New type of vulnerability curve gives insight in the hydraulic capacitance and conductivity of the xylem

Drought vulnerability of trees and other woody plants is much debated in the context of climate change, which creates a high interest in understanding plant water relations. The role and functioning of internal water storage is crucial, but still insufficiently understood. Drought vulnerability is typically assessed by considering loss in conductivity in function of decreasing xylem water potential, in a so-called ‘vulnerability curve’. The xylem water potential at which a certain percentage of conductivity is lost (usually 50%) gives an indication of the vulnerability to cavitation. In a ‘desorption curve’, we can examine the release of water from internal storage tissues with decreasing water potential. Both curves are very valuable, but rely on a sequence of manual measurements (xylem water potential, hydraulic conductivity and water content) and are time-consuming. Therefore, we propose a new type of vulnerability curve that is based on continuous measurements of diameter shrinkage and ultrasonic acoustic emissions (UAE). We monitored weight loss, xylem diameter shrinkage and UAE and measured xylem water potential during the dehydration of excised branches of Vitis vinifera L. ‘Johanniter’. The vulnerability curves could be interpreted in terms of water loss in elastic and inelastic tissues. The proposed method can be a tool to assess hydraulic capacitance and conductivity of the xylem

Evaluation of medication adherence methods in the treatment of malaria in Rwandan infants

Objectives: To compare three methods for evaluating treatment adherence in a 7-day controlled treatment period for malaria in children in Rwanda. Methods: Fifty-six children (< 5 years) with malaria were recruited at the University Hospital of Butare, Rwanda. Patients were treated with quinine sulfate, taste-masked, pellets during seven days: three days in hospital (in-patient) followed by a four-day out-patient period. Three methods to evaluate medication adherence among patients were compared: manual pill count of returned tablets, patient self-report and electronic pill-box monitoring. These pill-boxes were equipped with a microchip registering date and time of every opening. Medication adherence was defined as the proportion of prescribed doses taken. The inter-dose intervals were analysed as well. Results: Medication adherence data were available for 54 of the 56 patients. Manual pill count and patient self-report yielded a medication adherence of 100% for the in-and out-patient treatment periods. Based on electronic pill-box monitoring, medication adherence during the seven-day treatment period was 90.5 +/- 8.3%. Based on electronic pillbox monitoring inpatient medication adherence (99.3 +/- 2.7%) was markedly higher (p < 0.03) than out-patient adherence (82.7 +/- 14.7%), showing a clear difference between health workers' and consumers' medication adherence. Conclusion: Health workers' medication adherence was good. However, a significant lower medication adherence was observed for consumers' adherence in the outpatient setting. This was only detected by electronic pill-box monitoring. Therefore, this latter method is more accurate than the two other methods used in this study

COMAND - A Distributed Configuration Management Framework

Software development is becoming a more and more distributed process, which urgently needs supporting tools in the field of configuration management, software process/w orkflow management, communication and problem tracking. In this paper we present a new distributed software configuration management framework COMAND. It offers high availabilit y through replication and a mechanism to easily change and adapt the project structure to new business needs. To better understand and formally prove some properties of COMAND, we have modeled it in a formal technique based on distributed graph transformations. This formalism provides an intuitive rule-based description technique mainly for the dynamic behavior of the system on an abstract level. We use it here to model the replication subsystem

Learning Long-Term Style-Preserving Blind Video Temporal Consistency

When trying to independently apply image-trained algorithms to successive frames in videos, noxious flickering tends to appear. State-of-the-art post-processing techniques that aim at fostering temporal consistency, generate other temporal artifacts and visually alter the style of videos. We propose a postprocessing model, agnostic to the transformation applied to videos (e.g. style transfer, image manipulation using GANs, etc.), in the form of a recurrent neural network. Our model is trained using a Ping Pong procedure and its corresponding loss, recently introduced for GAN video generation, as well as a novel style preserving perceptual loss. The former improves long-term temporal consistency learning, while the latter fosters style preservation. We evaluate our model on the DAVIS and videvo.net datasets and show that our approach offers state-of-the-art results concerning flicker removal, and better keeps the overall style of the videos than previous approaches