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A bio approach (mediated by Agaricus bisporus) was attempted in the present study to synthesize ferrite nanoparticles MFe 2 O 4 (M = Zn, Cu and Co]. The synthesized ferrites nanoparticles were characterized in terms of variations in the crystallinity, dimension and sizes using standard techniques (XRD, FTIR, SEM-EDAX, Zeta potential and DLS). VSM analysis showed noticeable differences in the magnetic saturation values: zinc ferrite (12.5 emu/g); cobalt ferrite (27.5 emu/g) and copper ferrite (21.5 emu/g). In- vitro cytotoxic effect of the synthesised ferrite nanoparticles resulted in effective inhibition of colon cell line growth (SW620). The ferrite nanoparticles were also evaluated for their drug-release behaviour using doxorubicin (DOX). The results indicated that the maximum DOX delivery was 98.74% using zinc ferrite, 97.34% using cobalt ferrite and 99.52% using copper ferrite within 6 h using 10 mg of nanoparticles. From the hyperthermia results, a SAR of 337 W/g was noted using 10 mg of copper ferrite nanoparticles at an applied frequency of 335 kHz and magnetic field strength of 235 A/m.

The present study deals with the biosynthesis of palladium nanoparticles (PdNPs) using Agaricus bisporus and exploring its potential biological applications. The synthesized PdNPs were characterized by UV–visible, FTIR and XRD techniques. Microscopic analyses revealed the triangular (SEM and AFM) and spherical (TEM) morphologies of the nanoparticles with nanosize dimension ranging from 13 to 18 nm. The surface charge of the PdNPs were identified with the help of zeta potential and found to be negatively charged (− 24.3 mV). The PdNPs exhibited good antioxidant effect against DPPH free radicals with maximum radical scavenging activity of 77% using 50 μg/ml. FTIR spectra of the final DPPH solution depicted sharp intense signals at 1018 cm −1 (polysaccharides) and 3342 cm −1 (phenolic acids) evidencing the role of these bio functional groups in neutralizing the free radicals. Antibacterial assay revealed that PdNPs exhibited enhanced growth inhibition effect against gram positive bacteria ( S. auerus ; S. pyrogens ; B. subtilis ) than gram negative bacterial pathogens ( E. aerogenes ; K. pneumoniae ; P. vulgaris ). Anti-inflammatory activity performed with RBC cells showing 87% of activity for biosynthesized PdNPs. MTT assay demonstrated that PdNPs exhibited excellent cytotoxic effect against PK13 cell lines. Maximum growth inhibition of 79% was observed for the maximum dose (50 µg/ml) with IC50 value of 26.1 µg/ml.

Ovarian cancer is one of the most prevalent gynecological cancers, having a relatively high fatality rate with a low five-year chance of survival when detected in late stages. The early detection, treatment and prevention of metastasis is pertinent and a pressing research priority as many patients are diagnosed only in stage three of ovarian cancer. Despite surgical interventions, targeted immunotherapy and adjuvant chemotherapy, relapses are significantly higher than other cancers, suggesting the dire need to identify the root cause of metastasis and relapse and present more precise therapeutic options. In this review, we first describe types of ovarian cancers, the existing markers and treatment modalities. As ovarian cancer is driven and sustained by an elusive and highly chemoresistant population of cancer stem cells (CSCs), their role and the associated signature markers are exhaustively discussed. Non-invasive diagnostic markers, which can be identified early in the disease using circulating tumor cells (CTCs), are also described. The mechanism of the self-renewal, chemoresistance and metastasis of ovarian CSCs is regulated by the Wnt signaling pathway. Thus, its role in ovarian cancer in promoting stemness and metastasis is delineated. Based on our findings, we propose a novel strategy of Wnt inhibition using a well-known Wnt antagonist, secreted frizzled related protein 4 (sFRP4), wherein short micropeptides derived from the whole protein can be used as powerful inhibitors. The latest approaches to early diagnosis and novel treatment strategies emphasized in this review will help design precision medicine approaches for an effective capture and destruction of highly aggressive ovarian cancer.

In this study, yttrium doped zinc ferrite (ZnFe 2− x Y x O 4 x  = 0.01–0.1) and yttrium and copper co-doped zinc ferrite (Cu y Zn 1− y Fe 2− x Y x O 4 x  = 0.1 and y  = 0.5) were synthesized by solution combustion method. The synthesized nanoparticles were authenticated by various techniques such as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy–energy dispersive X-ray analysis (SEM-EDAX) and UV–visible spectroscopy. The photocatalytic activity of synthesized nanoparticles was studied by performing the degradation of methylene blue (MB) under visible light. 95% of MB was degraded in 180 min using yttrium-doped zinc ferrite. 89% of MB was degraded in 30 min using copper and yttrium co-doped zinc ferrite under visible light using 10 mg of the catalyst and 50 µl of hydrogen peroxide. Photocatalytic degradation of colorless pollutant bisphenol A also carried out using the doped zinc ferrite.

The software Defect Prediction (SDP) model uses previously learned data to predict whether a future example (such as a file, class, or module) will be defective or not. Accurate forecast results can help software testers arrange testing resources more efficiently. Nonetheless, there are still difficulties in more exact and reliable defect predictions. This study presents the SDP-CMPOA framework, which stands for Software Defect Prediction Crossover with Brownian Motion-based Pelican Optimization Algorithm. The stages of the approach are pre-processing, feature extraction, feature selection, and detection. The preprocessing stage involves improved data normalization. Higher-order statistical features, enhanced statistical features, and raw features are all taken from the pre-processed data. The primary barrier to precise software defect detection and hence adhering to the ideal feature selection method will be the curse of dimensionality. This research proposes a novel Crossover with the Brownian motion-based Pelican Optimization Algorithm for selecting the best features (CMPOA). Finally, hybrid classifiers like Improved Bidirectional Long Short Term Memory (Bi-LSTM and) Deep Max out models are employed for defect prediction. The classifier analysis of KCI/SDP dataset accuracy of the projected model is 9.17%, 9.6%, 7.20%, 13.3%, and 5.7% better than the other methods such as DBN, SVM, CNN, RF, RNN, and SDP- CMPOA respectively at the 60th learning percentage. The efficiacy of the SDP-CMPOA is examined in comparison to other extant schemes with respect to certain measures.

Wnt signaling has been linked with P-glycoprotein (P-gp) overexpression and which was mainly mediated by β-catenin nuclear translocation. Flavonoids have already been reported as modulators of the Wnt/β-catenin pathway and hence they may serve as promising agents in the reversal of P-gp mediated cancer multi drug resistance (MDR). In this study, we screened selected flavonoids against Wnt/β-catenin signaling molecules. The binding interaction of flavonoids (theaflavin, quercetin, rutin, epicatechin 3 gallate and tamarixetin) with GSK 3β was determined by molecular docking. Flavonoids on P-gp expression and the components of Wnt signaling in drug-resistant KBCH 8-5 cells were analyzed by western blotting and qRT-PCR. The MDR reversal potential of these selected flavonoids against P-gp mediated drug resistance was analyzed by cytotoxicity assay in KBCH 8-5 and MCF7/ADR cell lines. The chemosensitizing potential of flavonoids was further analyzed by observing cell cycle arrest in KBCH 8-5 cells. In this study, we observed that the components of Wnt/β-catenin pathway such as Wnt and GSK 3β were activated in multidrug resistant KBCH 8-5 cell lines. All the flavonoids selected in this study significantly decreased the expression of Wnt and GSK 3β in KBCH 8-5 cells and subsequently modulates P-gp overexpression in this drug-resistant cell line. Further, we observed that these flavonoids considerably decreased the doxorubicin resistance in KBCH 8-5 and MCF7/ADR cell lines. The MDR reversal potential of flavonoids were found to be in the order of theaflavin > quercetin > rutin > epicatechin 3 gallate > tamarixetin. Moreover, we observed that flavonoids pretreatment significantly induced the doxorubicin-mediated arrest at the phase of G2/M. Further, the combinations of doxorubicin with flavonoids significantly modulate the expression of drug response genes in KBCH 8-5 cells. The present findings illustrate that the studied flavonoids significantly enhances doxorubicin-mediated cell death through modulating P-gp expression pattern by targeting Wnt/β-catenin signaling in drug-resistant KBCH 8-5 cells.

Cashew is the second most important tree nut crop in the global market. Cashew is a diploid and heterozygous species closely related to the mango and pistachio. Its improvement by conventional breeding is slow due to the long juvenile phase. Despite the economic importance, very little genomics/transcriptomics information is available for cashew. In this study, the Oxford nanopore reads and Illumina reads were used for de novo assembly of the cashew genome. The hybrid assembly yielded a 356.6 Mb genome corresponding to 85% of the estimated genome size (419 Mb). The BUSCO analysis showed 91.8% of genome completeness. Transcriptome mapping showed 92.75% transcripts aligned with the assembled genome. Gene predictions resulted in the identification of 31,263 genes coding for a total of 35,000 gene isoforms. About 46% (165 Mb) of the cashew genome comprised of repetitive sequences. Phylogenetic analyses of the cashew with nine species showed that it was closely related to  Mangifera indica . Analysis of cashew genome revealed 3104 putative R-genes. The first draft assembly of the genome, transcriptome and R gene information generated in this study would be the foundation for understanding the molecular basis of economic traits and genomics-assisted breeding in cashew.

Deep learning establishes an ongoing, modern technique for image processing with large potential and promising results. After proving its efficiency in various applications DL has also entered into the domain of agriculture. Here, we surveyed 38 research works that applied deep learning techniques to various research problems in tomato plant. We examine the areas of tomato plant research where deep learning is applied, data preprocessing techniques applied, transfer learning and augmentation techniques used. Studied dataset information like data sources used, number of images, classes and train test validation ratio applied. In addition, we study comparisons done on various deep learning architectures and discussed the outcome. The finding showed that DL techniques outperformed all other image processing techniques but DL performs mainly depends on the dataset used.

Cashew is cultivated in varied agro-ecological regions of India and yield levels vary with regions. Therefore, to identify stable genotype for yield, 18 genotypes were tested in four environments for nut yield and ancillary traits during 2008 to 2018 in randomized block design with two replications. The data of 6th annual harvest and cumulative nut yield of six years was analyzed employing additive main effect and multiplicative interaction (AMMI) and genotype and genotype by environment (GGE) methods. Analysis of variance for 6th annual harvest indicated significant differences ( p  < 0.01) for eight traits. Environments varied significantly ( p  < 0.01) for seven traits. Genotype by environment (G × E) interactions were significant ( p  < 0.01) for all traits. Analysis of variance for cumulative yield revealed significant variations between genotypes, environments, G x E interactions. Interaction principal component analysis (IPCA) 1 (84.39%) and IPCA 2 (10.27%) together captured 95% of variability. Genotypes, environments and G × E interaction were accounted for 16.18%, 4.50% and 77.22% respectively of total variation. The environment Pilicode discriminated better while Vridhachalam was representative. BPP-8 and Vengulra-7 were the winning genotypes in Bhubaneswar while Kanaka and Priyanka in Pilicode, Vengurla-4 in Jhargram and UN-50 in Vridhachalam. Therefore, promoting cultivation of these winning genotypes in the corresponding environments is highly recommended to enhance cashew nut production. As per ASV (AMMI stability value,) K-22-1 was stable genotype followed by Bhubaneswar-1. As per YSI (yield stability index), Bhubaneswar-1 was stable and high yielding followed by K-22-1 and BPP-8. Thus stable genotypes identified in this study viz., K-22-1 and Bhuvaneswar-1 are recommended for cultivation in west and east regions of India which have most cashew growing areas for increasing the cashew nut production.

This paper aims to evaluate two machine learning (ML) algorithms, namely, convolutional neural network (CNN) and long short-term memories (LSTM) deep learning algorithms, to predict the hydrological regime of the 3S River Basin under various climate change scenarios. Climate models CMCC-CMS, HadGEM-AO2, and MIROC5 were used to predict future climate and streamflow for three future periods: near-future (2020–2050), mid-future (2050–2080), and far-future (2080–2100) under two Representative Concentration Pathways (RCPs) 4.5 and 8.5. The future projection shows an increase in mean annual temperature from 0.08 to 4.3 °C by CMCC-CMS, from 0.13 to 4.4 °C by HadGEM-AO2, and −0.07 to 4.2 °C MIROC5 models. Similarly, the annual precipitation is projected to fluctuate from 13.3 to 62.5% by CMCC-CMS, from −12.4 to 26.1% by HadGEM-AO2, and from 6.9 to 49% by the MIROC5 model. The 3S River Basin expects an increasing trend in streamflow in the Srepok and Sesan Rivers, while the Sekong is projected to have reduced streamflow. ML models predicted the increasing flood risk in the Sekong and Sesan catchments with the increase of the Q5 index in the future but a decrease in the Srepok.