Process economical effects of implementation of ready-to-use micro carriers in cell- based virus vaccine production

Micro-carriers are used as support for the growth of adherent cells. By providing a large cultivation surface in bioreactor cultures, micro-carriers have replaced, to a great extent, cultivation in Cell Factory™ systems or roller bottles over the last decades. At Sanofi Pasteur, one of the world leaders in human vaccines, Cytodex™ 1 microcarriers have been used in the production of viral vaccines on Vero cells for several years. In accordance with the supplier’s recommendation, the microcarriers that are delivered dry are swollen in buffer, washed, and heat-sterilized before use. Since October 2016 a ready-to-use Cytodex™ 1 alternative, delivered presterilized by gamma irradiation, is available. Before implementing the change, the presterilized alternative was first evaluated with regards to reduced preparation time and cost. With a two-year shelf-life, the presterilized alternative reduced utility cost and added flexibility to operations by decreasing the need for steam and stainless steel materials in viral production facilities, and in alignment with extended use of single-use bioreactors equipment. The second step was to compare the cell growth and viral productivity using this ready-to-use alternative with that of the prior referenced product in place. Both cell growth and viral productivity were comparable between the two products, which supported further the documentation for the implementation of this ready-to-use alternative in GMP manufacturing for new R&D vaccine projects. The qualification process covered technical, quality, and analytical aspects based on the supplier documentation, and internal analyses and justification regarding our requirements in upstream vaccine production. While the presterilized Cytodex™ 1 microcarriers are now implemented in process development for new vaccines and qualified for manufacturing of clinical batches of new vaccine products, the next step will be to evaluate the benefits and impacts of replacing the microcarrier reference product with the gamma sterilized alternative on industrial products

the redox sensitive ape1 is a master cellular regulator for inflammatory pain condition

n/

Bilinear noise subtraction at the GEO 600 observatory

We develop a scheme to subtract off bilinear noise from the gravitational wave strain data and demonstrate it at the GEO 600 observatory. Modulations caused by test mass misalignments on longitudinal control signals are observed to have a broadband effect on the mid-frequency detector sensitivity ranging from 50 Hz to 500 Hz. We estimate this bilinear coupling by making use of narrow-band signal injections that are already in place for noise projection purposes. A coherent bilinear signal is constructed by a two-stage system identification process where the involved couplings are approximated in terms of stable rational functions. The time-domain filtering efficiency is observed to depend upon the system identification process especially when the involved transfer functions cover a large dynamic range and have multiple resonant features. We improve upon the existing filter design techniques by employing a Bayesian adaptive directed search strategy that optimizes across the several key parameters that affect the accuracy of the estimated model. The resulting post-offline subtraction leads to a suppression of modulation side-bands around the calibration lines along with a broadband reduction of the mid-frequency noise floor. The filter coefficients are updated periodically to account for any non-stationarities that can arise within the coupling. The observed increase in the astrophysical range and a reduction in the occurrence of non-astrophysical transients suggest that the above method is a viable data cleaning technique for current and future gravitational wave observatories

DNA-inspired Scheme for Building the Energy Profile of HPC Systems

International audienceEnergy usage is becoming a challenge for the design of next generation large scale distributed systems. This paper explores an inno- vative approach of profiling such systems. It proposes a DNA-like solution without making any assumptions on the running applications and used hardware. This profiling based on internal counters usage and energy monitoring allows to isolate specific phases during the execution and enables some energy consumption control and energy usage prediction. First experimental validations of the system modeling are presented and analyzed

A Runtime Framework for Energy Efficient HPC Systems Without a Priori Knowledge of Applications

International audienceThe rising computing demands of scientific endeavours often require the creation and management of High Performance Computing (HPC) systems for running experiments and processing vast amounts of data. These HPC systems generally operate at peak performance, consuming a large quantity of electricity, even though their workload varies over time. Understanding the behavioural patterns i.e., phases) of HPC systems during their use is key to adjust performance to resource demand and hence improve the energy efficiency. In this paper, we describe (i) a method to detect phases of an HPC system based on its workload, and (ii) a partial phase recognition technique that works cooperatively with on-the-fly dynamic management. We implement a prototype that guides the use of energy saving capabilities to demonstrate the benefits of our approach. Experimental results reveal the effectiveness of the phase detection method under real-life workload and benchmarks. A comparison with baseline unmanaged execution shows that the partial phase recognition technique saves up to 15% of energy with less than 1% performance degradation

Exploiting Performance Counters to Predict and Improve Energy Performance of HPC Systems

International audienceHardware monitoring through performance counters is available on almost all modern processors. Although these counters are originally designed for performance tuning, they have also been used for evaluating power consumption. We propose two approaches for modelling and understanding the behaviour of high performance computing (HPC) systems relying on hardware monitoring counters. We evaluate the effectiveness of our system modelling approach considering both optimising the energy usage of HPC systems and predicting HPC applications' energy consumption as target objectives. Although hardware monitoring counters are used for modelling the system, other methods -- including partial phase recognition and cross platform energy prediction -- are used for energy optimisation and prediction. Experimental results for energy prediction demonstrate that we can accurately predict the peak energy consumption of an application on a target platform; whereas, results for energy optimisation indicate that with no a priori knowledge of workloads sharing the platform we can save up to 24\% of the overall HPC system's energy consumption under benchmarks and real-life workloads

Beyond CPU Frequency Scaling for a Fine-grained Energy Control of HPC Systems

International audienceModern high performance computing subsystems (HPC) - including processor, network, memory, and IO - are provided with power management mechanisms. These include dynamic speed scaling and dynamic resource sleeping. Understanding the behavioral patterns of high performance computing systems at runtime can lead to a multitude of optimization opportunities including controlling and limiting their energy usage. In this paper, we present a general purpose methodology for optimizing energy performance of HPC systems consid- ering processor, disk and network. We rely on the concept of execution vector along with a partial phase recognition technique for on-the-fly dynamic management without any a priori knowledge of the workload. We demonstrate the effectiveness of our management policy under two real-life workloads. Experimental results show that our management policy in comparison with baseline unmanaged execution saves up to 24% of energy with less than 4% performance overhead for our real-life workloads

Targeting Neuroinflammation with Abscisic Acid Reduces Pain Sensitivity in Females and Hyperactivity in Males of An ADHD Mice Model

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental syndrome characterized by dopaminergic dysfunction. In this study, we aimed to demonstrate that there is a link between dopaminergic deficit and neuroinflammation that underlies ADHD symptoms. We used a validated ADHD mice model involving perinatal 6-OHDA lesions. The animals received abscisic acid (ABA), an anti-inflammatory phytohormone, at a concentration of 20 mg/L (drinking water) for one month. We tested a battery of behavior tests, learning and memory, anxiety, social interactions, and pain thresholds in female and male mice (control and lesioned, with or without ABA treatment). Postmortem, we analyzed microglia morphology and Ape1 expression in specific brain areas related to the descending pain inhibitory pathway. In females, the dopaminergic deficit increased pain sensitivity but not hyperactivity. In contrast, males displayed hyperactivity but showed no increased pain sensitivity. In females, pain sensitivity was associated with inflammatory microglia and lower Ape1 levels in the anterior cingulate cortex (ACC) and posterior insula cortex (IC). In addition, ABA treatment alleviated pain sensitivity concomitant with reduced inflammation and normalized APE1. In males, ABA reduced hyperactivity but had no significant effect on inflammation in these areas. This is the first study proving a sex-dependent association between dopamine dysfunction and inflammation in specific brain areas, hence leading to different behavioral outcomes in a mouse model of ADHD. These findings provide new clues for potential treatments for ADHD

Use of Dogs in the Mediation of Conservation Conflicts

Conflicts between wildlife and humans are of global importance and are increasing. These conflicts may negatively impact wildlife, humans, and other resources, primarily livestock. Human safety and economic well-being can be adversely impacted by depredation of livestock and perpetuation of wildlife-borne diseases in agricultural systems. Conversely, management approaches to mitigate these conflicts may employ primarily lethal control methods that can negatively impact wildlife populations of conservation importance. Dogs, principally livestock protection breeds, have been used for centuries in some cultures to protect livestock from predators. Dogs have also been used for a variety of other conservation-specific practices. Here we provide an overview of a chapter we developed on this topic for a book entitled Free-ranging Dogs and Wildlife Conservation, just released by Oxford University Press (2013). We will review past and current use of dogs for mediating wildlife-human conflict and highlight future areas of research that are needed to more effectively use dogs for mediating conservation conflicts