Study of genetic divergence among wheat genotypes through random amplified polymorphic DNA

The degree of genetic divergence was estimated in seven wheat genotypes, six exotic genotypes and one local variety, through random amplified polymorphic DNA methodology. A total of 112 DNA fragments were generated by the 15 random primers, with an average of about 7.4 bands per primer. Among the 112, 50 fragments showed polymorphism among the seven wheat genotypes. Nei and Li's similarity matrix ranged from 86.2 to 93.0%, which indicated a narrow genetic base among the genotypes. The maximum similarity, 93.0%, was observed between 12WLRG/1-12 and WL-43. The local variety, Chenab-70, showed the lowest similarity with the exotic types. We conclude that random amplified polymorphic DNA analysis can be used for the characterization and grouping of wheat genotypes; these results will be helpful in our wheat breeding program.     

An Eye-Shaped Ultra-Sensitive Localized Surface Plasmon Resonance–Based Biochemical Sensor

Plasmonics - This research illustrates a parametric study based on surface plasmon resonance of a slightly gold-coated photonic crystal fiber (PCF). In verifying sensing accuracy, the proposed...     

Impact of competition on the growth of Pinus tabulaeformis in response to climate on the Loess Plateau of China

Plant Ecology - As climate changes, understanding tree responses to climate is important to predict tree growth, and plant competition as a nonnegligible biotic factor plays a key role in such...     

Potential of bacterial culture media in biofabrication of metal nanoparticles and the therapeutic potential of the as-synthesized nanoparticles in conjunction with artemisinin against MDA-MB-231 breast cancer cells

In the recent past, various groups have proposed diverse biocompatible methods for the synthesis of metal nanoparticles (NPs). Besides culture biomass, culture supernatants (CS) are increasingly being explored for the synthesis of NPs; however, with the ever-increasing exploration of various CS in the biofabrication of NPs, it is equally important to explore the potential of various culture media (CMs) in the synthesis of metal NPs. Considering these aspects, in the present investigation, we explore the possible applicability of various CMs in the biofabrication of metal NPs. The synthesis of NPs was primarily followed by UV/VIS spectroscopy, and, thereafter, the NPs were characterized by various physiochemical techniques, including EM, EDX, FT_IR, X-ray diffraction, and DLS measurements, and finally, their anticancer potentialities were investigated against breast cancer. In addition, the NPs were examined in conjunction with artemisinin for therapeutic benefits against aggressive and highly metastatic MDA-MB-231 breast cancer cells. Cumulatively, the results of the present study collated the potentials of various bacterial CMs in the biofabrication of metal NPs and ascertained the efficacy of the as-synthesized silver nanoparticles, especially the combinatorial entity as intriguing breast cancer therapeutics. The data of the present study plausibly assist in advancing the therapeutic applicability of the combinatorial amalgam against aggressive and highly metastatic MDA-MB-231 breast cancer cells.     

A volatile producing endophytic Bacillus siamensis YC7012 promotes root development independent on auxin or ethylene/jasmonic acid pathway

Plant and Soil - In many mixed grass-shrub ecosystems, increased shrub biomass tends to promote overall carbon storage, but the distribution of carbon pools may be complicated by disturbances such...     

Changes in the photosynthesis properties and photoprotection capacity in rice (Oryza sativa) grown under red,blue, or white light

Photosynthesis Research - Non-photochemical quenching (NPQ) of the excited state of chlorophyll a is a major photoprotective mechanism plants utilize to survive under high light. Here, we report...     

AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features

It is of utmost importance to develop a computational method for accurate prediction of antioxidants, as they play a vital role in the prevention of several diseases caused by oxidative stress. In this correspondence, we present an effective computational methodology based on the notion of deep latent space encoding. A deep neural network classifier fused with an auto-encoder learns class labels in a pruned latent space. This strategy has eliminated the need to separately develop classifier and the feature selection model, allowing the standalone model to effectively harness discriminating feature space and perform improved predictions. A thorough analytical study has been presented alongwith the PCA/tSNE visualization and PCA-GCNR scores to show the discriminating power of the proposed method. The proposed method showed a high MCC value of 0.43 and a balanced accuracy of 76.2%, which is superior to the existing models. The model has been evaluated on an independent dataset during which it outperformed the contemporary methods by correctly identifying the novel proteins with an accuracy of 95%.