Beta-lactamase-producing mutants of Streptococcus cremoris

Penicillin-resistant mutants were isolated for six strains of Streptococcus cremoris used in commercial Cheddar cheese manufacture after treatment with N-methyl-N-nitro-N-nitrosoguanidine. The resistant mutants had an elevated minimal growth inhibitory concentration, 2.5 micrograms (4.13 units)/ml, for penicillin G and other beta-lactam antibiotics as compared with the penicillin-susceptible parent strains, which were each sensitive to .05 micrograms (.08 units)/ml. Penicillin resistance was due to the production of beta-lactamase. Plasmid DNA was not demonstrated in partially purified lysates of four mutants. Mutants had normal cellular morphology but altered phage sensitivity patterns. All except one strain were able to support complete phage adsorption. Resistance was retained after 20 passages in absence of penicillin.     

Temporal and Spatial Assessment of Supply and Demand of the Water-yield Ecosystem Service for Water Scarcity Management in Arid to Semi-arid Ecosystems

Water scarcity is one of the problems affecting people’s livelihoods in arid and semi-arid areas, requiring a sustainable balance between water demands and water resources. This study was carried out to assess temporal and spatial distribution of water supply and demand in order to help managers to overcome water scarcity in Jiroft basin, southeastern Iran. Spatial supply and demand of water were mapped and standardized rainfall index (SPI) was used to assess drought for a 20 years period (1994–2014). Supply and demand of water were matched in 23% of the basin area, mostly concentrated in the cold zones. Water supply was reduced up to 80% during dry years, declining water supply-demand matching to 5% of the basin area. Shrub-grass rangelands and deciduous woodlands were the most valuable land covers for conservation with $ 1,100 and $ 936 per hectare water prices respectively. Water value dropped more than 72% in mismanaged ecosystems (p?<?0.01). Our finding showed that water supply-demand ratio can be used as a proxy of ecosystem health and water-yield, which can provide a good information for water resources managers to reduce the threats of water scarcity in arid and semi-arid regions.

     

Design,Facile Synthesis and Characterization of Porphyrin-Zirconium-Ferrite@SiO2 Core-Shell and Catalytic Application in Cyclohexane Oxidation

Silicon - In this study, a new magnetic ZrFe2O4@SiO2-TCPP nanocatalyst with high efficiency was used for the oxidation of cyclohexane to cyclohexanone (Ke) and cyclohexanol (Al). The mesoporous...     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

Analytics of learning tactics and strategies in an online learnersourcing environment

Background

The use of crowdsourcing in a pedagogically supported form to partner with learners in developing novel content is emerging as a viable approach for engaging students in higher-order learning at scale. However, how students behave in this form of crowdsourcing, referred to as learnersourcing, is still insufficiently explored.

Objectives

To contribute to filling this gap, this study explores how students engage with learnersourcing tasks across a range of course and assessment designs.

Methods

We conducted an exploratory study on trace data of 1279 students across three courses, originating from the use of a learnersourcing environment under different assessment designs. We employed a new methodology from the learning analytics (LA) field that aims to represent students' behaviour through two theoretically-derived latent constructs: learning tactics and the learning strategies built upon them.

Results

The study's results demonstrate students use different tactics and strategies, highlight the association of learnersourcing contexts with the identified learning tactics and strategies, indicate a significant association between the strategies and performance and contribute to the employed method's generalisability by applying it to a new context.

Implications

This study provides an example of how learning analytics methods can be employed towards the development of effective learnersourcing systems and, more broadly, technological educational solutions that support learner-centred and data-driven learning at scale. Findings should inform best practices for integrating learnersourcing activities into course design and shed light on the relevance of tactics and strategies to support teachers in making informed pedagogical decisions.     

Biosynthesis of SeNPs by Mycobacterium bovis and their enhancing effect on the immune response against HBs antigens: an in vivo study

Conventional hepatitis B vaccine contains alum but is less effective to induce Th1 response. Selenium nanoparticles and Bacillus Calmette–Guerin were reported as immune modulators. In this study, SeNPs were extracted from Mycobacterium bovis and characterised. SeNPs were mixed with HBs‐Ag and administered to the mice to investigate he immune response pattern. With an addition of Se ions at a sub‐inhibitory concentration to the Sauton medium broth after 24 h, SeNPs were extracted from M. bovis and characterised by Fourier transform infrared spectroscopy, dynamic light scattering, atomic forcemicroscopy, energy dispersive X‐ray spectrum, transmission electron microscopy, and thermogravimetric analysis. Furthermore, female inbred BALB/c mice were injected subcutaneously on the first, 14th, 28th day with 100 and 200 µg doses of that SeNPs supplemented with HBs‐Ag vaccine. Later, the total antibody, isotypes of Immunoglobulin G1, Interlukin 4, and interferon‐γ were measured by enzye‐linked immunosorbent assay. The size of the SeNPs was <150 nm. Level of total antibody and immunoglobulin G2a increased significantly in the group that received 200 µg/ml nano selenium extracted from M. bovis. SeNPs in dose of 200 µg coated with organic materials of M. bovis could induce an influential immune response in relation to the conventional HBs‐Ag vaccine.Inspec keywords: selenium, nanoparticles, nanofabrication, microorganisms, Fourier transform spectra, infrared spectra, light scattering, atomic force microscopy, X‐ray chemical analysis, transmission electron microscopy, thermal analysis, nanomedicineOther keywords: biosynthesis, selenium nanoparticles, Mycobacterium bovis, enhnced immune response, HBs antigens, Bacillus Calmette‐Guerin, immune modulators, biogenic SeNPs, immune response pattern, Sauton medium broth, Fourier transform infrared spectroscopy, dynamic light scattering, atomic force microscopy, energy dispersive X‐ray spectrum, transmission electron microscopy, thermogravimetric analysis, female inbred BALB/c mice, antibody, IgG1 isotype, IgG2a isotype, IL₄ isotype, interferon‐γ, enzyme‐linked immunosorbent assay, bacterium, time 24 h, time 14 day, time 28 day, Se     

Design of laminated composite structures for optimum fiber direction and layer thickness,using optimality criteria

In this study, two optimality criteria are presented for optimum design of composite laminates using finite element method. Thickness of the layers and fiber orientation angles in each finite element are considered as the design variables. It will be shown that the optimum design of composite laminates with varying fiber orientations and layers thicknesses may be found by using these optimality criteria in an efficient way, without performing the sensitivity analysis.     

Measurement and mathematical modelling of nutrient level and water quality parameters

Physico-chemical water quality parameters and nutrient levels such as water temperature, turbidity, saturated oxygen, dissolved oxygen, pH, chlorophyll-a, salinity, conductivity, total nitrogen and total phosphorus, were measured from April to September 2011 in the Karaj dam area, Iran. Total nitrogen in water was modelled using an artificial neural network system. In the proposed system, water temperature, depth, saturated oxygen, dissolved oxygen, pH, chlorophyll-a, salinity, turbidity and conductivity were considered as input data, and the total nitrogen in water was considered as output. The weights and biases for various systems were obtained by the quick propagation, batch back propagation, incremental back propagation, genetic and Levenberg-Marquardt algorithms. The proposed system uses 144 experimental data points; 70% of the experimental data are randomly selected for training the network and 30% of the data are used for testing. The best network topology was obtained as (9-5-1) using the quick propagation method with tangent transform function. The average absolute deviation percentages (AAD%) are 2.329 and 2.301 for training and testing processes, respectively. It is emphasized that the results of the artificial neural network (ANN) model are compatible with the experimental data.     

Analysis of a clock synchronization protocol for wireless sensor networks

The Dutch company Chess develops a wireless sensor network (WSN) platform using an epidemic communication model. One of the greatest challenges in the design is to find suitable mechanisms for clock synchronization. In this paper, we study a proposed clock synchronization protocol for the Chess platform. First, we model the protocol as a network of timed automata and verify various instances using the Uppaal model checker. Next, we present a full parametric analysis of the protocol for the special case of cliques (networks with full connectivity), that is, we give constraints on the parameters that are both necessary and sufficient for correctness. These results have been checked using the proof assistant Isabelle. We report on the exhaustive analysis of the protocol for networks with four nodes, and we present a negative result for the special case of line topologies: for any instantiation of the parameters, the protocol will eventually fail if the network grows.