Antimicrobial effect of different herbal plant extracts against different microbial population

This study evaluates the antimicrobial effects of ethanolic extract of five herbal plants; Guava (Psidium guajava), Sage (Salvia officinalis), Rhamnus (Ziziphusspina Christi), Mulberry (Morusalba L.), and Olive (Oleaeuropaea L) leaves against several microbial population representing Gram positive, Gram negative and Mollicutes; S. aureus, E. coli, Pasteurella multocida, B. cereus, Salmonella Enteritidis and M. gallisepticum using standard agar disc diffusion technique and minimal inhibitory concentration (MIC). Different extracts reveal variable results against the microorganism under study. All extracts have no antibacterial potency for Mycoplasma gallisepticum except Psidium guajava. The results of minimal inhibitory concentration (MIC) and Minimum bactericidal concentration (MBC) of the extracts against the six bacteria ranged from 625 to 5000 μg/ml. The used herbal extract could inhibit the selected microorganism under study with variable minimal inhibitory concentration (MIC) and minimum bactericidal concentration (MBC).     

Model‐assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways

Efficient approaches to increase plant lipid production are necessary to meet current industrial demands for this important resource. While Jatropha curcas cell culture can be used for in vitro lipid production, scaling up the system for industrial applications requires an understanding of how growth conditions affect lipid metabolism and yield. Here we present a bottom‐up metabolic reconstruction of J. curcas supported with labeling experiments and biomass characterization under three growth conditions. We show that the metabolic model can accurately predict growth and distribution of fluxes in cell cultures and use these findings to pinpoint energy expenditures that affect lipid biosynthesis and metabolism. In addition, by using constraint‐based modeling approaches we identify network reactions whose joint manipulation optimizes lipid production. The proposed model and computational analyses provide a stepping stone for future rational optimization of other agronomically relevant traits in J. curcas.     

Straightforward method for calibration of mechanistic cation exchange chromatography models for industrial applications

Mechanistic modeling of chromatography processes is one of the most promising techniques for the digitalization of biopharmaceutical process development. Possible applications of chromatography models range from in silico process optimization in early phase development to in silico root cause investigation during manufacturing. Nonetheless, the cumbersome and complex model calibration still decelerates the implementation of mechanistic modeling in industry. Therefore, the industry demands model calibration strategies that ensure adequate model certainty in a limited amount of time. This study introduces a directed and straightforward approach for the calibration of pH-dependent, multicomponent steric mass action (SMA) isotherm models for industrial applications. In the case investigated, the method was applied to a monoclonal antibody (mAb) polishing step including four protein species. The developed strategy combined well-established theories of preparative chromatography (e.g. Yamamoto method) and allowed a systematic reduction of unknown model parameters to 7 from initially 32. Model uncertainty was reduced by designing two representative calibration experiments for the inverse estimation of remaining model parameters. Dedicated experiments with aggregate-enriched load material led to a significant reduction of model uncertainty for the estimates of this low-concentrated product-related impurity. The model was validated beyond the operating ranges of the final unit operation, enabling its application to late-stage downstream process development. With the proposed model calibration strategy, a systematic experimental design is provided, calibration effort is strongly reduced, and local minima are avoided.     

Tropical Bacillus Strains Inoculation Enhances Maize Root Surface Area,Dry Weight,Nutrient Uptake and Grain Yield

Journal of Plant Growth Regulation - The rising demand for agricultural commodities in developing countries has put increasing pressure on land resources for higher yields, with associated growth...     

Comparaison de l’estimation du débit de filtration glomérulaire par le 99mTc-DTPA au radiotraceur de référence le 51Cr-EDTA comparativement aux méthodes basées sur la mesure de la créatininémie

Since the end of 2018, the distribution of the reference tracer for the measurement of glomerular filtration rate (GFR), the 51Cr-EDTA, is no longer provided by radiopharmaceutical companies around the world. In this study, we propose to compare the measurement of glomerular filtration rate by 99mTc-DTPA to that by 51Cr-EDTA. A double estimation of GFR by plasma clearance was performed in 12 patients, 10 of which were referred for GFR calculation prior to possible kidney donation. Linear regression coefficients and intraclass correlation coefficient (ICC) were calculated for the GFR measurement by 99mTc-DTPA, and by MDRD, CKD-EPI and Cockcroft and Gault formulas, relative to the 51Cr-EDTA measurement. The clearance measurement with 99mTc-DTPA is on average 7.25 [2.00; 14.96] mL/min/1.73m² higher than that of 51Cr-EDTA. The GFR measurement with 99mTc-DTPA showed a trend towards better agreement with the 51Cr-EDTA measurement in terms of linear regression parameters, but also in terms of ICC compared to the MDRD, CKD-EPI and Cockcroft and Gault methods. In conclusion, our study supports the use of the 99mTc-DTPA tracer in place of 51Cr-EDTA and shows a higher reliability compared to methods based on blood creatinine measurement.     

A new cloud-based classification methodology (CBCM) for efficient semantic web service discovery

Over the last decades, web services are used for performing specific tasks demanded by users. The most important task of service’s classification system is to match an anonymous input service with the stored pre-classified web services. The most challenging issue is that web services are currently organized and classified according to syntax while the context of the requested service is ignored. Due to this motivation, Cloud-based Classification Methodology is proposed as it presents a new methodology based on semantic web service’s classification. Furthermore, cloud computing is used for not only storing but also allocating the high scale of web services with both high availability and accessibility. Fog technology is employed to reduce the latency and to speed up response time. The experimental results using the suggested methodology show a better performance of the proposed system regarding both precision and accuracy in comparison with most of the methods discussed in the literature of the current study.

     

Resource allocation mechanisms for maximizing provider’s revenue in infrastructure as a service (IaaS) cloud

Infrastructure as a Service (IaaS) is a cloud computing service provided over the internet to facilitate the provisioning of various services such as storage, processes, etc. The provider in the IaaS market may offer some purchasing plans including: reservation, on-demand, and spot plans for its resources. As in real scenarios, demand volume for each plan is assumed to be a random variable with a given probability distribution. The provider maximizes its average revenue in the long run by optimal allocation of its resources among the plans. We formulate an Integer Linear Programming (ILP) model with a stochastic constraint, to determine the number of resources to be allocated for each plan in every time slot in the planning horizon. First, fixed prices are considered for each plan, then two mechanisms of Continuous Double Auction and Second Price Sealed Bid Auction are considered for reservations and spot plans, respectively, to obtain market-driven prices of the services. The Seasonal Weighted Moving Average method is used to predict the amount of demand in every slot. Finally, the proposed mechanisms are evaluated through simulations and the results confirm the effectiveness of the methods in maximizing the revenue and overall utilization of the available IaaS capacity.

     

Advances in approaches to seagrass restoration in Australia

Three case studies involving two temperate Australian seagrass species – Pondweed (Ruppia tuberosa) and Ribbon Weed (Posidonia australis) – highlight different approaches to their restoration. Seeds and rhizomes were used in three collaborative programmes to promote new approaches to scale up restoration outcomes.