menu). To this aim, a simple diegetic interface has been developed based on the design expedient of incorporating into the virtual game space some virtual devices (such as smart watches and tablets) through which contextual information is displayed to players.

Cardiopulmonary resuscitation (CPR) is a first-aid key survival technique used to stimulate breathing and keep blood flowing to the heart. Its effective administration can significantly increase the chances of survival in victims of cardiac arrest. In this paper, we propose a markerless system for quality CPR training based on RGB-D (RGB + Depth) sensors, called RELIVE. Then, we report the results of a series of experimental tests conducted to evaluate RELIVE tracking performance. The proposed system is able to accurately track the 3-D position of the hands performing CPR by means of RGB-D sensors to estimate the chest compression rate and depth, providing a real-time visual/audio feedback about the rescuer's performance. Finally, the system usability has been assessed by both healthcare professionals and lay people.

UNLABELLED: MpTheory Java library is an open-source project collecting a set of objects and algorithms for modeling observed dynamics by means of the Metabolic P (MP) theory, that is, a mathematical theory introduced in 2004 for modeling biological dynamics. By means of the library, it is possible to model biological systems both at continuous and at discrete time. Moreover, the library comprises a set of regression algorithms for inferring MP models starting from time series of observations. To enhance the modeling experience, beside a pure Java usage, the library can be directly used within the most popular computing environments, such as MATLAB, GNU Octave, Mathematica and R. AVAILABILITY AND IMPLEMENTATION: The library is open-source and licensed under the GNU Lesser General Public License (LGPL) Version 3.0. Source code, binaries and complete documentation are available at http://mptheory.scienze.univr.it. CONTACT: [email protected], [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This paper shows the possibility of using Metabolic P systems (MP systems) for chaotic system identification-reconstruction and it compares presented results with previous ones obtained by evolutionary algorithms. An important potentiality of MP theory is given by its powerful computational chaos generation that can be also used as an internal module of evolutionary algorithms by increasing their ability in specific cases of their application. Reported numerical experiments are discussed at the end.

MOTIVATION: Oncogenes are known drivers of cancer phenotypes and targets of molecular therapies; however, the complex and diverse signaling mechanisms regulated by oncogenes and potential routes to targeted therapy resistance remain to be fully understood. To this end, we present an approach to infer regulatory mechanisms downstream of the HER2 driver oncogene in SUM-225 metastatic breast cancer cells from dynamic gene expression patterns using a succession of analytical techniques, including a novel MP grammars method to mathematically model putative regulatory interactions among sets of clustered genes. RESULTS: Our method highlighted regulatory interactions previously identified in the cell line and a novel finding that the HER2 oncogene, as opposed to the proto-oncogene, upregulates expression of the E2F2 transcription factor. By targeted gene knockdown we show the significance of this, demonstrating that cancer cell-matrix adhesion and outgrowth were markedly inhibited when E2F2 levels were reduced. Thus, validating in this context that upregulation of E2F2 represents a key intermediate event in a HER2 oncogene-directed gene expression-based signaling circuit. This work demonstrates how predictive modeling of longitudinal gene expression data combined with multiple systems-level analyses can be used to accurately predict downstream signaling pathways. Here, our integrated method was applied to reveal insights as to how the HER2 oncogene drives a specific cancer cell phenotype, but it is adaptable to investigate other oncogenes and model systems. AVAILABILITY AND IMPLEMENTATION: Accessibility of various tools is listed in methods; the Log-Gain Stoichiometric Stepwise algorithm is accessible at http://www.cbmc.it/software/Software.php.

This paper addresses the person following problem for nonholonomic wheeled robots. Because of the robot's nonholonomy and the difficulty to estimate the person orientation, classical control laws used to address this problem induce strong limitations on the desired robot location with respect to the person. We propose a new nonlinear control law that allows for much more versatility in this following application. Simulation and experimental results performed in real scenarios verify the effectiveness of the proposed approach.

Multi-robot patrolling is a fundamental functionality for multi-robot surveillance and environmental monitoring and has been longly investigated. However, benchmarks for multi-robot patrolling, the realization of realistic simulators and testing on real robotic platforms are still very limited. In this paper we discuss the application of state-of-the-art patrolling strategies in realistic applications, showing that it is important to take into account: perception needs of the robots, uncertainty on action execution, characteristics of the environment, closed-loop coordinated behaviors, and realistic simulation environments.

We propose an adaptive tracking system for assisted living that integrates user information about emergency events. Information fusion between user data and visual data is performed in order to estimate and assess the situation at hand. The system is able to dynamically switch between different segmentation and tracking algorithms improving its performance, as shown by the proposed examples.

Metabolic P systems (MP systems) are a special class of P systems introduced for expressing biological metabolic phenomena. The graphical formalism of MP graphs represents, in a simple and intuitive way, the structure of these systems. However, there are some cases in which they would be better specified by semi-structured textual documents, especially for information exchanging between different computational tools elaborating on different biological aspects. The aim of this paper is to define such a way of exportation from MP graphs to XML documents. It turns out that all properties which guarantee the correctness of MP graphs can be formally described by means of logical formulae on trees, and completely expressed as XML constraints in XSD (XML Schema Definition), a W3C standard for XML validation.

Metabolic P systems, shortly MP systems, are a special class of P systems, introduced for expressing biological metabolism. Their dynamics is computed by metabolic algorithms which transform populations of objects according to a mass partition principle, based on suitable generalizations of chemical laws. The basic principles of MP systems are discussed and Psim, a simulation tool we developed in this context to discretely compute systems dynamics, is highlighted in its basic features. A concrete example is reported as well including a real simulation experiment by means of Psim.