A Novel DUF569 Gene Is a Positive Regulator of the Drought Stress Response in Arabidopsis

In the last two decades, global environmental change has increased abiotic stress on plants and severely affected crops. For example, drought stress is a serious abiotic stress that rapidly and substantially alters the morphological, physiological, and molecular responses of plants. In Arabidopsis, several drought-responsive genes have been identified; however, the underlying molecular mechanism of drought tolerance in plants remains largely unclear. Here, we report that the “domain of unknown function” novel gene DUF569 (AT1G69890) positively regulates drought stress in Arabidopsis. The Arabidopsis loss-of-function mutant atduf569 showed significant sensitivity to drought stress, i.e., severe wilting at the rosette-leaf stage after water was withheld for 3 days. Importantly, the mutant plant did not recover after rewatering, unlike wild-type (WT) plants. In addition, atduf569 plants showed significantly lower abscisic acid accumulation under optimal and drought-stress conditions, as well as significantly higher electrolyte leakage when compared with WT Col-0 plants. Spectrophotometric analyses also indicated a significantly lower accumulation of polyphenols, flavonoids, carotenoids, and chlorophylls in atduf569 mutant plants. Overall, our results suggest that novel DUF569 is a positive regulator of the response to drought in Arabidopsis.     

A spatio‐temporal water quality assessment of the Beas and Sutlej Rivers at the Harike Wetland: A Ramsar site in Punjab,India

Lakes play a vital role in regulating water storage, flow of river water, and ultimately maintaining a balanced ecosystem. Spatial and temporal variations in physicochemical parameters of water in Harike Wetland, a Ramsar site in the northwestern state of Punjab, India, were studied. This study was conducted on a monthly basis from January to December 2015. The water quality was studied at ten locations from sites 1 to 10 upstream, central and downstream from Harike Lake for ten physicochemical parameters, including temperature, pH, electrical conductivity, turbidity, dissolved oxygen concentration biological oxygen demand, nitrate and phosphate concentrations and salinity. The findings of this study revealed that, except for temperature and pH, all parameters exhibited relatively higher values for the Sutlej River, compared with the Beas River, with sampling sites 5 to site 7 exhibiting intermediate results. The mean seasonal temperature variations ranged from 16.9 to 26.6 °C, the pH from 7.7 to 8.2, electrical conductivity from 223 to 303 μS cm^?1 and TDS concentration from 148.7 to 180.4 ppm. Correlation analysis was conducted to assess the relations between the variables. The electrical conductivity exhibited a high positive correlation with salinity and biological oxygen demand, whereas it correlated negatively with the dissolved oxygen concentration. Box and whisker plots were also plotted for the study results to better examine the data distribution.     

Defect Prediction Using Akaike and Bayesian Information Criterion

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values.     

Determination of dairy wastewater treatability by bio-trickling filter packed with lava rocks - case study PEGAH dairy factory

This paper investigates the effectiveness of a biological trickling filter for the treatment of wastewaters produced by a company manufacturing dairy products. First a bio-trickling column with a height of 150 cm was packed with lava rocks from north mountain of Tehran. It operates with the recirculation of liquid through the packing. In order to startup the pilot scale, steady state condition was gained by pumping activated sludge and dairy wastewater for 23 days. Afterwards, dairy wastewater was added to liquid tank for treatment. Hydraulic retention time (HRT) of treatment decreases from 5 days to 1 day then at HRT of 12, 8, 7, 6 and 4 h. Results show that the average chemical oxygen demand (COD) decreased from 2,750 to 98 mg/L at HRT of 7 h and efficiency of TKN removal was more than 70%. The microorganisms developed in the bio-trickling filter were able to efficiently remove COD levels up to 2,750 mg/L, under aerobic conditions at pH values between 6.8 and 7.2 under low temperature condition between 10 and 13 °C.     

Indicators of research planning: A comparative study of research groups in six countries

In this paper, an attempt has been made to examine the characteristics of research planning at the micrososmic level of the research group in six countries-Argentina, Egypt, India, Republic of Korea, Poland and UkSSR. The paper focusses on the following aspects: (1) intrinsic and extrinsic factors influencing the choice of research themes and orientation of the research programme; (2) quality of research planning measured by dimensions, such as planning consistency, task-interdependence and prior contacts with potential users; and (3) pattern of funding of research groups. Variations in the characteristics of research planning and funding mechanisms in different institutional and socio-cultural settings (countries) have been examined.     

Exploring the Limits of Social Cognitive Theory: Why Negatively Reinforced Behaviors on TV May Be Modeled Anyway

Despite extensive discussion of the potential for viewers' social learning of risky health behaviors from TV programming, there has been relatively little effort to test behavioral modeling predictions. That is, it is not clear whether televised depictions of negatively reinforced undesirable behaviors (e.g., unsafe sex), in fact, influence the value viewers attach to those behaviors and their future likelihood of performing them, as social cognitive theory (SCT) would predict. Indeed, we argue that social learning in likely minimal in such contexts due to the programming schemas audiences bring to the viewing experience. In Study 1, the contents of serial TV programming schemas are examined. Results suggest that viewers expect main characters to ultimately survive and thrive, despite the adversity they face. In Study 2, competing predictions from SCT and schema theory are tested. College women ( N = 400) were exposed to various portrayals of promiscuous sexual behavior (1-night stands) that were edited to display more or less positive or negative outcomes. Results suggested that, consistent with SCT, those with direct experience with the behavior were not influenced by the portrayals. However, inconsistent with SCT, but consistent with the schema argument, those without direct experience were more likely to expect future participation in the unsafe behavior, regardless of the valence of the reinforcement depicted. Implications for future research on media, SCT, and risky behaviors are discussed.     

Memory effect of locally ordered α-phase in the melting and phase transformation behavior of β-isotactic polypropylene

Structural changes in β-isotactic polypropylene (β-iPP) during the heating were studied by means of differential scanning calorimetry and real-time in situ X-ray diffraction using a synchrotron source. Crystalline phase transformation and the memory effect caused by residual nuclei of α-iPP were observed during the heating of β-iPP. The memory effect observed in β-iPP during heating and crystallization is believed to be due to the existence of locally ordered α-from in the melt. The effect of local α-form order was probed by studying the behavior under heating of samples with a range of thermal histories. Samples were heated above the equilibrium melting temperature of iPP to remove all residual local order and the memory effect associated with this local order. The samples crystallized isothermally at different temperatures exhibited a significantly different melting and phase transformation behavior during heating. β-iPP is found to be an excellent material for the study of polymorphism, phase transformations, and characteristic memory effects in semicrystalline polymers.