Biological diversity within species is an overlooked but fundamental level of biodiversity which contributes to the stability of ecosystems, as well as to adaptation to heterogeneous and changing environments. The coexistence of specialised ecotypes and morphotypes within a species, as well as adaptation to local environments is promoted by peculiar genomic structural variants called chromosomal inversions. Inversion-associated diversity nevertheless shows contrasting patterns, from widespread polymorphism to fixation between habitats or lineages, and it is still unclear what causes and consequences of such different evolutionary dynamics are. In particular, an inversion behaves and evolves like a large-effect single locus (“supergene”) under selective and demographic processes that shape its evolutionary trajectory. Then, inside the inversion, there is a second level of diversity, the DNA content, that varies and evolves, but the feedback loops between the two levels are poorly understood empirically. In this project, I propose contrasting two adaptive inversions in the seaweed fly Coleopa frigida that follow different evolutionary dynamics: one is widely polymorphic and the other is strongly structured along latitudinal clines. By combining experimental and genomic approaches on four parallel replicates across continents, my team and I will assess the relative role of selection and historical processes in determining inversions' distributions. Then, we will test the prediction that the evolutionary trajectories of inversions condition the evolution of its content. Altogether, those results will shed light on the evolution of genetic and phenotypic diversity within species, and push forward our knowledge about inversions which are widespread structural variants relevant for fitness and adaptation but also human health.

TRUST focuses on personal data protection measures to meet the objectives of the RGPD but also the texts in preparation such as the "Data Act" or the "Data Governance Act". We propose to study and develop new security solutions, based on advanced cryptography, for use cases involving the reuse of personal data. These use cases will present various configurations in terms of actors, type of data and processing, opening the way to different technical and legal issues. We thus seek to anticipate legal evolutions and prepare technical architectures to allow the reuse of personal data in compliance with the various legal frameworks.

Robot-assisted surgery, allowing surgeons to perform complex surgeries through tiny incisions, has been significantly increasing in popularity worldwide. However, surgical safety is still a major concern in the high-risk operating environment and remains a threat to the quality of surgical outcome. As global statistics, millions of surgeries per year would encounter safety-critical intraoperative adverse events, most of which were otherwise avoidable if the surgeon can be timely aware of the potential risks in operation. In this project, we aim to introduce smart context-awareness into robot- assisted surgery, by developing novel artificial intelligence techniques to provide automatic cognitive assistance for surgeons during critical moments of the procedure, in order to improve surgical safety and quality. The use case of this project will be robot-assisted hysterectomy, which is the most common gynecological procedure performed on women diagnosed with uterine fibroids or cervical cancer. Both Hong Kong and French teams will explore together innovative multimodal machine learning methods, based on available synchronized clinical video and kinematic data, which will be more advanced and clinically relevant than all existing methods that only used visual perception. Based on our pilot studies, we have identified a set of critical intraoperative scenarios to address avoidable adverse events in hysterectomy, such as injury of the pelvic ureter during both the coagulation of the uterus pedicle and adnexectomy. To achieve our goal, we will solve the following key challenges: 1) How to yield precise and real-time recognition of the surgical context, i.e., surgical workflow, operation actions, surgical instruments, anatomical tissues and the reconstructed 3D surgical environments. 2) How to conduct automatic assessment of the identified critical-context-of-safety (CCS), and further provide informed decision-making support to surgeons for their best practice to avoid safety risks. By a research collaboration between world-class teams with complementary expertise and already-available clinical and annotated data, the i-SaferS project will generate outputs that provide fundamentally new and generic solutions and impactful references to the field. The project outcomes will significantly contribute to the emerging field of intelligent robotic surgery, and further strengthen the leading competitiveness of both partners in this field nationally and internationally.

Vagus nerve stimulation (VNS) is an approved clinical therapy for medically refractory epilepsy and depression. More recently, VNS has been proposed as a promising therapeutic approach for other pathologies such as heart failure, cardiac arrhythmia, inflammation and auto-immune diseases. One common difficulty currently encountered in all these established or promising clinical applications is to deliver an efficient therapy, while minimizing side effects. This is a particularly complex problem in the case of VNS, since a typical stimulation pattern consists of a set of biphasic pulses, characterized by several parameters, delivered through different electrode configurations. Moreover, the effects of VNS are poorly known and complex to study, since they involve many different organs and physiological functions and may change through time, due to neural or organ remodeling. Due to this complexity, current VNS technology is applied using fixed parameters obtained from limited, non-optimal manual titration and this simplistic approach may explain the lack of effect or the intolerance to the therapy. Although many efforts have been performed recently to propose closed-loop VNS methods, the proposed algorithms remain simple, limited to the modulation of one parameter and without a clear definition of the appropriate control variables. Furthermore, current electrode technologies provide a poor spatial resolution for stimulation and a low signal-to-noise ratio for neural recording, also limiting the development of advanced closed-loop approaches. We hypothesize that the use of an automated, closed-loop and subject-specific method for VNS parameter optimization, integrating new electrode technologies and new knowledge of the underlying physiology, may lead to an improved outcome for VNS patients and to address new therapeutic applications. The main objective of this project is thus to propose such novel data processing methods and electrode technologies, allowing for a closed-loop, subject-specific optimization of VNS therapy. Although the proposed methods and technologies will be generic, a second objective is to explore, through extensive in-silico, in-situ and in-vivo experimentations, the usefulness of the proposed system on a promising therapeutic target for VNS: the prevention of Sudden and Unexpected Death in Epileptic Patients (SUDEP). This second objective requires 1) the early detection of the potential occurrence of a SUDEP event and 2) the application of an original acute, adaptive VNS, to block the propagation through vagal efferent pathways, in order to prevent bradycardia and respiratory arrest. This project is organized in 6 work-packages. WP1 concerns project management. Two WP will be focused on technical developments: WP2 for data processing, modelling and control methods and WP3, focused on novel electrode technologies. A prototype neurostimulation system integrating these technologies will be developed in WP4. Finally, WP5 and WP6 will address the in-situ and in-vivo animal experimentations required for this project. AdaptVNS is an ambitious project, that we consider however feasible, on the basis of progress achieved in recent years by the consortium and on their available intellectual property (5 patents). Although this project presents some risks, it has a high potential of societal and industrial impact. If this project is successful, the final product will be a complete, functional, neuromodulation system prototype, integrating advanced closed-loop methods and organic electrode technologies. To our knowledge, there is no equivalent system today. Also, new ways for preventing SUDEP will be investigated. Such results may open new ways to optimally deliver VNS on current target clinical applications, to provide novel therapeutic functions, but also, to renew research on novel VNS therapies which are not always effective when delivered with standard technologies (heart failure, antiarrhythmic therapies, etc).

ENDOSIM is a research project in the field of predictive simulation and computer-aided medical interventions (CAMI). It focuses on the treatment of aortic aneurisms and valvular stenoses. In a previous ANR TecSan project (ANGIOVISION, ended in February 2013), the partners of the ENDOSIM project have developed operative assistance tools using augmented angio-navigation for the treatment of abdominal aortic aneurysms (AAA). The results demonstrated, on more than 20 patients, the accuracy of the patient-specific simulation approach. Based on these developments and results, the team aims to move forward and tackle the problem of predictive planning, in order to maximize the accuracy and reliability of two complex endovascular procedures: • the implantation of fenestrated stent-grafts for the treatment of thoraco-abdominal aneurysms, • the endovascular implantation of cardiac valves for the treatment of aortic stenoses. For these two minimally invasive procedures, atheromatous plaques are sources of numerous, unsolved so far, difficulties among which: navigability issues in the vicinity of the lesions, risk of plaque rupture due to ancillary contacts, complexity for positioning the device on the lesion site, brittleness of the vasculature, crushing of the native valves… These issues currently constitute a major obstacle for a more massive use of endovascular techniques. The goal of ENDOSIM is to develop the first predictive endovascular surgery planning software in the world. This will lead to optimize the pre-operative planning and to secure per-operative navigation, through the following points: • tool navigability estimation from the patient’s imaging data, • improvement of the pre-operative device sizing reliability, • pre-operative prediction of the device positioning and per-operative visualization, • decision-making help for patient eligibility and device selection. In order to reach these objectives, the novel approach featured in ENDOSIM relies upon the joint use of image analysis techniques and biomechanical numerical simulation techniques, both being patient-specific and predictive. The scientific breakthroughs of ENDOSIM comprise mainly accurate and predictive patient-specific simulations of the endovascular ancillary insertion and device deployment. These simulations will be based on pre-operative imaging data and validated using per- and post-operative data on a group of atheromatous patients. The prediction of the risk of surgery-induced injury at the atheromatous sites is also very original. The numerical simulations developed through the project will be systematically enhanced and validated thanks to 3D imaging data obtained on real patients with the per-operative multi-incidence equipment of the TherA-Image platform. From a clinical point of view, the benefits of the ENDOSIM project will relate to securing the surgical planning thanks to simulations based on pre-operative data and improved positioning accuracy thanks augmented navigation tools. This should allow a more massive use of endovascular treatments and hence make the most of these minimally invasive procedures for the patients. From the industrial point of view, ENDOSIM will lead Therenva® (French leader in endovascular surgery software) to market the first predictive endovascular planning software solution. This will also be complemented by a visualization system for per-operative assistance. The close partnership with Ansys® (worldwide leader in numerical simulation) will promote a widespread adoption of Therenva® software solutions by endovascular device companies, as a first step, and by the worldwide clinical community as a second step.