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S. Fischer, R. Ehrig, S. Schaefer, E. Tronci, T. Mancini, M. Egli, F. Ille, T. H. C. Krueger, B. Leeners, and S. Roeblitz. "Mathematical Modeling and Simulation Provides Evidence for New Strategies of Ovarian Stimulation." Frontiers in Endocrinology 12 (2021): 117. ISSN: 1664-2392. DOI: 10.3389/fendo.2021.613048.
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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B. Leeners, T. H. C. Krueger, K. Geraedts, E. Tronci, T. Mancini, M. Egli, S. Roeblitz, L. Saleh, K. Spanaus, C. Schippert et al. "Associations Between Natural Physiological and Supraphysiological Estradiol Levels and Stress Perception." Frontiers in Psychology 10 (2019): 1296. ISSN: 1664-1078. DOI: 10.3389/fpsyg.2019.01296.
Abstract: Stress is a risk factor for impaired general, mental and reproductive health. The role of physiological and supraphysiological estradiol concentrations in stress perception and stress processing is less well understood. We therefore, conducted a prospective observational study to investigate the association between estradiol, stress perception and stress-related cognitive performance within serial measurements either during the natural menstrual cycle or during fertility treatment, where estradiol levels are strongly above the physiological level of a natural cycle and consequently, represent a good model to study dose-dependent effects of estradiol. Data from 44 women receiving in vitro fertilization at the Department of Reproductive Endocrinology in Zurich, Switzerland was compared to data from 88 women with measurements during their natural menstrual cycle. The german version of the Perceived Stress Questionnaire (PSQ) and the Cognitive Bias Test (CBT), in which cognitive performance is tested under time stress were used to evaluate subjective and functional aspects of stress. Estradiol levels were investigated at four different time points during the menstrual cycle and at two different time points during a fertility treatment. Cycle phase were associated with PSQ worry and cognitive bias in normally cycling women, but different phases of fertility treatment were not associated with subjectively perceived stress and stress-related cognitive bias. PSQ lack of joy and PSQ demands related to CBT in women receiving fertility treatment but not in women with a normal menstrual cycle. Only strong changes of the estradiol level during fertility treatment were weakly associated with CBT, but not with subjectively experienced stress. Our research emphasises the multidimensional character of stress and the necessity to adjust stress research to the complex nature of stress perception and processing. Infertility is associated with an increased psychological burden in patients. However, not all phases of the process to overcome infertility do significantly increase patient stress levels. Also, research on the psychological burden of infertility should consider that stress may vary during the different phases of fertility treatment.
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L. Tortora, G. Meynen, J. Bijlsma, E. Tronci, and S. Ferracuti. "Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective." Frontiers in Psychology 11 (2020): 220. ISSN: 1664-1078. DOI: 10.3389/fpsyg.2020.00220.
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 ‘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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Amedeo Cesta, Simone Fratini, Andrea Orlandini, Alberto Finzi, and Enrico Tronci. "Flexible Plan Verification: Feasibility Results." Fundamenta Informaticae 107, no. 2 (2011): 111–137. DOI: 10.3233/FI-2011-397.
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T. Mancini, F. Mari, A. Massini, I. Melatti, and E. Tronci. "SyLVaaS: System Level Formal Verification as a Service." Fundamenta Informaticae 149, no. 1-2 (2016): 101–132. DOI: 10.3233/FI-2016-1444.
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Michele Cecconi, and Enrico Tronci. "Requirements Formalization and Validation for a Telecommunication Equipment Protection Switcher." In Hase. IEEE Computer Society, 2000. ISSN: 0-7695-0927-4. DOI: 10.1109/HASE.2000.895456.
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G. Dipoppa, G. D'Alessandro, R. Semprini, and E. Tronci. "Integrating Automatic Verification of Safety Requirements in Railway Interlocking System Design." In High Assurance Systems Engineering, 2001. Sixth IEEE International Symposium on, 209–219. Albuquerque, NM, USA: IEEE Computer Society, 2001. ISSN: 0-7695-1275-5. DOI: 10.1109/HASE.2001.966821.
Abstract: A railway interlocking system (RIS) is an embedded system (namely a supervisory control system) that ensures the safe, operation of the devices in a railway station. RIS is a safety critical system. We explore the possibility of integrating automatic formal verification methods in a given industry RIS design flow. The main obstructions to be overcome in our work are: selecting a formal verification tool that is efficient enough to solve the verification problems at hand; and devising a cost effective integration strategy for such tool. We were able to devise a successful integration strategy meeting the above constraints without requiring major modification in the pre-existent design flow nor retraining of personnel. We run verification experiments for a RIS designed for the Singapore Subway. The experiments show that the RIS design flow obtained from our integration strategy is able to automatically verify real life RIS designs.
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B. Leeners, T. Krueger, K. Geraedts, E. Tronci, T. Mancini, F. Ille, M. Egli, S. Roeblitz, D. Wunder, L. Saleh et al. "Cognitive function in association with high estradiol levels resulting from fertility treatment." Hormones and Behavior 130 (2021): 104951. ISSN: 0018-506x. DOI: 10.1016/j.yhbeh.2021.104951.
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.
Keywords: Cognition, Estrogen, Estradiol, Fertility treatment, Attention, Cognitive bias
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Federico Mari, and Enrico Tronci. "CEGAR Based Bounded Model Checking of Discrete Time Hybrid Systems." In Hybrid Systems: Computation and Control (HSCC 2007), edited by A. Bemporad, A. Bicchi and G. C. Buttazzo, 399–412. Lecture Notes in Computer Science 4416. Springer, 2007. DOI: 10.1007/978-3-540-71493-4_32.
Abstract: Many hybrid systems can be conveniently modeled as Piecewise Affine Discrete Time Hybrid Systems PA-DTHS. As well known Bounded Model Checking (BMC) for such systems comes down to solve a Mixed Integer Linear Programming (MILP) feasibility problem. We present a SAT based BMC algorithm for automatic verification of PA-DTHSs. Using Counterexample Guided Abstraction Refinement (CEGAR) our algorithm gradually transforms a PA-DTHS verification problem into larger and larger SAT problems. Our experimental results show that our approach can handle PA-DTHSs that are more then 50 times larger than those that can be handled using a MILP solver.
Keywords: Model Checking, Abstraction, CEGAR, SAT, Hybrid Systems, DTHS
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Giuseppe Della Penna, Benedetto Intrigila, Igor Melatti, Michele Minichino, Ester Ciancamerla, Andrea Parisse, Enrico Tronci, and Marisa Venturini Zilli. "Automatic Verification of a Turbogas Control System with the Mur$\varphi$ Verifier." In Hybrid Systems: Computation and Control, 6th International Workshop, HSCC 2003 Prague, Czech Republic, April 3-5, 2003, Proceedings, edited by O. Maler and A. Pnueli, 141–155. Lecture Notes in Computer Science 2623. Springer, 2003. ISSN: 3-540-00913-2. DOI: 10.1007/3-540-36580-X.
Abstract: Automatic analysis of Hybrid Systems poses formidable challenges both from a modeling as well as from a verification point of view. We present a case study on automatic verification of a Turbogas Control System (TCS) using an extended version of the Mur$\varphi$ verifier. TCS is the heart of ICARO, a 2MW Co-generative Electric Power Plant. For large hybrid systems, as TCS is, the modeling effort accounts for a significant part of the whole verification activity. In order to ease our modeling effort we extended the Mur$\varphi$ verifier by importing the C language long double type (finite precision real numbers) into it. We give experimental results on running our extended Mur$\varphi$ on our TCS model. For example using Mur$\varphi$ we were able to compute an admissible range of values for the variation speed of the user demand of electric power to the turbogas.
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