Institute of Biology Valrose

Understanding how giant and terrestrial planets form and evolve, what is their internal structure and that of their atmosphere, represents one of the major challenges of modern astronomy, which is directly connected to the ultimate search for life at the horizon 2030–2050. However, several astrophysical (understanding of the formation and physics of giant and terrestrial exoplanets), biological (identification of the best biomarkers) and technological (technical innovations for the new generations of telescopes and instruments) obstacles must be overcome. From the astrophysical point of view, it is indeed crucial to understand the mechanisms of formation and evolution of giant planets, including planet and disk interactions, which will completely sculpt the planetary architectures and thus dominate the formation of terrestrial planets, especially in regions around the host star capable of supporting life. It is also important to develop dedicated instrumentation and techniques to study in their totality the population of giant and terrestrial planets, but also to reveal in the near future the first biological markers of life in the atmospheres of terrestrial planets. In that perspective, direct imaging from ground-based observatories or in space is playing a central role in concert with other observing techniques. In this paper, I will briefly review the genesis of this observing technique, the main instrumental innovation and challenges, stellar targets and surveys, to then present the main results obtained so far about the physics and the mechanisms of formation and evolution of young giant planets and planetary system architectures. I will then present the exciting perspectives offered by the upcoming generation of planet imagers about to come online, particularly on the future extremely large telescopes. On the timescale of a human Life, we may well be witnessing the first discovery of an exoplanet and the first detection of indices of life in the atmosphere of a nearby exo-Earth!

Abstract This chapter investigates the dynamic synergy between pedagogy, social dynamics, and technological developments in Digital Game-Based Learning (DGBL), which is enhanced by artificial intelligence (AI). The chapter navigates through educational modifications, learner profiling challenges, social enhancements, and technical considerations, emphasising AI's revolutionary influence. The topic expands on the critical interaction between learning analytics and machine learning, demonstrating AI's promise for personalised and adaptable DGBL experiences. The practical ramifications of real-time feedback in AI-driven DGBL are discussed, with the goal of providing timely instruction and encouraging positive behaviours. Finally, the chapter sheds light on the collaborative evolution of AI-enhanced education, providing useful insights for educators, instructional designers, and developers in creating optimised digital learning environments.

The heart, which is the first organ to develop in the embryo, is indispensable for vital functions throughout life [...].

Cancer is initiated by somatic mutations in oncogenes or tumor suppressor genes. However, additional alterations provide selective advantages to the tumor cells to resist treatment and develop metastases. Their identification is of paramount importance. Reduced expression of EFA6B (Exchange Factor for ARF6, B) is associated with breast cancer of poor prognosis. Here, we report that loss of EFA6B triggers a transcriptional reprogramming of the cell-to-ECM interaction machinery and unleashes CDC42-dependent collective invasion in collagen. In xenograft experiments, MCF10 DCIS.com cells, a DCIS-to-IDC transition model, invades faster when knocked-out for EFA6B. In addition, invasive and metastatic tumors isolated from patients have lower expression of EFA6B and display gene ontology signatures identical to those of EFA6B knock-out cells. Thus, we reveal an EFA6B-regulated molecular mechanism that controls the invasive potential of mammary cells; this finding opens up avenues for the treatment of invasive breast cancer.

Insulin signaling in Drosophila has a significant role in regulating growth, metabolism, fecundity, stress response, and longevity. The molecular mechanism by which insulin signaling regulates these vital processes is dependent on the nutrient status and oxygen availability of the organism. In a genetic screen to identify novel genes that regulate Drosophila insulin signaling, we discovered lumens interrupted (lint), a gene that has previously been shown to act in tracheal development. The knockdown of lint gene expression using a Dilp2Gal4 driver which expresses in the neuronal insulin producing cells (IPCs), led to defects in systemic insulin signaling, metabolic status and growth. However, our analysis of lint knockdown phenotypes revealed that downregulation of lint in the trachea and not IPCs was responsible for the growth phenotypes, as the Gal4 driver is also expressed in the tracheal system. We found various tracheal terminal branch defects, including reduction in the length as well as number of branches in the lint knockdown background. Our study reveals that substantial effects of lint downregulation arose because of tracheal defects, which induced tissue hypoxia, altered systemic insulin/TOR signaling, and resulted in effects on developmental growth regulation.

In this paper, we aim to present a unified mathematical modeling and description of the kinetics of magnetic nanoparticles phase condensation (conducting to the appearance of bulk elongated aggregates) under homogeneous permanent or alternating magnetic field. For such case, the aggregate growth rate usually takes the form dV/dt = G(V)∆(t), with V and t being the aggregate's volume and time, respectively, ∆(t)—the supersaturation of the nanoparticle suspension, and with the function G(V) depending on the precise configuration of the applied field. The Liouville equation for the aggregate size distribution function is solved by the method of characteristics. The solution is obtained in parametric form for an arbitrary function G(V), providing a general framework for any type of the applied magnetic field. In the particular case of low‐frequency rotating magnetic field (G(V)~V2/3), an explicit expression of the distribution function is obtained, while the dimensionless average aggregate volume 〈V〉 is found by the method of moments allowing a complete decoupling of the system of equations for the statistical moments 〈Vn〉 of the distribution function. Numerical examples are provided for the cases of permanent and low‐ or medium‐frequency rotating fields. It is shown that in all cases, the average volume 〈V〉 only slightly depends on the relative width of the initial size distribution. Nevertheless, at any times, t > 0, the size distribution shows a significant spreading around the average value 〈V〉, which increases progressively with time and achieves a final plateau at long times. This model can be helpful for several biomedical or environmental applications of magnetic nanoparticles in which the nanoparticle suspension undergoes a field‐induced phase condensation.