Search Results (6)

Dye-sensitized solar cells (DSSCs) have emerged as a potential candidate for third-generation thin film solar energy conversion systems because of their outstanding optoelectronic properties, cost-effectiveness, environmental friendliness, and easy manufacturing process. The electron transport layer is one of the most essential components in DSSCs since it plays a crucial role in the device’s greatest performance. Silver ions as a dopant have drawn attention in DSSC device applications because of their stability under ambient conditions, decreased charge recombination, increased efficient charge transfer, and optical, structural, and electrochemical properties. Because of these concepts, herein, we report the synthesis of pristine TiO₂ using a novel green modified solvothermal simplistic method. Additionally, the prepared semiconductor nanomaterials, Ag-doped TiO₂ with percentages of 1, 2, 3, and 4%, were used as photoanodes to enhance the device’s performance. The obtained nanomaterials were characterized using XRD, FTIR, FE-SEM, EDS, and UV–vis techniques. The average crystallite size for pristine TiO₂ and Ag-doped TiO₂ with percentages of 1, 2, 3, and 4% was found to be 13 nm by using the highest intensity peaks in the XRD spectra. The Ag-doped TiO₂ nanomaterials exhibited excellent photovoltaic activity as compared to pristine TiO₂. The incorporation of Ag could assist in successful charge transport and minimize the charge recombination process. The DSSCs showed a J_sc of 8.336 mA/cm², a V_oc of 698 mV, and an FF of 0.422 with a power conversion efficiency (PCE) of 2.45% at a Ag concentration of 4% under illumination of 100 mW/cm² power with N719 dye, indicating an important improvement when compared to 2% Ag-doped (PCE of 0.97%) and pristine TiO₂ (PCE of 0.62%). Full article

This study encompasses the grain size distribution of the playa lakes (Pachpadra, Pokhran, and Didwana) of the Thar Desert in Rajasthan, India. The grain size of sediment particles is the most fundamental feature, giving essential information regarding their origin, transport history, and depositional conditions. The aeolian and fluvial transport processes were evaluated through environmentally sensitive grain size subpopulations to identify the differential sedimentary sources and dynamics in the playas. End-member modelling further determined the sediment grain size distribution through statistical analysis. The playa sediments mainly consist of very fine sand (46–54%) and very coarse silt (22–37%). The results show that the average fine fraction of Pachpadra, Pokhran, and Didwana playa was 46.29%, 66.11%, and 66.28%, respectively. In contrast, the average coarser fraction deposition in Pachpadra, Pokhran, and Didwana corresponds to 53.71%, 33.89%, and 33.72%, respectively. This suggests that the playas mostly contain aeolian sediment rather than fluvial sediment transported by dust/sand storms. Additionally, the textural pattern and depositional distribution of the sediments determined through the Passega CM diagram and bivariate plots indicate that 82% of the samples were poorly sorted, and 18% were very poorly sorted. Furthermore, an environmentally sensitive grain size component (ESGSC) was also assessed to identify the spatial variability and transport processes of sediment between these playas. Three ESGSCs in Pokhran (250 µ, 31 µ, and 2 µ) and Pachpadra (125 µ, 31 µ, and 4 µ), while two ESGSCs in Didwana playa (125 µ and 16 µ) were identified, indicating sediment deposition with moderate velocity in a low energy environment with a mixed sediment population transported by aeolian and fluvial activities. Full article

Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment. Full article

Rice is a global food grain crop for more than one-third of the human population and a source for food and nutritional security. Rice production is subjected to various stresses; blast disease caused by Magnaporthe oryzae is one of the major biotic stresses that has the potential to destroy total crop under severe conditions. In the present review, we discuss the importance of rice and blast disease in the present and future global context, genomics and molecular biology of blast pathogen and rice, and the molecular interplay between rice–M. oryzae interaction governed by different gene interaction models. We also elaborated in detail on M. oryzae effector and Avr genes, and the role of noncoding RNAs in disease development. Further, rice blast resistance QTLs; resistance (R) genes; and alleles identified, cloned, and characterized are discussed. We also discuss the utilization of QTLs and R genes for blast resistance through conventional breeding and transgenic approaches. Finally, we review the demonstrated examples and potential applications of the latest genome-editing tools in understanding and managing blast disease in rice. Full article

Quinoline-3-carboxamides are an essential class of drug-like small molecules that are known to inhibit the phosphatidylinositol 3-kinase-related kinases (PIKK) family kinases. The quinoline nitrogen is shown to bind to the hinge region of the kinases, making them competitive inhibitors of adenosine triphosphate (ATP). We have previously designed and synthesized quinoline-3-carboxamides as potential ataxia telangiectasia mutated (ATM) kinase inhibitors to function as an adjuvant treatment with DNA damaging agents. This article discusses the molecular docking studies performed with these derivatives with the DNA damage and response (DDR) kinases-ATM, ataxia telangiectasia and rad3 related (ATR), and DNA dependent protein kinase catalytic subunit (DNA-PKcs) and highlights their selectivity towards ATM kinase. Docking studies were also performed with mTOR and PI3Kγ, which are close homologs of the DDR kinases. Molecular dynamics simulations were performed for one of the inhibitors against all the enzymes to establish the stability of the interactions involved. Finally, the absorption, distribution, metabolism, and excretion (ADME) properties of the inhibitors were predicted using the QikProp manual in Maestro. In conclusion, the molecules synthesized showed high selectivity towards the ATM kinase in comparison with the other kinases, though the sequence similarity between them was relatively high. Full article

The history of DNA sequencing dates back to 1970s. During this period the two first generation nucleotide sequencing techniques were developed. Subsequently, Sanger's dideoxy method of sequencing gained popularity over Maxam and Gilbert's chemical method of sequencing. However, in the last decade, we have observed revolutionary changes in DNA sequencing technologies leading to the emergence of next-generation sequencing (NGS) techniques. NGS technologies have enhanced the throughput and speed of sequencing combined with bringing down the overall cost of the process over a time. The major applications of NGS technologies being genome sequencing and resequencing, transcriptomics, metagenomics in relation to plant-microbe interactions, exon and genome capturing, development of molecular markers and evolutionary studies. In this review, we present a broader picture of evolution of NGS tools, its various applications in crop plants, and future prospects of the technology for crop improvement. Full article