The unit groups of semisimple group algebras of some non-metabelian groups of order 144144

summary:We consider all the non-metabelian groups $G$ of order $144$ that have exponent either $36$ or $72$ and deduce the unit group $U(\mathbb {F}_qG)$ of semisimple group algebra $\mathbb {F}_qG$. Here, $q$ denotes the power of a prime, i.e., $q=p^r$ for $p$ prime and a positive integer $r$. Up to isomorphism, there are $6$ groups of order $144$ that have exponent either $36$ or $72$. Additionally, we also discuss how to simply obtain the unit groups of the semisimple group algebras of those non-metabelian groups of order $144$ that are a direct product of two nontrivial groups. In all, this paper covers the unit groups of semisimple group algebras of $17$ non-metabelian groups.\looseness -

On unit group of finite semisimple group algebras of non-metabelian groups up to order 72

summary:We characterize the unit group of semisimple group algebras $\mathbb {F}_qG$ of some non-metabelian groups, where $F_q$ is a field with $q=p^k$ elements for $p$ prime and a positive integer $k$. In particular, we consider all 6 non-metabelian groups of order 48, the only non-metabelian group $((C_3\times C_3)\rtimes C_3)\rtimes C_2$ of order 54, and 7 non-metabelian groups of order 72. This completes the study of unit groups of semisimple group algebras for groups upto order 72

Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition

Over the last few years, several researchers have worked on handwritten character recognition and have proposed various techniques to improve the performance of Indic and non-Indic scripts recognition. Here, a Deep Convolutional Neural Network has been proposed that learns deep features for offline Gurmukhi handwritten character and numeral recognition (HCNR). The proposed network works efficiently for training as well as testing and exhibits a good recognition performance. Two primary datasets comprising of offline handwritten Gurmukhi characters and Gurmukhi numerals have been employed in the present work. The testing accuracies achieved using the proposed network is 98.5% for characters and 98.6% for numerals

Role of Apoptotic Proteins in REC-2006 Mediated Radiation Protection in Hepatoma Cell Lines

The present study was carried out to evaluate the role of apoptotic proteins in REC-2006-mediated radiation protection in hepatoma cell lines. REC-2006 treatment 2 h before irradiation strongly inhibited the cleavage of ATM and PARP-1 in HepG2 cells. The expression of nuclear apoptosis inducing factor (AIF) was found to be more inhibited (~17%) in HepG2 cells in REC-2006 + radiation-treated group. More inhibition (~33%) of cytochrome c was observed in HepG2 cells upon REC-2006 treatment 2 h prior irradiation. Similarly, significantly more (P<.05) inhibition of Apaf-1, caspase-9 and caspase-3 was observed in REC-2006 + radition-treated group in HepG2 cells. REC-2006 treatment restored the expression of ICAD in HepG2 cells; however, no restoration was observed in Hep3B cells. Lower nuclear to cytoplasmic CAD ratio was observed in HepG2 cells (~0.6) as compared with Hep3B cells (~1.2) in REC-2006 + radiation-treated group. In conclusion, REC-2006 rendered higher protection in HepG2 cells by inhibiting the expression and translocation of AIF, inhibiting the cleavage of ATM and PARP-1, restoring the expression of ICAD, inhibiting the release of cytochrome c and thus modulating the expression of Apaf-1 caspase-9 and activity of caspase-3

Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs) and Genetic Programming (GP). The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN model with the training function Levenberg-Marquardt (LM) for the prediction of concrete compressive strength is the best prediction tool

Computation of the Wedderburn decomposition of semisimple group algebras of groups up to order 120

In this paper, we discuss the Wedderburn decompositions of the semisimple group algebras of all groups up to order 120. More precisely, we explicitly compute the Wedderburn decompositions of the semisimple group algebras of 26 non-metabelian groups

Characterization of stem rust resistance gene Sr2 in Indian wheat varieties using polymerase chain reaction (PCR) based molecular markers

Stem rust or black rust is one of the most important diseases of wheat worldwide. In India, central, peninsular and southern hill zones are particularly prone to stem rust where favourable environmental conditions exist. The recent emergence of wheat stem rust race Ug99 (TTKSK) and related strains threatens global wheat production as Ug99 overcome resistance gene Sr31 effective for many years. Resistance gene Sr2, derived from cultivar ‘Hope’ is responsible for slow rusting and providing partial but durable resistance against stem rust of wheat. In addition to other unknown minor genes (Sr2 complex), this gene tends to be non-specific and is currently effective against all isolates of Puccinia graminis tritici throughout wheat-growing regions of the world. A set of 135 bread wheat varieties developed in the last forty years for prominent northern, central, peninsular and southern hill regions of India was screened with molecular markers, CsSr2 and GWM533, developed and identified for Sr2 gene. Out of 135 varieties screened, 92 confirmed the presence of Sr2 gene at molecular level. The molecular information of Sr2 gene was corroborated with the available morphological marker data for selected varieties to evaluate the efficacy of these molecular markers in Indian wheat germplasm. It was observed that these two molecular markers in combination provide accuracy in 92% lines for this gene at molecular level with presumed Sr2 status in Indian wheat varieties. It is proposed that the use of CsSr2 and GWM533 will help in gene pyramiding of Sr2 along with other stem rust resistance genes in future wheat varieties to accelerate Indian breeding program for rust resistance.Keywords: Wheat, stem rust, Puccinia graminis f.sp. tritici (Pgt), Sr2 gene, molecular markerAfrican Journal of Biotechnology Vol. 12(18), pp. 2353-235

Measurement of Natural Radioactivity, Radon Exhalation Rate and Radiation Hazard Assessment in Indian Cement Samples

AbstractBuilding materials are assumed to be the second source of Radon inside buildings. Due to low level of radon emanation from these materials, long term measurements are needed. Radiation doses from the building materials vary depending upon the natural radionuclides 226Ra, 232Th and their daughter products and 40K present in them. Cement is the main and important component used in the construction of buildings in many countries. These radio nuclides pose exposure risk due to their gamma ray emission and internally due to radon and its progeny that emit alpha particles. In the present study radon exhalation rate and the activity concentration of 226Ra, 232Th and 40K radionuclides in cement samples used in Aligarh region (U.P.), India have been measured by “Sealed Can technique” using LR-115 type II detectors and a low level NaI (Tl) based gamma ray spectrometer, respectively. From the measurements activity concentration of radionuclides 226Ra, 232Th and 40K vary from 9 to 28 Bq kg-1, 21 to 43 Bq kg-1 and 280 to 554 Bq kg-1 with overall average value of 19 Bq kg-1,35 Bq kg-1 and 406.7 Bq kg -1 respectively. Radon equivalent activity (Raeq) and external hazard index (Hex) have been found to vary from 60.8 to 121 Bq kg-1 and 0.16 to 0.30 respectively. The values are lower than the recommended safe values

Prospects of microgreens as budding living functional food: Breeding and biofortification through OMICS and other approaches for nutritional security

Nutrient deficiency has resulted in impaired growth and development of the population globally. Microgreens are considered immature greens (required light for photosynthesis and growing medium) and developed from the seeds of vegetables, legumes, herbs, and cereals. These are considered “living superfood/functional food” due to the presence of chlorophyll, beta carotene, lutein, and minerals like magnesium (Mg), Potassium (K), Phosphorus (P), and Calcium (Ca). Microgreens are rich at the nutritional level and contain several phytoactive compounds (carotenoids, phenols, glucosinolates, polysterols) that are helpful for human health on Earth and in space due to their anti-microbial, anti-inflammatory, antioxidant, and anti-carcinogenic properties. Microgreens can be used as plant-based nutritive vegetarian foods that will be fruitful as a nourishing constituent in the food industryfor garnish purposes, complement flavor, texture, and color to salads, soups, flat-breads, pizzas, and sandwiches (substitute to lettuce in tacos, sandwich, burger). Good handling practices may enhance microgreens’stability, storage, and shelf-life under appropriate conditions, including light, temperature, nutrients, humidity, and substrate. Moreover, the substrate may be a nutritive liquid solution (hydroponic system) or solid medium (coco peat, coconut fiber, coir dust and husks, sand, vermicompost, sugarcane filter cake, etc.) based on a variety of microgreens. However integrated multiomics approaches alongwith nutriomics and foodomics may be explored and utilized to identify and breed most potential microgreen genotypes, biofortify including increasing the nutritional content (macro-elements:K, Ca and Mg; oligo-elements: Fe and Zn and antioxidant activity) and microgreens related other traits viz., fast growth, good nutritional values, high germination percentage, and appropriate shelf-life through the implementation of integrated approaches includes genomics, transcriptomics, sequencing-based approaches, molecular breeding, machine learning, nanoparticles, and seed priming strategiesetc