Human embryo polarization requires PLC signaling to mediate trophectoderm specification

Apico-basal polarization of cells within the embryo is critical for the segregation of distinct lineages during mammalian development. Polarized cells become the trophectoderm (TE), which forms the placenta, and apolar cells become the inner cell mass (ICM), the founding population of the fetus. The cellular and molecular mechanisms leading to polarization of the human embryo and its timing during embryogenesis have remained unknown. Here, we show that human embryo polarization occurs in two steps: it begins with the apical enrichment of F-actin and is followed by the apical accumulation of the PAR complex. This two-step polarization process leads to the formation of an apical domain at the 8–16 cell stage. Using RNA interference, we show that apical domain formation requires Phospholipase C (PLC) signaling, specifically the enzymes PLCB1 and PLCE1, from the eight-cell stage onwards. Finally, we show that although expression of the critical TE differentiation marker GATA3 can be initiated independently of embryo polarization, downregulation of PLCB1 and PLCE1 decreases GATA3 expression through a reduction in the number of polarized cells. Therefore, apical domain formation reinforces a TE fate. The results we present here demonstrate how polarization is triggered to regulate the first lineage segregation in human embryos

Insulin Production and Signaling in Renal Tubules of Drosophila Is under Control of Tachykinin-Related Peptide and Regulates Stress Resistance

The insulin-signaling pathway is evolutionarily conserved in animals and regulates growth, reproduction, metabolic homeostasis, stress resistance and life span. In Drosophila seven insulin-like peptides (DILP1-7) are known, some of which are produced in the brain, others in fat body or intestine. Here we show that DILP5 is expressed in principal cells of the renal tubules of Drosophila and affects survival at stress. Renal (Malpighian) tubules regulate water and ion homeostasis, but also play roles in immune responses and oxidative stress. We investigated the control of DILP5 signaling in the renal tubules by Drosophila tachykinin peptide (DTK) and its receptor DTKR during desiccative, nutritional and oxidative stress. The DILP5 levels in principal cells of the tubules are affected by stress and manipulations of DTKR expression in the same cells. Targeted knockdown of DTKR, DILP5 and the insulin receptor dInR in principal cells or mutation of Dilp5 resulted in increased survival at either stress, whereas over-expression of these components produced the opposite phenotype. Thus, stress seems to induce hormonal release of DTK that acts on the renal tubules to regulate DILP5 signaling. Manipulations of S6 kinase and superoxide dismutase (SOD2) in principal cells also affect survival at stress, suggesting that DILP5 acts locally on tubules, possibly in oxidative stress regulation. Our findings are the first to demonstrate DILP signaling originating in the renal tubules and that this signaling is under control of stress-induced release of peptide hormone

Effectiveness of breeding selection for grain quality in common bean.

he aims of this study were to investigate the genetic variability and the genotype × environment interaction for quality and yield traits in common bean (Phaseolus vulgaris L.), to evaluate the degree of informativeness of the evaluations of grain quality in only one environment, to estimate genetic parameters for grain quality traits, and to select lines with superior grain quality. We evaluated 81 carioca common bean lines in preliminary line trials in several environments for nutritional, technological, and commercial quality and selected the 20 superior lines, which were evaluated in validation trials in nine environments. Individual and combined ANOVAs were performed for all the traits. Correlations were estimated between Fe and Zn concentrations and yield; adaptability and phenotypic stability were analyzed; and superior genotypes were selected based on the Mulamba & Mock index. It is possible to increase the Fe, Zn, and crude protein concentrations and reduce cooking time; however, increasing crude fiber is a challenge. Preliminary evaluation of the quality traits in only one environment was effective and sufficient for selection of genotypes superior in Fe concentration, crude fiber, crude protein, and cooking time; and genetic gains can be obtained from selection for these traits. Genetic and phenotypic correlations were observed between Fe and Zn concentrations. The lines CNFC 16627, CNFC 16518, CNFC 16602, CNFC 16615, and CNFC 16520 are superior based on the selection index and are recommended for breeding for grain quality in carioca common bean

Estimating the burden of antimicrobial resistance: a systematic literature review.

Background: Accurate estimates of the burden of antimicrobial resistance (AMR) are needed to establish the magnitude of this global threat in terms of both health and cost, and to paramaterise cost-effectiveness evaluations of interventions aiming to tackle the problem. This review aimed to establish the alternative methodologies used in estimating AMR burden in order to appraise the current evidence base. Methods: MEDLINE, EMBASE, Scopus, EconLit, PubMed and grey literature were searched. English language studies evaluating the impact of AMR (from any microbe) on patient, payer/provider and economic burden published between January 2013 and December 2015 were included. Independent screening of title/abstracts followed by full texts was performed using pre-specified criteria. A study quality score (from zero to one) was derived using Newcastle-Ottawa and Philips checklists. Extracted study data were used to compare study method and resulting burden estimate, according to perspective. Monetary costs were converted into 2013 USD. Results: Out of 5187 unique retrievals, 214 studies were included. One hundred eighty-seven studies estimated patient health, 75 studies estimated payer/provider and 11 studies estimated economic burden. 64% of included studies were single centre. The majority of studies estimating patient or provider/payer burden used regression techniques. 48% of studies estimating mortality burden found a significant impact from resistance, excess healthcare system costs ranged from non-significance to $1 billion per year, whilst economic burden ranged from$21,832 per case to over $3 trillion in GDP loss. Median quality scores (interquartile range) for patient, payer/provider and economic burden studies were 0.67 (0.56-0.67), 0.56 (0.46-0.67) and 0.53 (0.44-0.60) respectively. Conclusions: This study highlights what methodological assumptions and biases can occur dependent on chosen outcome and perspective. Currently, there is considerable variability in burden estimates, which can lead in-turn to inaccurate intervention evaluations and poor policy/investment decisions. Future research should utilise the recommendations presented in this review. Trial registration: This systematic review is registered with PROSPERO (PROSPERO CRD42016037510)

Formulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couples

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000229277400018This paper presents a new approach based on artificial neural networks (ANNs) to determine the properties of liquid and two phase boiling and condensing of two alternative refrigerant/absorbent couples (methanol/LiBr and methanol/LiCl). These couples do not cause ozone depletion and use in the absorption thermal systems (ATSs). ANN's are able to learn the key information patterns within multidimensional information domain. ANNs operate such as a 'black box' model, requiring no detailed information about the system. On the other hand, they learn the relationship between the input and the output. In order to train the neural network, limited experimental measurements were used as training data and test data. In this study, in input layer, there are temperatures in the range of 298-498 K, pressures (0.1-40 MPa) and concentrations of 2%, 7%, 12% of the couples; specific volume is in output layer. The back-propagation learning algorithm with three different variants, namely scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM), and logistic sigmoid transfer function were used in the network so that the best approach can find. The most suitable algorithm and neuron number in the hidden layer are found as SCG with 8 neurons. For this number level, after the training, it is found that maximum error is less than 3%, average error is about 1% and R-2 value are 99.999%. As seen from the results obtained the thermodynamic equations for each pair by using the weights of network have been obviously predicted within acceptable errors. This paper shows that values predicted with ANN can be used to define the thermodynamic properties instead of approximate and complex analytic equations. (c) 2004 Elsevier Ltd. All rights reserved

Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data

ARCAKLIOGLU, Erol/0000-0001-8073-5207WOS: 000223035700015Turkey is located at the Mediterranean at 36degrees and 42degrees N latitudes and has a typical Mediterranean climate. The solar energy potential is very high in Turkey. The yearly average solar radiation is 3.6 kW h/m(2) day, and the total yearly radiation period is similar to2610 h. This study consists of two cases. Firstly, the main focus of this study is to put forward the solar energy potential in Turkey using artificial neural networks (ANNs). Secondly, in this study, the best approach was investigated for each station by using different learning algorithms and a logistic sigmoid transfer function in the neural network with developed software. In order to train the neural network, meteorological data for last three years (2000-2002) from 17 stations (Ankara, Samsun, Edirne, Istanbul-Goztepe, Van, Izmir, Denizli, Sanliurfa, Mersin, Adana, Gaziantep, Aydin, Bursa, Diyarbakir, Yozgat, Antalya and Mugla) spread over Turkey were used as training (11 stations) and testing (6 stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration and mean temperature) are used in the input layer of the network. Solar radiation is in the output layer. The maximum mean absolute percentage error was found to be less than 6.735% and R-2 values were found to be about 99.893% for the testing stations. However, these values were found to be 4.398% and 99.965% for the training stations. The trained and tested ANN models show greater accuracy for evaluating the solar resource possibilities in regions where a network of monitoring stations has not been established in Turkey. The predicted solar potential values from the ANN are given in the form of monthly maps. These maps are of prime importance for different working disciplines, like scientists, architects, meteorologists and solar engineers, in Turkey. The predictions from the ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the best solar technology. (C) 2004 Elsevier Ltd. All rights reserved

Performance analysis of ejector absorption heat pump using ozone safe fluid couple through artificial neural networks

ARCAKLIOGLU, Erol/0000-0001-8073-5207;WOS: 000221590600020Thermodynamic analysis of absorption thermal systems is too complex because the analytic functions calculating the thermodynamic properties of fluid couples involve the solution of complex differential equations and simulation programs. This study aims at easing this complex situation and consists of three cases: (1) A special ejector, located at the absorber inlet, instead of the common location at the condenser inlet, to increase overall performance was used in the ejector absorption beat pump (EAHP). The ejector has two functions: Firstly, it aids the pressure recovery from the evaporator and then upgrades the mixing process and pre-absorption by the weak solution of the methanol coming from the evaporator. (ii) Use of artificial neural networks (ANNs) has been proposed to determine the properties of the liquid and two phase boiling and condensing of an alternative working fluid couple (methanol/LiCl), which does not cause ozone depletion. (iii) A comparative performance study of the EAHP was performed between the analytic functions and the values predicted by the ANN for the properties of the couple. The back propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. In the input layer, there are temperature, pressure and concentration of the couples. Specific volume is in the output layer. After training, it was found that the maximum error was less than 3%, the average error was less than 1.2% and the R-2 values were about 0.9999. Additionally, in comparison of the analysis results between analytic equations obtained by using experimental data and by means of the ANN, the deviations of the refrigeration effectiveness of the system for cooling (COPr), exergetic coefficient of performance of the system for cooling (ECOPr) and circulation ratio (F) for all working temperatures were found to be less than 1.7%, 5.1%, and 1.9%, respectively. Deviations for COPr, ECOPr and F at a generator temperature of similar to90 degreesC (cut off temperature) at which the coefficient of performance of the system is maximum are 0.9%, 1.8%, and 0.1%, respectively, for other working temperatures. When this system was used for heating, similar deviations were obtained. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable uncertainties. The results showed that the use of ANNs for determination of thermodynamic properties is acceptable in design of the EAHP. (C) 2003 Elsevier Ltd. All rights reserved

Turkey's net energy consumption

ARCAKLIOGLU, Erol/0000-0001-8073-5207;WOS: 000228757300007The main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using an artificial neural-network (ANN) technique in order to determine the future level of energy consumption in Turkey. In this study, two different models were used in order to train the neural network. In one of them, population, gross generation, installed capacity and years are used in the input layer of the network (Model 1). Other energy sources are used in input layer of network (Model 2). The net energy consumption is in the output layer for two models. Data from 1975 to 2003 are used for the training. Three years (1981, 1994 and 2003) are used only as test data to confirm this method. The statistical coefficients of multiple determinations (R-2-value) for training data are equal to 0.99944 and 0.99913 for Models 1 and 2, respectively. Similarly, R-2 values for testing data are equal to 0.997386 and 0.999558 for Models 1 and 2, respectively. According to the results, the net energy consumption using the ANN technique has been predicted with acceptable accuracy. Apart from reducing the whole time required, with the ANN approach, it is possible to find solutions that make energy applications more viable and thus more attractive to potential users. It is also expected that this study will be helpful in developing highly applicable energy policies. (c) 2004 Elsevier Ltd. All rights reserved