A multiscale probabilisitic framework to model early steps in tumor metastasis

Tumor metastasis is the leading cause of nearly all cancer related deaths. While several experimental and computational studies have addressed individual stages of the complex metastasis process, a comprehensive systems-biology model that links various stages of metastasis has not been put forth as of yet. In this paper we discuss the formulation and application of such a model that utilizes basic principles of cell biology, physics and mechanics to study the migratory patterns of tumor cells as they move from the parent tumor site to the connective tissue via the basement membrane. The model is first of its kind in capturing the essential early features of metastasis in a single simulation and shows good agreement with recent experimental studies.     

Blockchain based secure,efficient and coordinated energy trading and data sharing between electric vehicles

Cluster Computing - In this study, a secure and coordinated blockchain based energy trading system for Electric Vehicles (EVs) is presented. The major goals of this study are to provide secure and...     

The genetic drivers for the successful invasive potential of a generalist bird,the House crow

Biological Invasions - What kind of genetic structure helps the rapid range expansion of the invasive species is fundamental to understand spread of invasion. The House crow (Corvus splendens), an...     

Hexose monophosphate shunt,the role of its metabolites and associated disorders: A review

The hexose monophosphate (HMP) shunt acts as an essential component of cellular metabolism in maintaining carbon homeostasis. The HMP shunt comprises two phases viz. oxidative and nonoxidative, which provide different intermediates for the synthesis of biomolecules like nucleotides, DNA, RNA, amino acids, and so forth; reducing molecules for anabolism and detoxifying the reactive oxygen species during oxidative stress. The HMP shunt is significantly important in the liver, adipose tissue, erythrocytes, adrenal glands, lactating mammary glands and testes. We have researched the articles related to the HMP pathway, its metabolites and disorders related to its metabolic abnormalities. The literature for this paper was taken typically from a personal database, the Cochrane database of systemic reviews, PubMed publications, biochemistry textbooks, and electronic journals uptil date on the hexose monophosphate shunt. The HMP shunt is a tightly controlled metabolic pathway, which is also interconnected with other metabolic pathways in the body like glycolysis, gluconeogenesis, and glucuronic acid depending upon the metabolic needs of the body and depending upon the biochemical demand. The HMP shunt plays a significant role in NADPH₂ formation and in pentose sugars that are biosynthetic precursors of nucleic acids and amino acids. Cells can be protected from highly reactive oxygen species by NADPH ₂. Deficiency in the hexose monophosphate pathway is linked to numerous disorders. Furthermore, it was also reported that this metabolic pathway could act as a therapeutic target to treat different types of cancers, so treatments at the molecular level could be planned by limiting the synthesis of biomolecules required for proliferating cells provided by the HMP shunt, hence, more experiments still could be carried out to find additional discoveries.     

Glutamine Synthetase (GS): A Key Enzyme for Nitrogen Assimilation in The Macroalga Gracilariopsis lemaneiformis (Rhodophyta)

This study aimed to address the importance of glutamine synthetase II (GSII) during nitrogen assimilation in macroalga Gracilariopsis lemaneiformis. The cDNA full‐length sequence of the three glGSII genes was revealed to have the 5′ m⁷G cap, 5′‐untranslated region, open reading frame (ORF), 3′‐untranslated region, and a 3′ poly (A) tail. The three glGSIIs were classified into plastid glGS2 and cytosolic glGS1‐1 and glGS1‐2, having conserved GSII domains but different cDNA sequences. The complicated 5′ end flanking region indicates complex function of glGS genes. glGS1 genes were significantly up‐regulated under the different NH₄⁺: NO₃^? ratio (i.e., 40:10, 25:25, 10:40, and 0:50) except glGS2 which dramatically up‐regulated under the low NH₄⁺: NO₃^? ratio (i.e., 10:40 and 0:50) during different cultivation times. These different expression patterns perhaps are due to the different biological roles of GS1 and GS2 in the gene family. Furthermore, hypothetical working model of nitrogen assimilation pathway exhibiting the role of glGS1 and glGS2 is proposed. Finally, glGS2 was expressed in Escherichia coli BL21 (DE3), and the optimal conditions for culture (15°C, overnight), purification (500 mM imidazole washing), and activity (pH 7.4, 37°C) were established. This study lays a very important foundation for exploring the role of GS in nitrogen assimilation in algae and plants.     

High intrinsic seed Zn concentration improves abiotic stress tolerance in wheat

Plant and Soil - Abiotic stresses are threatening wheat productivity across the globe, which is often associated with nutrient deficiencies. Zinc (Zn) is involved in many physiological processes of...     

N-(2-hydroxyphenyl)acetamide and its gold nanoparticle conjugation prevent glycerol-induced acute kidney injury by attenuating inflammation and oxidative injury in mice

Molecular and Cellular Biochemistry - The protective activity of N-(2-hydroxyphenyl)acetamide (NA-2) and NA-2-coated gold nanoparticles (NA-2-AuNPs) in glycerol-treated model of acute kidney injury...     

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%.