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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 |
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Journal Article |
| Year |
2021 |
Publication |
Bioinformatics |
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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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1367-4803 |
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MCLab @ davi @ ref10.1093/bioinformatics/btaa1026 |
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182 |
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Tortora, L.; Meynen, G.; Bijlsma, J.; Tronci, E.; Ferracuti, S. |
| Title |
Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective |
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Journal Article |
| Year |
2020 |
Publication |
Frontiers in Psychology |
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| Volume |
11 |
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220 |
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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 ‘A.I. neuroprediction,’ 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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1664-1078 |
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MCLab @ davi @ Neuroprediction-2020 |
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180 |
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Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Tronci, E. |
| Title |
On Checking Equivalence of Simulation Scripts |
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Journal Article |
| Year |
2021 |
Publication |
Journal of Logical and Algebraic Methods in Programming |
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100640 |
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Formal verification, Simulation based formal verification, Formal Verification of cyber-physical systems, System-level formal verification |
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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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2352-2208 |
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no |
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MCLab @ davi @ Mancini2021100640 |
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183 |
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Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Tronci, E. |
| Title |
Anytime system level verification via parallel random exhaustive hardware in the loop simulation |
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Journal Article |
| Year |
2016 |
Publication |
Microprocessors and Microsystems |
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41 |
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12-28 |
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Model Checking of Hybrid Systems; Model checking driven simulation; Hardware in the loop simulation |
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Abstract System level verification of cyber-physical systems has the goal of verifying that the whole (i.e., software + hardware) system meets the given specifications. Model checkers for hybrid systems cannot handle system level verification of actual systems. Thus, Hardware In the Loop Simulation (HILS) is currently the main workhorse for system level verification. By using model checking driven exhaustive HILS, System Level Formal Verification (SLFV) can be effectively carried out for actual systems. We present a parallel random exhaustive HILS based model checker for hybrid systems that, by simulating all operational scenarios exactly once in a uniform random order, is able to provide, at any time during the verification process, an upper bound to the probability that the System Under Verification exhibits an error in a yet-to-be-simulated scenario (Omission Probability). We show effectiveness of the proposed approach by presenting experimental results on SLFV of the Inverted Pendulum on a Cart and the Fuel Control System examples in the Simulink distribution. To the best of our knowledge, no previously published model checker can exhaustively verify hybrid systems of such a size and provide at any time an upper bound to the Omission Probability. |
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0141-9331 |
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MCLab @ davi @ Mancini201612 |
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155 |
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Mancini, T.; Mari, F.; Melatti, I.; Salvo, I.; Tronci, E.; Gruber, J.; Hayes, B.; Prodanovic, M.; Elmegaard, L. |
| Title |
Parallel Statistical Model Checking for Safety Verification in Smart Grids |
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Conference Article |
| Year |
2018 |
Publication |
2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) |
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1-6 |
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MCLab @ davi @ mancini-etal:2018:smartgridcomm |
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170 |
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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 |
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Journal Article |
| Year |
2021 |
Publication |
Hormones and Behavior |
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130 |
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104951 |
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Cognition, Estrogen, Estradiol, Fertility treatment, Attention, Cognitive bias |
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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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0018-506x |
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MCLab @ davi @ Leeners2021104951 |
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185 |
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| Author |
Driouich, Y.; Parente, M.; Tronci, E. |
| Title |
A methodology for a complete simulation of Cyber-Physical Energy Systems |
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Conference Article |
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2018 |
Publication |
EESMS 2018 – Environmental, Energy, and Structural Monitoring Systems, Proceedings |
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1-5 |
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MCLab @ davi @ Driouich20181 |
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169 |
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Driouich, Y.; Parente, M.; Tronci, E. |
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Model Checking Cyber-Physical Energy Systems |
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Conference Article |
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2018 |
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Proceedings of 2017 International Renewable and Sustainable Energy Conference, IRSEC 2017 |
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Institute of Electrical and Electronics Engineers Inc. |
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MCLab @ davi @ Driouich2018 |
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177 |
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Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Tronci, E. |
| Title |
SyLVaaS: System Level Formal Verification as a Service |
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Journal Article |
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2016 |
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Fundamenta Informaticae |
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149 |
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1-2 |
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101-132 |
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MCLab @ davi @ DBLP:journals/fuin/ManciniMMMT16 |
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160 |
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Mancini, T. |
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Now or Never: Negotiating Efficiently with Unknown or Untrusted Counterparts |
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Journal Article |
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2016 |
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Fundamenta Informaticae |
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149 |
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1-2 |
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61-100 |
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MCLab @ davi @ DBLP:journals/fuin/Mancini16 |
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161 |
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