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Spiking neural P systems are parallel and distributed computation devices which are inspired by the neuro-physiological behavior of biological neurons. In this paper we will present, with a tutorial approach, the main underlying ideas and the most interesting variants that have been proposed in the literature. In particular, we will discuss the results on the computational power of these models, both in terms of Turing completeness and of efficiency in solving hard problems, under different assumptions for information encoding, form and application of rules, and bounds on the main parameters defining the systems.

The problem of matching a query string to a directed graph, whose vertices are labeled by strings, has application in different fields. In this paper we present results on the complexity of the approximate matching problem, where edit operations are symbol substitutions and are allowed only to the graph labels or both to the graph labels and the query string. We show that deciding if there exists a path in a graph that represents a query string with edit operations to vertex labels is an NP-complete problem and it is fixed-parameter tractable when parameterized by the length of the input string. We show that the variants of approximate string matching we consider are not fixed-parameter tractable, when the parameter is (1) the length of the query string and (2) the number of edit operations. Moreover, we provide inapproximability results for these variants of the of approximate string matching problem.

During the COVID-19 outbreak, the risk of depression has increased for pregnant women and especially for first-time mothers-to-be. Pre-COVID-19 literature showed that depression is negatively linked to mental representations during pregnancy. This pilot study explored the difference in depressive symptoms and maternal representations style in primiparous and multiparous pregnant women during the outbreak of COVID-19 (2020-2021). 25 women (14 primiparous, 11 multiparous) were recruited in their last trimester of pregnancy. Participants responded to the Edinburgh Postnatal Depression Scale (EPDS) and Interview for Maternal Representations during Pregnancy (IntervistasulleRappresentazioniMaterne in Gravidanza- IRMAG). Results showed that primiparous women presented higher depressive symptoms than multiparous ones. Moreover, primiparous women reported lower richness of perception (p = 0.008), openness to change (p = 0.035) and dominance of fantasies (p = 0.000) in maternal representation and, globally, more restricted representations (71.4%) than multiparous ones (18.2%) (p = 0.020). Mental representations were related to the level of depression, with integrated representations being associated with lower depression than restricted and ambivalent ones (p = 0.001). A preventive intervention to support primiparous pregnant women during future pandemics would be necessary in particular to avoid negative repercussions also in the post-partum experience.

Background: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods: The European “ITFoC (Information Technology for the Future Of Cancer)” consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the “ITFoC Challenge”. This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

The first definition of space complexity for P systems was based on a hypothetical real implementation by means of biochemical materials, and thus it assumes that every single object or membrane requires some constant physical space. This is equivalent to using a unary encoding to represent multiplicities for each object and membrane. A different approach can also be considered, having in mind an implementation of P systems in silico; in this case, the multiplicity of each object in each membrane can be stored using binary numbers, thus reducing the amount of needed space. In this paper, we give a formal definition for this alternative space complexity measure, we define the corresponding complexity classes and we compare such classes both with standard space complexity classes and with complexity classes defined in the framework of P systems considering the original definition of space.

In the latest period as the pandemic has spread worldwide, the need to provide students and learners with effective and personalized didactical resources in order to maintain and even empower the potential level of training has significantly increased: specifically, the major necessity is related to the high potentialities in adopting educational models and methods based on artificial intelligence to reduce the cognitive effort made by teachers when searching, interacting with peers, creating and organizing educational resources. Therefore, nowadays there is a greater diffusion of Artificial Intelligence (AI) techniques and methods for educational purposed and platforms. Every training institution, school and organization definitively requires both advanced, smart and user-friendly technological tools to foster education and training process in the present historical situation. AI algorithms have proved to be crucial in different and heterogeneous research domains: among these methods, recommendation systems have been studied and applied in the e-learning domain. Nevertheless, AI and deep learning (DL) models suffer from trust issues, which poses specific problems which are different than in other kinds of technologies. Thus, if similar concepts are not addressed, there is the concrete to obtain models that perform well but exhibit inconsistent behaviors causing confusion during the interaction process between user and system: such confusion can even be a reason for abandonment of AI technology. Among these intelligent algorithms, content filtering mechanisms such as recommendation systems allow to provide explicit and interactive mechanisms to foster effective cooperation between the users and the system. In this paper, we describe the current human interaction level within the recommendation system (RS) for filtering educational resources of the educational platform ”WhoTeach”, which aims to create and personalize educational courses. In particular, we report the

Image texture extraction and analysis are fundamental steps in computer vision. In particular, considering the biomedical field, quantitative imaging methods are increasingly gaining importance because they convey scientifically and clinically relevant information for prediction, prognosis, and treatment response assessment. In this context, radiomic approaches are fostering large-scale studies that can have a significant impact in the clinical practice. In this work, we present a novel method, called CHASM (Cuda, HAralick & SoM), which is accelerated on the graphics processing unit (GPU) for quantitative imaging analyses based on Haralick features and on the self-organizing map (SOM). The Haralick features extraction step relies upon the gray-level co-occurrence matrix, which is computationally burdensome on medical images characterized by a high bit depth. The downstream analyses exploit the SOM with the goal of identifying the underlying clusters of pixels in an unsupervised manner. CHASM is conceived to leverage the parallel computation capabilities of modern GPUs. Analyzing ovarian cancer computed tomography images, CHASM achieved up to ∼ 19.5 × and ∼ 37 × speed-up factors for the Haralick feature extraction and for the SOM execution, respectively, compared to the corresponding C++ coded sequential versions. Such computational results point out the potential of GPUs in the clinical research.