History of POIC Capabilities and Limitations to Conduct International Space Station Payload Operations

Payload science operations on the International Space Station (ISS) have been conducted continuously twenty-four hours per day, 365 days a year beginning February, 2001 and continuing through present day. The Payload Operations Integration Center (POIC), located at the Marshall Space Flight Center in Huntsville, Alabama, has been a leader in integrating and managing NASA distributed payload operations. The ability to conduct science operations is a delicate balance of crew time, onboard vehicle resources, hardware up-mass to the vehicle, and ground based flight control team manpower. Over the span of the last ten years, the POIC flight control team size, function, and structure has been modified several times commensurate with the capabilities and limitations of the ISS program. As the ISS vehicle has been expanded and its systems changed throughout the assembly process, the resources available to conduct science and research have also changed. Likewise, as ISS program financial resources have demanded more efficiency from organizations across the program, utilization organizations have also had to adjust their functionality and structure to adapt accordingly. The POIC has responded to these often difficult challenges by adapting our team concept to maximize science research return within the utilization allocations and vehicle limitations that existed at the time. In some cases, the ISS and systems limitations became the limiting factor in conducting science. In other cases, the POIC structure and flight control team size were the limiting factors, so other constraints had to be put into place to assure successful science operations within the capabilities of the POIC. This paper will present the POIC flight control team organizational changes responding to significant events of the ISS and Shuttle programs

Dietary yeast influences ethanol sedation in Drosophila via serotonergic neuron function

Abuse of alcohol is a major clinical problem with far- reaching health consequences. Understanding the environmental and genetic factors that contribute to alcohol- related behaviors is a potential gateway for developing novel therapeutic approaches for patients that abuse the drug. To this end, we have used Drosophila melanogaster as a model to investigate the effect of diet, an environmental factor, on ethanol sedation. Providing flies with diets high in yeast, a routinely used component of fly media, increased their resistance to ethanol sedation. The yeast- induced resistance to ethanol sedation occurred in several different genetic backgrounds, was observed in males and females, was elicited by yeast from different sources, was readily reversible, and was associated with increased nutrient intake as well as decreased internal ethanol levels. Inhibition of serotonergic neuron function using multiple independent genetic manipulations blocked the effect of yeast supplementation on ethanol sedation, nutrient intake, and internal ethanol levels. Our results demonstrate that yeast is a critical dietary component that influences ethanol sedation in flies and that serotonergic signaling is required for the effect of dietary yeast on nutrient intake, ethanol uptake/elimination, and ethanol sedation. Our studies establish the fly as a model for diet- induced changes in ethanol sedation and raise the possibility that serotonin might mediate the effect of diet on alcohol- related behavior in other species.Flies fed a high yeast diet consume more nutrients, have decreased levels of internal ethanol when exposed to ethanol vapor and require longer exposure to ethanol to become sedated (ie, increased ST50). Our studies implicate serotonergic neurons as key regulators of nutrient consumption and therefore, the effect of dietary yeast on ethanol sedation in flies.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155987/1/adb12779.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155987/2/adb12779_am.pd

Toilet training: what can the cookstove sector learn from improved sanitation promotion?

Within the domain of public health, commonalities exist between the sanitation and cookstove sectors. Despite these commonalities and the grounds established for cross-learning between both sectors, however, there has not been much evidence of knowledge exchange across them to date. Our paper frames this as a missed opportunity for the cookstove sector, given the capacity for user-centred innovation and multi-scale approaches demonstrated in the sanitation sector. The paper highlights points of convergence and divergence in the approaches used in both sectors, with particular focus on behaviour change approaches that go beyond the level of the individual. The analysis highlights the importance of the enabling environment, community-focused approaches and locally-specific contextual factors in promoting behavioural change in the sanitation sector. Our paper makes a case for the application of such approaches to cookstove interventions, especially in light of their ability to drive sustained change by matching demand-side motivations with supply-side opportunities

Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo

Leçons tirées du défi NeurIPS 2021 MetaDL : le réglage fin du backbone sans méta-apprentissage épisodique domine pour la classification d'images d'apprentissage en quelques prises de vue

International audienceAlthough deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are available. Metalearning methods can address this problem by transferring knowledge from related tasks, thus reducing the amount of data and computing resources needed to learn new tasks. We organize the MetaDL competition series, which provide opportunities for research groups all over the world to create and experimentally assess new meta-(deep)learning solutions for real problems. In this paper, authored collaboratively between the competition organizers and the top-ranked participants, we describe the design of the competition, the datasets, the best experimental results, as well as the top-ranked methods in the NeurIPS 2021 challenge, which attracted 15 active teams who made it to the final phase (by outperforming the baseline), making over 100 code submissions during the feedback phase. The solutions of the top participants have been open-sourced. The lessons learned include that learning good representations is essential for effective transfer learning.Bien que les réseaux de neurones profonds soient capables d'atteindre des performances supérieures aux humains sur diverses tâches, ils sont connus pour nécessiter de grandes quantités de données et de ressources informatiques, limitant leur succès aux domaines où ces ressources sont disponibles. Les méthodes de méta-apprentissage peuvent résoudre ce problème en transférant des connaissances de tâches connexes, réduisant ainsi la quantité de données et de ressources informatiques nécessaires pour apprendre de nouvelles tâches. Nous organisons la série de challenges MetaDL, qui offre aux groupes de recherche du monde entier la possibilité de créer et d'évaluer expérimentalement de nouvelles solutions de méta-(deep)learning pour des problèmes réels. Dans cet article, rédigé en collaboration entre les organisateurs du concours et les meilleurs participants, nous décrivons la conception du challenge, les ensembles de données, les meilleurs résultats expérimentaux, ainsi que les méthodes les mieux classées du challenge NeurIPS 2021, qui a attiré 15 équipes actives qui ont atteint la phase finale (en surpassant la ligne de base), en faisant plus de 100 soumissions de code pendant la phase de feed-back. Les solutions des meilleurs participants ont été mises en open-source. Les leçons apprises incluent que l'apprentissage de bonnes représentations est essentiel pour un apprentissage par transfert efficace

Leçons tirées du défi NeurIPS 2021 MetaDL : le réglage fin du backbone sans méta-apprentissage épisodique domine pour la classification d'images d'apprentissage en quelques prises de vue

International audienceAlthough deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are available. Metalearning methods can address this problem by transferring knowledge from related tasks, thus reducing the amount of data and computing resources needed to learn new tasks. We organize the MetaDL competition series, which provide opportunities for research groups all over the world to create and experimentally assess new meta-(deep)learning solutions for real problems. In this paper, authored collaboratively between the competition organizers and the top-ranked participants, we describe the design of the competition, the datasets, the best experimental results, as well as the top-ranked methods in the NeurIPS 2021 challenge, which attracted 15 active teams who made it to the final phase (by outperforming the baseline), making over 100 code submissions during the feedback phase. The solutions of the top participants have been open-sourced. The lessons learned include that learning good representations is essential for effective transfer learning.Bien que les réseaux de neurones profonds soient capables d'atteindre des performances supérieures aux humains sur diverses tâches, ils sont connus pour nécessiter de grandes quantités de données et de ressources informatiques, limitant leur succès aux domaines où ces ressources sont disponibles. Les méthodes de méta-apprentissage peuvent résoudre ce problème en transférant des connaissances de tâches connexes, réduisant ainsi la quantité de données et de ressources informatiques nécessaires pour apprendre de nouvelles tâches. Nous organisons la série de challenges MetaDL, qui offre aux groupes de recherche du monde entier la possibilité de créer et d'évaluer expérimentalement de nouvelles solutions de méta-(deep)learning pour des problèmes réels. Dans cet article, rédigé en collaboration entre les organisateurs du concours et les meilleurs participants, nous décrivons la conception du challenge, les ensembles de données, les meilleurs résultats expérimentaux, ainsi que les méthodes les mieux classées du challenge NeurIPS 2021, qui a attiré 15 équipes actives qui ont atteint la phase finale (en surpassant la ligne de base), en faisant plus de 100 soumissions de code pendant la phase de feed-back. Les solutions des meilleurs participants ont été mises en open-source. Les leçons apprises incluent que l'apprentissage de bonnes représentations est essentiel pour un apprentissage par transfert efficace