Designing an artificial neural network for prediction of pregnancy outcomes in women with systemic lupus erythematosus in Iran

Background: Pregnancy in women with systemic lupus erythematosus (SLE) is still introduced as a major challenge. Consulting before pregnancy in these patients is essential in order to estimating the risk of undesirable maternal and fetal outcomes by using appropriate information. The purpose of this study was to develop an artificial neural network for prediction of pregnancy outcomes including spontaneous abortion and live birth in SLE. Methods: In a retrospective study, forty-five variables were identified as effective factors for prediction of pregnancy outcomes in systemic lupus erythematosus. Data of 104 pregnancies in women with systemic lupus erythematosus in Shariati Hospital and 45 pregnancies in a private specialized center in Tehran from 1982 to 2014 in August and September, 2014 were collected and analyzed. For feature selection, information of the 149 pregnancies was analyzed with a binary logistic regression model in SPSS software, version 20 (SPSS, Inc., Chicago, IL, USA). These selected variables were used for inputs of neural networks in MATLAB software, version R2013b (MathWorks Inc., Natick, MA, USA). A Multi-Layer Perceptron (MLP) network with scaled conjugate gradient (trainscg) back propagation learning algorithm has been designed and evaluated for this purpose. We used confusion matrix for evaluation. The accuracy, sensitivity and specificity were calculated from the confusion matrix. Results: Twelve features with P<0.05 and four features with P<0.1 were identified by using binary logistic regression as effective features. These sixteen features were used as input variables in artificial neural networks. The accuracy, sensitivity and specificity of the test data for the MLP network were 90.9, 80.0, and 94.1 respectively and for the total data were 97.3, 93.5, and 99.0 respectively. Conclusion: According to the results, we concluded that feed-forward Multi-Layer Perceptron (MLP) neural network with scaled conjugate gradient (trainscg) back propagation learning algorithm can help physicians to predict the pregnancy outcomes (spontaneous abortion and live birth) among pregnant women with lupus by using identified effective variables. © 2015, Tehran University of Medical Sciences. All rights reserved

Seasonality buffers carbon budget variability across heterogeneous landscapes in Alaskan Arctic tundra

Arctic tundra exhibits large landscape heterogeneity in microtopography, hydrology, and active layer depth. While many carbon flux measurements and experiments are done at or below the mesoscale (≤1 km), modern ecosystem carbon modeling is often done at scales of 0.25°–1.0° latitude, creating a mismatch between processes, process input data, and verification data. Here we arrange the naturally complex terrain into mesoscale landscape types of varying microtopography and moisture status to evaluate how landscape types differ in terms of CO2 and CH4 balances and their combined warming potential, expressed as CO2 equivalents (CO2-eq). Using a continuous 4 year dataset of CO2 and CH4 fluxes obtained from three eddy covariance (EC) towers, we investigate the integrated dynamics of landscape type, vegetation community, moisture regime, and season on net CO2 and CH4 fluxes. EC towers were situated across a moisture gradient including a moist upland tundra, a heterogeneous polygon tundra, and an inundated drained lake basin. We show that seasonal shifts in carbon emissions buffer annual carbon budget differences caused by site variability. Of note, high growing season gross primary productivity leads to higher fall zero-curtain CO2 emissions, reducing both variability in annual budgets and carbon sink strength of more productive sites. Alternatively, fall zero-curtain CH4 emissions are equal across landscape types, indicating site variation has little effect on CH4 emissions during the fall despite large differences during the growing season. We find that the polygon site has the largest mean warming potential (107 ± 8.63 g C–CO2-eq m−2 yr−1) followed by the drained lake basin site (82.12 ± 9.85 g C–CO2-eq m−2 yr−1) and the upland site (77.19 ± 21.8 g C–CO2-eq m−2 yr−1), albeit differences were not significant. The highest temperature sensitivities are also at the polygon site with mixed results between CO2 and CH4 at the other sites. Results show a similar mean annual net warming effect across dominant landscape types but that these landscape types vary significantly in the amounts and timing of CO2 and CH4 fluxes

Enhanced cosmic-ray flux toward zeta Persei inferred from laboratory study of H3+ - e- recombination rate

The H3+ molecular ion plays a fundamental role in interstellar chemistry, as it initiates a network of chemical reactions that produce many interstellar molecules. In dense clouds, the H3+ abundance is understood using a simple chemical model, from which observations of H3+ yield valuable estimates of cloud path length, density, and temperature. On the other hand, observations of diffuse clouds have suggested that H3+ is considerably more abundant than expected from the chemical models. However, diffuse cloud models have been hampered by the uncertain values of three key parameters: the rate of H3+ destruction by electrons, the electron fraction, and the cosmic-ray ionisation rate. Here we report a direct experimental measurement of the H3+ destruction rate under nearly interstellar conditions. We also report the observation of H3+ in a diffuse cloud (towards zeta Persei) where the electron fraction is already known. Taken together, these results allow us to derive the value of the third uncertain model parameter: we find that the cosmic-ray ionisation rate in this sightline is forty times faster than previously assumed. If such a high cosmic-ray flux is indeed ubiquitous in diffuse clouds, the discrepancy between chemical models and the previous observations of H3+ can be resolved.Comment: 6 pages, Nature, in pres

Разработка веб-приложения для моделирования распространения загрязняющих веществ в атмосферном воздухе от животноводческих предприятий

Целью данной работы является разработка веб-приложения для моделирования распространения загрязняющих веществ в атмосферном воздухе от животноводческих предприятий с использованием геоинформационной системы OpenStreetMap. Объектом исследования является картографическое моделирование распространения загрязняющих веществ в атмосферном воздухе. В отчете приведены результаты анализа предметной области, проектирования, программной реализации и практического применения веб-приложения для моделирования распространения загрязняющих веществ в атмосферном воздухе от животноводческих предприятий с отображением результатов на карте.The aim of this work is to develop a web-application for modeling the distribution of pollutants in the air from livestock enterprises using the OpenStreetMap geographic information system. The object of research is cartographic modeling of the distribution of pollutants in the air. The report presents the results of the analysis of the subject area, design, software implementation and practical application of the web-application for modeling the distribution of pollutants in the air from livestock enterprises with the results displayed on the map

Experimental design, modeling and mechanism of cationic dyes biosorption on to magnetic chitosan-lutaraldehyde composite

Magnetic separation of toxic dyes has become a potential and effective method in wastewater treatments. In present research, a facile in situ one step co-precipitation synthetic approach is used to develop water-dispersible Fe 3 O 4 /Chitosan/Glutaraldehyde nanocomposites (MCS-GA) as an efficient adsorbent for the removal of Crystal Violet (CV) from aqueous solution. The physicochemical properties of the MCS-GA were investigated using FTIR, SEM, TEM, XRD, BET, and VSM techniques. 5-level and 3-factors central composite design (CCD) combined with the response surface methodology (RSM) was applied to investigate the statistical relationships between independent variables i.e. initial pH, adsorbent dosage, initial dye concentration and adsorption process as response. The optimal values of the parameters for the best efficiency (99.99) were as follows: pH of 11, the initial dye concentration of 60 mg L �1 and MCS-GA dosage of 0.817 g L �1 , respectively. The adsorption equilibrium and kinetic data were fitted with the Langmuir monolayer isotherm model (q max : 105.467 mg g �1 , R 2 : 0.996) and pseudo-second order kinetics (R 2 : 0.960). Thermodynamic parameters (R 2 &gt; 0.941, �H°: 690.609�896.006 kJ mol �1 , �G°: �1.6849 to �13.4872 kJ mol �1 , �S°: 0.168�0.232 kJ mol �1 K �1 ) also indicated CV adsorption is feasible, spontaneous and endothermic in nature. Overall, taking into account the excellent efficiency, good regeneration and acceptable performance in real terms, MCS-GA can be introduced as a promising absorbent for dyes removal from the textile wastewater. © 2019 Elsevier B.V

Enhancing somatic embryogenesis of Malaysian rice cultivar MR219 using adjuvant materials in a high efficiency protocol

Enhancing of the efficient tissue culture protocol for somatic embryos would facilitate the engineered breeding plants program. In this report, we describe the reproducible protocol of Malaysian rice (Oryza sativa L.) cultivar MR219 through somatic embryogenesis. Effect of a wide spectrum of exogenesis materials was assessed in three phases, namely callogenesis, proliferation and regeneration. Initially, rice seeds were subjected under various auxin treatments. Secondly, the effect of different concentrations of 2,4-D on callus induction was evaluated. In the next step, the efficiency of different explants was identified. Subsequently, the effects of different auxins, cytokinins, L-proline, casein hydrolysate and potassium metasilicate concentrations on the callus proliferation and regeneration were considered. For the callogenesis phase, 2 mg L-1 of 2,4-D and roots were chosen as the best auxin and explant. In the callus proliferation stage, the highest efficiency was observed at week eight in the MS media supplemented with 2 mg L-1 of 2,4-D, 2 mg L-1 of kinetin, 50 mg L-1 of L-proline, 100 mg L-1 of casein hydrolysate and 30 mg L-1 of potassium metasilicate. In the last phase of the research, the MS media added with 3 mg L-1 of kinetin, 30 mg L-1 of potassium metasilicate and 2 mg L-1 of NAA were selected. Meanwhile, to promote the roots of regenerated explants, 0.4 mg L-1 of IBA has shown potential as an appropriate activator

Earlier snowmelt may lead to late season declines in plant productivity and carbon sequestration in Arctic tundra ecosystems

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season

Earlier snowmelt may lead to late season declines in plant productivity and carbon sequestration in Arctic tundra ecosystems

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.Peer reviewe

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)