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.     

Investigating the effect of varying block size on power and energy consumption of GPU kernels

The Journal of Supercomputing - Power consumption is likely to remain a significant concern for exascale performance in the foreseeable future. In addition, graphics processing units (GPUs) have...     

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.     

Chaos based key expansion function for block ciphers

The Key Expansion Function is a vital constituent component of any block cipher. Many of Key Expansion Functions generate subkeys through the algorithms which are based on Feistel or Substitution Permutation Network (SPN) structures against which cryptanalytic methods have been well researched. In this very paper, an efficient method for generating subkeys based on chaotic maps has been suggested. The phenomenon behind the proposed Key Expansion Function is the mixing property of Tent Map. Using chaotic binary sequences, the proposed Key Expansion Function satisfies the specific statistical and cryptographic properties of chaotic generators. A new Bit Extraction Technique based on IEEE-754 Floating-point Standard (binary32) is used to extract the bits of subkeys from the chaotic binary sequences. The generated subkeys are then analyzed. The results show that the given Chaos-based Key Expansion Function is well protected and fully strengthened in all respects.     

Formalizing a hierarchical file system

An abstract file system is defined here as a partial function from (absolute) paths to data. Such a file system determines the set of valid paths. It allows the file system to be read and written at a valid path, and it allows the system to be modified by the Unix operations for creation, removal, and moving of files and directories. We present abstract definitions (axioms) for these operations. This specification is refined towards a pointer implementation. The challenge is to have a natural abstraction function from the implementation to the specification, to define operations on the concrete store that behave exactly in the same way as the corresponding functions on the abstract store, and to prove these facts. To mitigate the problems attached to partial functions, we do this in two steps: first a refinement towards a pointer implementation with total functions, followed by one that allows partial functions. These two refinements are proved correct by means of a number of invariants. Indeed, the insights gained consist, on the one hand, of the invariants of the pointer implementation that are needed for the refinement functions, and on the other hand of the precise enabling conditions of the operations on the different levels of abstraction. Each of the three specification levels is enriched with a permission system for reading, writing, or executing, and the refinement relations between these permission systems are explored. Files and directories are distinguished from the outset, but this rarely affects our part of the specifications. All results have been verified with the proof assistant PVS, in particular, that the invariants are preserved by the operations, and that, where the invariants hold, the operations commute with the refinement functions.     

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F‐score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods.     

Dielectric and electrical studies on iron oxide (α-Fe2O3) nanoparticles synthesized by modified solution combustion reaction for microwave applications

Journal of Electroceramics - Hematite (α-Fe2O3) nanoparticles were synthesized by modified solution combustion method using a mixture of hexamethylenetetramine (HMTA) and glycine fuels at fuel...