Learning from organisational embedding for climate resilience

The term ‘resilience’, which is integral to the UK Climate Resilience Programme (UKCR), has been used increasingly in academic, practice and public discourse around climate change, and crises more generally. The term’s appeal comes from its ability to frame crises not as uncontrollable and uncertain phenomena to be feared, but as challenges over which one can triumph, with the potential for improving society

Using stochastic dual dynamic programming in problems with multiple near-optimal solutions

Stochastic dual dynamic programming (SDDP) is one of the few algorithmic solutions available to optimize large‐scale water resources systems while explicitly considering uncertainty. This paper explores the consequences of, and proposes a solution to, the existence of multiple near‐optimal solutions (MNOS) when using SDDP for mid or long‐term river basin management. These issues arise when the optimization problem cannot be properly parametrized due to poorly defined and/or unavailable data sets. This work shows that when MNOS exists, (1) SDDP explores more than one solution trajectory in the same run, suggesting different decisions in distinct simulation years even for the same point in the state‐space, and (2) SDDP is shown to be very sensitive to even minimal variations of the problem setting, e.g., initial conditions—we call this “algorithmic chaos.” Results that exhibit such sensitivity are difficult to interpret. This work proposes a reoptimization method, which simulates system decisions by periodically applying cuts from one given year from the SDDP run. Simulation results obtained through this reoptimization approach are steady state solutions, meaning that their probability distributions are stable from year to year

Meeting sustainable development goals via robotics and autonomous systems

Robotics and autonomous systems are reshaping the world, changing healthcare, food production and biodiversity management. While they will play a fundamental role in delivering the UN Sustainable Development Goals, associated opportunities and threats are yet to be considered systematically. We report on a horizon scan evaluating robotics and autonomous systems impact on all Sustainable Development Goals, involving 102 experts from around the world. Robotics and autonomous systems are likely to transform how the Sustainable Development Goals are achieved, through replacing and supporting human activities, fostering innovation, enhancing remote access and improving monitoring. Emerging threats relate to reinforcing inequalities, exacerbating environmental change, diverting resources from tried-and-tested solutions and reducing freedom and privacy through inadequate governance. Although predicting future impacts of robotics and autonomous systems on the Sustainable Development Goals is difficult, thoroughly examining technological developments early is essential to prevent unintended detrimental consequences. Additionally, robotics and autonomous systems should be considered explicitly when developing future iterations of the Sustainable Development Goals to avoid reversing progress or exacerbating inequalities

Résilience et vulnérabilité dans le cadre de la théorie de la viabilité et des systèmes dynamiques stochastiques contrôlés

This thesis proposes mathematical definitions of the resilience and vulnerability concepts, in the framework of stochastic controlled dynamical system, and particularly that of discrete time stochastic viability theory. It relies on previous works defining resilience in the framework of deterministic viability theory. The proposed definitions stem from the hypothesis that it is possible to distinguish usual uncertainty, included in the dynamics, from extreme or surprising events. Stochastic viability and reliability only deal with the first kind of uncertainty, and both evaluate the probability of exiting a subset of the state space in which the system’s properties are verified. Stochastic viability thus appears to be a branch of reliability theory. One of its central objects is the stochastic viability kernel, which contains all the states that are controllable so their probability of keeping the properties over a given time horizon is greater than a threshold value. We propose to define resilience as the probability of getting back to the stochastic viability kernel after an extreme or surprising event. We use stochastic dynamic programming to maximize both the probability of being viable and the probability of resilience at a given time horizon. We propose to then define vulnerability from a harm function defined on every possible trajectory of the system. The trajectories’ probability distribution implies that of the harm values and we define vulnerability as a statistic over this latter distribution. This definition is applicable with both the aforementioned uncertainty sources. On one hand, considering usual uncertainty, we define sets such that vulnerability is below a threshold, which generalizes the notion of stochastic viability kernel. On the other hand, after an extreme or surprising event, vulnerability proposes indicators to describe recovery trajectories (assuming that only usual uncertainty comes into play then). Vulnerability indicators related to a cost or to the crossing of a threshold can be minimized thanks to stochastic dynamic programming. We illustrate the concepts and tools developed in the thesis through an application to preexisting indicators of reliability and vulnerability that are used to evaluate the performance of a water supply system. We focus on proposing a stochastic dynamic programming algorithm to minimize a criterion that combines criteria of cost and of exit from the constraint set. The concepts are then articulated to describe the performance of a reservoir.Cette thèse propose des définitions mathématiques des concepts de résilience et de vulnérabilité dans le cadre des systèmes dynamiques stochastiques contrôlés, et en particulier celui de la viabilité stochastique en temps discret. Elle s’appuie sur les travaux antérieurs définissant la résilience dans le cadre de la viabilité pour des dynamiques déterministes. Les définitions proposées font l’hypothèse qu’il est possible de distinguer des aléas usuels, inclus dans la dynamique, et des événements extrêmes ou surprenants dont on étudie spécifiquement l’impact. La viabilité stochastique et la fiabilité ne mettent en jeu que le premier type d’aléa, et s’intéressent à l’évaluation de la probabilité de sortir d’un sous-ensemble de l’espace d’état dans lequel les propriétés d’intérêt du système sont satisfaites. La viabilité stochastique apparaît ainsi comme une branche de la fiabilité. Un objet central en est le noyau de viabilité stochastique, qui regroupe les états contrôlables pour que leur probabilité de garder les propriétés sur un horizon temporel défini soit supérieure à un seuil donné. Nous proposons de définir la résilience comme la probabilité de revenir dans le noyau de viabilité stochastique après un événement extrême ou surprenant. Nous utilisons la programmation dynamique stochastique pour maximiser la probabilité d’être viable ainsi que pour optimiser la probabilité de résilience à un horizon temporel donné. Nous proposons de définir ensuite la vulnérabilité à partir d’une fonction de dommage définie sur toutes les trajectoires possibles du système. La distribution des trajectoires définit donc une distribution de probabilité des dommages et nous définissons la vulnérabilité comme une statistique sur cette distribution. Cette définition s’applique aux deux types d’aléas définis précédemment. D’une part, en considérant les aléas du premier type, nous définissons des ensembles tels que la vulnérabilité soit inférieure à un seuil, ce qui généralise la notion de noyau de viabilité stochastique. D’autre part, après un aléa du deuxième type, la vulnérabilité fournit des indicateurs qui aident à décrire les trajectoires de retour (en considérant que seul l’aléa de premier type intervient). Des indicateurs de vulnérabilité lié à un coût ou au franchissement d’un seuil peuvent être minimisés par la programmation dynamique stochastique. Nous illustrons les concepts et outils développés dans la thèse en les appliquant aux indicateurs pré-existants de fiabilité et de vulnérabilité, utilisés pour évaluer la performance d’un système d’approvisionnement en eau. En particulier, nous proposons un algorithme de programmation dynamique stochastique pour minimiser un critère qui combine des critères de coût et de sortie de l’ensemble de contraintes. Les concepts sont ensuite articulés pour décrire la performance d’un réservoir

Resilience and vulnerability in the framework of viability theory and stochastic controlled dynamical systems

Cette thèse propose des définitions mathématiques des concepts de résilience et de vulnérabilité dans le cadre des systèmes dynamiques stochastiques contrôlés, et en particulier celui de la viabilité stochastique en temps discret. Elle s’appuie sur les travaux antérieurs définissant la résilience dans le cadre de la viabilité pour des dynamiques déterministes. Les définitions proposées font l’hypothèse qu’il est possible de distinguer des aléas usuels, inclus dans la dynamique, et des événements extrêmes ou surprenants dont on étudie spécifiquement l’impact. La viabilité stochastique et la fiabilité ne mettent en jeu que le premier type d’aléa, et s’intéressent à l’évaluation de la probabilité de sortir d’un sous-ensemble de l’espace d’état dans lequel les propriétés d’intérêt du système sont satisfaites. La viabilité stochastique apparaît ainsi comme une branche de la fiabilité. Un objet central en est le noyau de viabilité stochastique, qui regroupe les états contrôlables pour que leur probabilité de garder les propriétés sur un horizon temporel défini soit supérieure à un seuil donné. Nous proposons de définir la résilience comme la probabilité de revenir dans le noyau de viabilité stochastique après un événement extrême ou surprenant. Nous utilisons la programmation dynamique stochastique pour maximiser la probabilité d’être viable ainsi que pour optimiser la probabilité de résilience à un horizon temporel donné. Nous proposons de définir ensuite la vulnérabilité à partir d’une fonction de dommage définie sur toutes les trajectoires possibles du système. La distribution des trajectoires définit donc une distribution de probabilité des dommages et nous définissons la vulnérabilité comme une statistique sur cette distribution. Cette définition s’applique aux deux types d’aléas définis précédemment. D’une part, en considérant les aléas du premier type, nous définissons des ensembles tels que la vulnérabilité soit inférieure à un seuil, ce qui généralise la notion de noyau de viabilité stochastique. D’autre part, après un aléa du deuxième type, la vulnérabilité fournit des indicateurs qui aident à décrire les trajectoires de retour (en considérant que seul l’aléa de premier type intervient). Des indicateurs de vulnérabilité lié à un coût ou au franchissement d’un seuil peuvent être minimisés par la programmation dynamique stochastique. Nous illustrons les concepts et outils développés dans la thèse en les appliquant aux indicateurs pré-existants de fiabilité et de vulnérabilité, utilisés pour évaluer la performance d’un système d’approvisionnement en eau. En particulier, nous proposons un algorithme de programmation dynamique stochastique pour minimiser un critère qui combine des critères de coût et de sortie de l’ensemble de contraintes. Les concepts sont ensuite articulés pour décrire la performance d’un réservoir.This thesis proposes mathematical definitions of the resilience and vulnerability concepts, in the framework of stochastic controlled dynamical system, and particularly that of discrete time stochastic viability theory. It relies on previous works defining resilience in the framework of deterministic viability theory. The proposed definitions stem from the hypothesis that it is possible to distinguish usual uncertainty, included in the dynamics, from extreme or surprising events. Stochastic viability and reliability only deal with the first kind of uncertainty, and both evaluate the probability of exiting a subset of the state space in which the system’s properties are verified. Stochastic viability thus appears to be a branch of reliability theory. One of its central objects is the stochastic viability kernel, which contains all the states that are controllable so their probability of keeping the properties over a given time horizon is greater than a threshold value. We propose to define resilience as the probability of getting back to the stochastic viability kernel after an extreme or surprising event. We use stochastic dynamic programming to maximize both the probability of being viable and the probability of resilience at a given time horizon. We propose to then define vulnerability from a harm function defined on every possible trajectory of the system. The trajectories’ probability distribution implies that of the harm values and we define vulnerability as a statistic over this latter distribution. This definition is applicable with both the aforementioned uncertainty sources. On one hand, considering usual uncertainty, we define sets such that vulnerability is below a threshold, which generalizes the notion of stochastic viability kernel. On the other hand, after an extreme or surprising event, vulnerability proposes indicators to describe recovery trajectories (assuming that only usual uncertainty comes into play then). Vulnerability indicators related to a cost or to the crossing of a threshold can be minimized thanks to stochastic dynamic programming. We illustrate the concepts and tools developed in the thesis through an application to preexisting indicators of reliability and vulnerability that are used to evaluate the performance of a water supply system. We focus on proposing a stochastic dynamic programming algorithm to minimize a criterion that combines criteria of cost and of exit from the constraint set. The concepts are then articulated to describe the performance of a reservoir

Culex quinquefasciatus Late Trypsin Biosynthesis Is Translationally Regulated by Trypsin Modulating Oostatic Factor

International audienceTrypsin is a serine protease that is synthesized by the gut epithelial cells of female mosquitoes; it is the enzyme that digests the blood meal. To study its molecular regulation, Culex quinquefasciatus late trypsin was purified by diethylaminoethyl (DEAE), affinity, and C 18 reverse-phase high performance liquid chromatography (HPLC) steps, and the N-terminal amino acid sequence was determined for molecular cloning. Five overlapping segments of the late trypsin cDNA were amplified by PCR, cloned, and the full sequence (855 bp) was characterized. Three-dimensional models of the pro-trypsin and activated trypsin were built and compared with other trypsin models. Trypsin modulating oostatic factor (TMOF) concentrations in the hemolymph were determined by ELISA and compared with trypsin activity in the gut after the blood meal. The results showed that there was an increase in TMOF concentrations circulating in the hemolymph which has correlated to the reduction of trypsin activity in the mosquito gut. Northern blot analysis of the trypsin transcripts after the blood meal indicated that trypsin activity also followed the increase and decrease of the trypsin transcript. Injections of different amounts of TMOF (0.025 to 50 μg) decreased the amounts of trypsin in the gut. However, Northern blot analysis showed that TMOF injections did not cause a decrease in trypsin transcript abundance, indicating that TMOF probably affected trypsin translation

Cloning and expressing a highly functional and substrate specific farnesoic acid o-methyltransferase from the Asian citrus psyllid ( Diaphorina citri Kuwayama)

International audienceThe Asian citrus psyllid,Diaphorina citri, transmits a phloem-limited bacterium,Candidatus ‘Liberibacter’ asiaticus that causes citrus greening disease. Because juvenile hormone (JH) plays an important role in adult and nymphal development, we studied the final steps in JH biosynthesis inD. citri. A putative JH acid methyltransferase ortholog gene (jmtD) and its cognate cDNA were identified by searchingD. citri genome database. Expression analysis shows expression in all life stages. In adults, it is expressed in the head-thorax, (containing the corpora allata), and the abdomen (containing ovaries and male accessory glands). A 3D protein model identified the catalytic groove with catalytically active amino acids and the S-adenosyl methionine (SAM)-binding loop. The cDNA was expressed inEscherichia coli cells and the purified enzyme showed high preference for farnesoic acid (FA) and homoFA (kcat of 0.752 × 10−3 and 0.217 × 10−3s−1, respectively) as compared to JH acid I (JHA I) (cis/trans/cis; 2Z, 6E, 10cis), JHA III (2E, 6E,10cis), and JHA I (trans/cis/cis; 2E, 2Z, 10cis) (kcat of 0.081 × 10−3, 0.013 × 10−3, and 0.003 × 10−3s−1, respectively). This suggests that this ortholog is aDcFA-o-methyl transferase gene (fmtD), not ajmtD, and that JH biosynthesis inD. citri proceeds from FA to JH III through methyl farnesoate (MF).DcFA-o-MT does not require Ca2+, Mg2+ or Zn2+, however, Zn2+ (1 mM) completely inhibits the enzyme probably by binding H115 at the active groove. This represents the first purified FA-o-MT from Hemiptera with preferred biological activity for FA and not JHA

Les sujets de l'architecture

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Aedes aegypti juvenile hormone acid methyl transferase, the ultimate enzyme in the biosynthetic pathway of juvenile hormone III, exhibits substrate control

International audienceWe report on the cloning, sequencing, characterization, 3D modeling and docking of Aedes aegypti juvenile hormone acid methyl transferase (AeaJHAMT), the enzyme that converts juvenile hormone acid (JHA) into juvenile hormone (JH). Purified recombinant AeaJHAMT was extensively characterized for enzymatic activity and the Michaelis Menten kinetic parameters Km, Vmax, k(cat) (turn over number) and k(cat)/Km (catalytic efficiency) using JHA and its analogues as substrates. AeaJHAMT methylates JHA III 5-fold faster than farnesoic acid (FA). Significant differences in lower methyl transferase (MT) activities towards the cis/trans/trans, cis/trans/cis and the trans/cis/cis isomers of JHA I (1.32, 4.71 and 156-fold, respectively) indicate that substrate chirality is important for proper alignment at the catalytic cavity and for efficient methyl transfer by S-adenosyl methionine (SAM). Our 3D model shows a potential binding site below the main catalytic cavity for JHA analogues causing conformational change and steric hindrance in the transfer of the methyl group to JHA III. These, in silico, observations were corroborated by, in vitro, studies showing that several JHA analogues are potent inhibitors of AeaJHAMT. In vitro, and in vivo studies using [(3)H-methyl]SAM show that the enzyme is present and active throughout the adult life stage of A. aegypti. Tissue specific expressions of the JHAMT gene of A. aegypti (jmtA) transcript during the life cycle of A. aegypti show that AeaJHAMT is a constitutive enzyme and jmtA transcript is expressed in the corpora allata (CA), and the ovary before and after the blood meal. These results indicate that JH III can be synthesized from JHA III by the mosquito ovary, suggesting that ovarian JH III may play an important physiological role in ovarian development and reproduction. Incubating AeaJHAMT with highly pure synthetic substrates indicates that JHA III is the enzyme's preferred substrate, suggesting that AeaJHAMT is the ultimate enzyme in the biosynthetic pathway of JH III