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Author Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Salvo, I.; Sinisi, S.; Tronci, E.; Ehrig, R.; Röblitz, S.; Leeners, B.
Title Computing Personalised Treatments through In Silico Clinical Trials. A Case Study on Downregulation in Assisted Reproduction Type Conference Article
Year 2018 Publication 25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018) Abbreviated Journal
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Call Number MCLab @ davi @ Serial 175
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Author Tortora, L.; Meynen, G.; Bijlsma, J.; Tronci, E.; Ferracuti, S.
Title Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective Type Journal Article
Year 2020 Publication Frontiers in Psychology Abbreviated Journal
Volume 11 Issue Pages 220
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Abstract Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as ÃâA.I. neuroprediction,Ãâ and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed.
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ISSN 1664-1078 ISBN Medium
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Call Number MCLab @ davi @ Neuroprediction-2020 Serial 180
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Author Pappagallo, A.; Massini, A.; Tronci, E.
Title Monte Carlo Based Statistical Model Checking of Cyber-Physical Systems: A Review Type Journal Article
Year 2020 Publication Information Abbreviated Journal
Volume 11 Issue 558 Pages
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Call Number MCLab @ davi @ Serial 181
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Author Sinisi, S.; Alimguzhin, V.; Mancini, T.; Tronci, E.; Leeners, B.
Title Complete populations of virtual patients for in silico clinical trials Type Journal Article
Year 2021 Publication Bioinformatics Abbreviated Journal
Volume Issue Pages 1-8
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Abstract Model-based approaches to safety and efficacy assessment of pharmacological drugs, treatment strategies, or medical devices (In Silico Clinical Trial, ISCT) aim to decrease time and cost for the needed experimentations, reduce animal and human testing, and enable precision medicine. Unfortunately, in presence of non-identifiable models (e.g., reaction networks), parameter estimation is not enough to generate complete populations of Virtual Patient (VPs), i.e., populations guaranteed to show the entire spectrum of model behaviours (phenotypes), thus ensuring representativeness of the trial.We present methods and software based on global search driven by statistical model checking that, starting from a (non-identifiable) quantitative model of the human physiology (plus drugs PK/PD) and suitable biological and medical knowledge elicited from experts, compute a population of VPs whose behaviours are representative of the whole spectrum of phenotypes entailed by the model (completeness) and pairwise distinguishable according to user-provided criteria. This enables full granularity control on the size of the population to employ in an ISCT, guaranteeing representativeness while avoiding over-representation of behaviours.We proved the effectiveness of our algorithm on a non-identifiable ODE-based model of the female Hypothalamic-Pituitary-Gonadal axis, by generating a population of 4 830 264 VPs stratified into 7 levels (at different granularity of behaviours), and assessed its representativeness against 86 retrospective health records from Pfizer, Hannover Medical School and University Hospital of Lausanne. The datasets are respectively covered by our VPs within Average Normalised Mean Absolute Error of 15%, 20%, and 35% (90% of the latter dataset is covered within 20% error).
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ISSN 1367-4803 ISBN Medium
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Call Number MCLab @ davi @ ref10.1093/bioinformatics/btaa1026 Serial 182
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Author Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Tronci, E.
Title On Checking Equivalence of Simulation Scripts Type Journal Article
Year 2021 Publication Journal of Logical and Algebraic Methods in Programming Abbreviated Journal
Volume Issue Pages 100640
Keywords Formal verification, Simulation based formal verification, Formal Verification of cyber-physical systems, System-level formal verification
Abstract To support Model Based Design of Cyber-Physical Systems (CPSs) many simulation based approaches to System Level Formal Verification (SLFV) have been devised. Basically, these are Bounded Model Checking approaches (since simulation horizon is of course bounded) relying on simulators to compute the system dynamics and thereby verify the given system properties. The main obstacle to simulation based SLFV is the large number of simulation scenarios to be considered and thus the huge amount of simulation time needed to complete the verification task. To save on computation time, simulation based SLFV approaches exploit the capability of simulators to save and restore simulation states. Essentially, such a time saving is obtained by optimising the simulation script defining the simulation activity needed to carry out the verification task. Although such approaches aim to (bounded) formal verification, as a matter of fact, the proof of correctness of the methods to optimise simulation scripts basically relies on an intuitive semantics for simulation scripting languages. This hampers the possibility of formally showing that the optimisations introduced to speed up the simulation activity do not actually omit checking of relevant behaviours for the system under verification. The aim of this paper is to fill the above gap by presenting an operational semantics for simulation scripting languages and by proving soundness and completeness properties for it. This, in turn, enables formal proofs of equivalence between unoptimised and optimised simulation scripts.
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ISSN 2352-2208 ISBN Medium
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Call Number MCLab @ davi @ Mancini2021100640 Serial 183
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Author Leeners, B.; Krueger, T.; Geraedts, K.; Tronci, E.; Mancini, T.; Ille, F.; Egli, M.; Roeblitz, S.; Wunder, D.; Saleh, L.; Schippert, C.; Hengartner, M.P.
Title Cognitive function in association with high estradiol levels resulting from fertility treatment Type Journal Article
Year 2021 Publication Hormones and Behavior Abbreviated Journal
Volume 130 Issue Pages 104951
Keywords Cognition, Estrogen, Estradiol, Fertility treatment, Attention, Cognitive bias
Abstract The putative association between hormones and cognitive performance is controversial. While there is evidence that estradiol plays a neuroprotective role, hormone treatment has not been shown to improve cognitive performance. Current research is flawed by the evaluation of combined hormonal effects throughout the menstrual cycle or in the menopausal transition. The stimulation phase of a fertility treatment offers a unique model to study the effect of estradiol on cognitive function. This quasi-experimental observational study is based on data from 44 women receiving IVF in Zurich, Switzerland. We assessed visuospatial working memory, attention, cognitive bias, and hormone levels at the beginning and at the end of the stimulation phase of ovarian superstimulation as part of a fertility treatment. In addition to inter-individual differences, we examined intra-individual change over time (within-subject effects). The substantial increases in estradiol levels resulting from fertility treatment did not relate to any considerable change in cognitive functioning. As the tests applied represent a broad variety of cognitive functions on different levels of complexity and with various brain regions involved, we can conclude that estradiol does not show a significant short-term effect on cognitive function.
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ISSN 0018-506x ISBN Medium
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Notes Approved no
Call Number MCLab @ davi @ Leeners2021104951 Serial 185
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Author Sinisi, S.; Alimguzhin, V.; Mancini, T.; Tronci, E.
Title Reconciling interoperability with efficient Verification and Validation within open source simulation environments Type Journal Article
Year 2021 Publication Simulation Modelling Practice and Theory Abbreviated Journal
Volume Issue Pages 102277
Keywords Simulation, Verification and Validation, Interoperability, FMI/FMU, Model Exchange, Cyber-Physical Systems
Abstract A Cyber-Physical System (CPS) comprises physical as well as software subsystems. Simulation-based approaches are typically used to support design and Verification and Validation (V&V) of CPSs in several domains such as: aerospace, defence, automotive, smart grid and healthcare. Accordingly, many simulation-based tools are available to support CPS design. This, on one side, enables designers to choose the toolchain that best suits their needs, on the other side poses huge interoperability challenges when one needs to simulate CPSs whose subsystems have been designed and modelled using different toolchains. To overcome such an interoperability problem, in 2010 the Functional Mock-up Interface (FMI) has been proposed as an open standard to support both Model Exchange (ME) and Co-Simulation (CS) of simulation models created with different toolchains. FMI has been adopted by several modelling and simulation environments. Models adhering to such a standard are called Functional Mock-up Units (FMUs). Indeed FMUs play an essential role in defining complex CPSs through, e.g., the System Structure and Parametrization (SSP) standard. Simulation-based V&V of CPSs typically requires exploring different simulation scenarios (i.e., exogenous input sequences to the CPS under design). Many such scenarios have a shared prefix. Accordingly, to avoid simulating many times such shared prefixes, the simulator state at the end of a shared prefix is saved and then restored and used as a start state for the simulation of the next scenario. In this context, an important FMI feature is the capability to save and restore the internal FMU state on demand. This is crucial to increase efficiency of simulation-based V&V. Unfortunately, the implementation of this feature is not mandatory and it is available only within some commercial software. As a result, the interoperability enabled by the FMI standard cannot be fully exploited for V&V when using open-source simulation environments. This motivates developing such a feature for open-source CPS simulation environments. Accordingly, in this paper, we focus on JModelica, an open-source modelling and simulation environment for CPSs based on an open standard modelling language, namely Modelica. We describe how we have endowed JModelica with our open-source implementation of the FMI 2.0 functions needed to save and restore internal states of FMUs for ME. Furthermore, we present experimental results evaluating, through 934 benchmark models, correctness and efficiency of our extended JModelica. Our experimental results show that simulation-based V&V is, on average, 22 times faster with our get/set functionality than without it.
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ISSN 1569-190x ISBN Medium
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Call Number MCLab @ davi @ Sinisi2021102277 Serial 186
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Author Sinisi, S.; Alimguzhin, V.; Mancini, T.; Tronci, E.; Mari, F.; Leeners, B.
Title Optimal Personalised Treatment Computation through In Silico Clinical Trials on Patient Digital Twins Type Journal Article
Year 2020 Publication Abbreviated Journal Fundamenta Informaticae
Volume 174 Issue Pages 283-310
Keywords Artificial Intelligence; Virtual Physiological Human; In Silico Clinical Trials; Simulation; Personalised Medicine; In Silico Treatment Optimisation
Abstract In Silico Clinical Trials (ISCT), i.e. clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present methods and an algorithm that, by means of extensive computer simulation-based experimental campaigns (ISCT) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine ). We show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans.
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Publisher IOS Press Place of Publication (up) Editor
Language Summary Language Original Title
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ISSN 1875-8681 ISBN Medium
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Call Number MCLab @ davi @ Serial 187
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Author Chen, Q.M.; Finzi, A.; Mancini, T.; Melatti, I.; Tronci, E.
Title MILP, Pseudo-Boolean, and OMT Solvers for Optimal Fault-Tolerant Placements of Relay Nodes in Mission Critical Wireless Networks Type Journal Article
Year 2020 Publication Abbreviated Journal Fundamenta Informaticae
Volume 174 Issue Pages 229-258
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Abstract In critical infrastructures like airports, much care has to be devoted in protecting radio communication networks from external electromagnetic interference. Protection of such mission-critical radio communication networks is usually tackled by exploiting radiogoniometers: at least three suitably deployed radiogoniometers, and a gateway gathering information from them, permit to monitor and localise sources of electromagnetic emissions that are not supposed to be present in the monitored area. Typically, radiogoniometers are connected to the gateway through relay nodes . As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper, we address the problem of computing a deployment for relay nodes that minimises the overall cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance ). We show that, by means of a computation-intensive pre-processing on a HPC infrastructure, the above optimisation problem can be encoded as a 0/1 Linear Program, becoming suitable to be approached with standard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMT solvers. Our problem formulation enables us to present experimental results comparing the performance of these three solving technologies on a real case study of a relay node network deployment in areas of the Leonardo da Vinci Airport in Rome, Italy.
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Publisher IOS Press Place of Publication (up) Editor
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ISSN 1875-8681 ISBN Medium
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Call Number MCLab @ davi @ Serial 188
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Author Fischer, S.; Ehrig, R.; Schaefer, S.; Tronci, E.; Mancini, T.; Egli, M.; Ille, F.; Krueger, T.H.C.; Leeners, B.; Roeblitz, S.
Title Mathematical Modeling and Simulation Provides Evidence for New Strategies of Ovarian Stimulation Type Journal Article
Year 2021 Publication Frontiers in Endocrinology Abbreviated Journal
Volume 12 Issue Pages 117
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Abstract New approaches to ovarian stimulation protocols, such as luteal start, random start or double stimulation, allow for flexibility in ovarian stimulation at different phases of the menstrual cycle. It has been proposed that the success of these methods is based on the continuous growth of multiple cohorts (“waves”) of follicles throughout the menstrual cycle which leads to the availability of ovarian follicles for ovarian controlled stimulation at several time points. Though several preliminary studies have been published, their scientific evidence has not been considered as being strong enough to integrate these results into routine clinical practice. This work aims at adding further scientific evidence about the efficiency of variable-start protocols and underpinning the theory of follicular waves by using mathematical modeling and numerical simulations. For this purpose, we have modified and coupled two previously published models, one describing the time course of hormones and one describing competitive follicular growth in a normal menstrual cycle. The coupled model is used to test ovarian stimulation protocols in silico. Simulation results show the occurrence of follicles in a wave-like manner during a normal menstrual cycle and qualitatively predict the outcome of ovarian stimulation initiated at different time points of the menstrual cycle.
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ISSN 1664-2392 ISBN Medium
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Notes Approved no
Call Number MCLab @ davi @ ref10.3389/fendo.2021.613048 Serial 189
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