Trident Nano-Indexing the Proteomics Table: Next-Version Clustering of Iron Carbide NPs and Protein Corona.

Protein corona composition and precise physiological understanding of differentially expressed proteins are key for identifying disease biomarkers. In this report, we presented a distinctive quantitative proteomics table of molecular cell signaling differentially expressed proteins of corona that formed on iron carbide nanoparticles (NPs). High-performance liquid chromatography/electrospray ionization coupled with ion trap mass analyzer (HPLC/ESI-Orbitrap) and MASCOT helped quantify 142 differentially expressed proteins. Among these proteins, 104 proteins showed upregulated behavior and 38 proteins were downregulated with respect to the control, whereas 48, 32 and 24 proteins were upregulated and 8, 9 and 21 were downregulated CW (control with unmodified NPs), CY (control with modified NPs) and WY (modified and unmodified NPs), respectively. These proteins were further categorized on behalf of their regularity, locality, molecular functionality and molecular masses using gene ontology (GO). A STRING analysis was used to target the specific range of proteins involved in metabolic pathways and molecular processing in different kinds of binding functionalities, such as RNA, DNA, ATP, ADP, GTP, GDP and calcium ion bindings. Thus, this study will help develop efficient protocols for the identification of latent biomarkers in early disease detection using protein fingerprints.

Fractionation of Biomolecules in Withania coagulans Extract for Bioreductive Nanoparticle Synthesis, Antifungal and Biofilm Activity.

Withania coagulans contains a complex mixture of various bioactive compounds. In order to reduce the complexity of the plant extract to purify its phytochemical biomolecules, a novel fractionation strategy using different solvent combination ratios was applied to isolate twelve bioactive fractions. These fractions were tested for activity in the biogenic synthesis of cobalt oxide nanoparticles, biofilm and antifungal activities. The results revealed that plant extract with bioactive fractions in 30% ratio for all solvent combinations showed more potent bioreducing power, according to the observed color changes and the appearance of representative absorption peaks at 500-510 nm in the UV-visible spectra which confirm the synthesis of cobalt oxide nanoparticles (Co3O4 NPs). XRD diffraction was used to define the crystal structure, size and phase composition of the products. The fractions obtained using 90% methanol/hexane and 30% methanol/hexane showed more effectiveness against biofilm formation by Pseudomonas aeruginosa and Staphylococcus aureus so these fractions could potentially be used to treat bacterial infections. The 90% hexane/H2O fraction showed excellent antifungal activity against Aspergillus niger and Candida albicans, while the 70% methanol/hexane fraction showed good antifungal activity for C. albicans, so these fractions are potentially useful for the treatment of various fungal infections. On the whole it was concluded that fractionation based on effective combinations of methanol/hexane was useful to investigate and study bioactive compounds, and the active compounds from these fractions may be further purified and tested in various clinical trials.

Nano-managing silver and zinc as bio-conservational approach against pathogens of the honey bee.

Herein, silver and zinc oxide Nanoparticles (NPs) were synthesized by using W. coagulant fruit extract as reducing agent and capping agent. The green synthesized NP with distinct properties were used for novel application against fungal and bacterial pathogen of honey bee (A. mellifera). The UV-spectroscopy confirms the synthesis of silver and zinc oxide NPs at 420 nm and 350 nm respectively. Further, XRD evaluated the monoclinic structure of Ag NPs while ZnO NPs showed wurtzite hexagonalcrystlized structure. Resistant honey bee pathogens such Paenibacilluslarvae, Melissococcus plutonius and Ascosphaera apis were isolated, identified and cultured in vitro to assess the antimicrobial potentials of Ag and ZnO NPs. Additionally, different biomolecules provide access to achieve maximum and stable Ag and ZnO NPs. It was also observed that with increasing the concentration of zinc oxide NPs and sliver NPs, zone of inhibition was also increased. Thus, present findings show that plant extracts can be a useful natural resource to prepare functional nonmaterial for targeted applications especially in the field of apicultural research.

Bioinspired Cobalt Oxide Nanoball Synthesis, Characterization, and Their Potential as Metal Stress Absorbants.

Massive accumulation of heavy metals in agricultural land as a result of enhanced levels of toxicity in the soil is an emerging global concern. Among various metals, zinc contamination has severe effects on plant and human health through the food chain. To remove such toxicity, a nanotechnological neutralizer, cobalt oxide nanoballs (Co3O4 Nbs) were synthesized by using the extract of Cordia myxa. The Co3O4 Nbs were well characterized via UV-vis spectrophotometry, scanning electron microscopy, and X-ray diffraction techniques. Green-synthesized Co3O4 Nbs were exposed over Acacia jacquemontii and Acacia nilotica at different concentrations (25, 50, 75, and 100 ppm). Highly significant results were observed for plant growth by the application of Co3O4 Nbs at 100 ppm, thereby increasing the root length (35%), shoot length (48%), fresh weight (44%), and dry weight (40%) of the Acacia species with respect to the control. Furthermore, physiological parameters including chlorophyll contents, relative water contents, and osmolyte contents like proline and sugar showed a prominent increase. The antioxidant activity and atomic absorption supported and justified the positive response to using Co3O4 Nbs that mitigated the heavy-metal zinc stress by improving the plant growth. Hence, the biocompatible Co3O4 Nbs counteract the zinc toxicity for governing and maintaining plant growth. Such nanotechnological tools can therefore step up the cropping system and overcome toxicity to meet the productivity demand along with the development of agricultural management strategies.

A Hybrid Model for Temperature Prediction in a Sheep House.

Too high or too low temperature in the sheep house will directly threaten the healthy growth of sheep. Prediction and early warning of temperature changes is an important measure to ensure the healthy growth of sheep. Aiming at the randomness and empirical problem of parameter selection of the traditional single Extreme Gradient Boosting (XGBoost) model, this paper proposes an optimization method based on Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO). Then, using the proposed PCA-PSO-XGBoost to predict the temperature in the sheep house. First, PCA is used to screen the key influencing factors of the sheep house temperature. The dimension of the input vector of the model is reduced; PSO-XGBoost is used to build a temperature prediction model, and the PSO optimization algorithm selects the main hyperparameters of XGBoost. We carried out a global search and determined the optimal hyperparameters of the XGBoost model through iterative calculation. Using the data of the Xinjiang Manas intensive sheep breeding base to conduct a simulation experiment, the results show that it is different from the existing ones. Compared with the temperature prediction model, the evaluation indicators of the PCA-PSO-XGBoost model proposed in this paper are root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), mean absolute error (MAE) , which are 0.0433, 0.0019, 0.9995, 0.0065, respectively. RMSE, MSE, and MAE are improved by 68, 90, and 94% compared with the traditional XGBoost model. The experimental results show that the model established in this paper has higher accuracy and better stability, can effectively provide guiding suggestions for monitoring and regulating temperature changes in intensive housing and can be extended to the prediction research of other environmental parameters of other animal houses such as pig houses and cow houses in the future.

Incubating Green Synthesized Iron Oxide Nanorods for Proteomics-Derived Motif Exploration: A Fusion to Deep Learning Oncogenesis.

The nanotechnological arena has revolutionized the diagnostic efficacies by investigating the protein corona. This displays provoking proficiencies in determining biomarkers and diagnostic fingerprints for early detection and advanced therapeutics. The green synthesized iron oxide nanoparticles were prepared via Withania coagulans and were well characterized using UV-visible spectroscopy, X-ray diffraction analysis, Fourier transform infrared spectroscopy, and nano-LC mass spectrophotometry. Iron oxides were rod-shaped with an average size of 17.32 nm and have crystalline properties. The as-synthesized nanotool mediated firm nano biointeraction with the proteins in treatment with nine different cancers. The resultant of the proteome series was filtered oddly that highlighted the variant proteins within the differentially expressed proteins on behalf of nano-bioinformatics. Further magnification focused on S13_N, RS15, RAB, and 14_3_3 domains and few abundant motifs that aid scanning biomarkers. The entire set of variant proteins contracting to common proteins elucidates the underlining mechanical proteins that are marginally assessed using the robotic nanotechnology. Additionally, the iron rods indirectly possess a prognostic effect in manipulating expression of proteins through a smarter route. Thereby, such biologically designed nanotools provide a dual approach for medical studies.

Pollution index of waterfowl farm assessment and prediction based on temporal convoluted network.

Environmental quality is a major factor that directly impacts waterfowl productivity. Accurate prediction of pollution index (PI) is the key to improving environmental management and pollution control. This study applied a new neural network model called temporal convolutional network and a denoising algorithm called wavelet transform (WT) for predicting future 12-, 24-, and 48-hour PI values at a waterfowl farm in Shanwei, China. The temporal convoluted network (TCN) model performance was compared with that of recurrent architectures with the same capacity, long-short time memory neural network (LSTM), and gated recurrent unit (GRU). Denoised environmental data, including ammonia, temperature, relative humidity, carbon dioxide (CO2), and total suspended particles (TSP), were used to construct the forecasting model. The simulation results showed that the TCN model in general produced a more precise PI prediction and provided the highest prediction accuracy for all phases (MAE = 0.0842, 0.0859, and 0.1115; RMSE = 0.0154, 0.0167, and 0.0273; R2 = 0.9789, 0.9791, and 0.9635). The PI assessment prediction model based on TCN exhibited the best prediction accuracy and general performance compared with other parallel forecasting models and is a suitable and useful tool for predicting PI in waterfowl farms.

Bioinspired synthesis of zinc oxide nano-flowers: A surface enhanced antibacterial and harvesting efficiency.

Despite of broad range application, the cost effective, highly stable and reproduceable synthesis of ZnO is needed, especially which can make it biosafe as well. Here, a unique bioinspired synthesis of ZnO nanoflowers (NFs) has been introduced using Withania coagulans extract as reducing agent. Different molar concentrations were assessed to counter the effect of structural, morphological, antibacterial activity and high efficiency of algae harvesting. The UV-spectroscopy authenticates the synthesis of ZnO NFs having Wurtzite hexagonal structure with the size in the range of 360-550 nm. While surface analysis revealed the presence of stabilizing agent like phenolic, amine, etc. on surface of ZnO NFs. These perineum ZnO NFs exhibited a stronger antibacterial with Gram-positive bacteria Staphylococcus aureus as compare to Gram-negative bacteria Pseudomonas aeruginosa and greater harvesting efficiency up to 94% on the account of greater surface area and unique surface chemistry, thus leading a new horizon of more efficient and effective applications for ethanol production.

Physiological and anti-oxidative response of biologically and chemically synthesized iron oxide: Zea mays a case study.

The synthesis methodology, particle size and shape, dose optimization, and toxicity studies of nano-fertilizers are vital prior to their field application. This study investigates the comparative response of chemically synthesized and biologically synthesized iron oxide nanorods (NRs) using moringa olefera along with bulk FeCl3 on summer maize (Zea mays). It is found that FeCl3 salt and chemically synthesized iron oxides NRs caused growth retardation and impaired plant physiological and anti-oxidative activities at a concentration higher than 25 mg/L due to toxicity by over accumulation. While iron released form biologically synthesized NRs have shown significantly positive results even at 50 mg/L due to their low toxicity, an improved leaf area (13%), number of leaves per plant (26%), total chlorophyll content (80%) and nitrate content (6%) with biologically synthesized NRs are obtained. Moreover, the plant anti-oxidative activity also increased on treatment with biologically synthesized NRs because of their ability to form a complex with metal ions. These findings suggest that biologically synthesized iron oxides NRs are an efficient iron source and can last for a long time. Thus, proving that nanofertilizer are required to have specific surface chemistry to release the nutrient in an appropriate concentration for better plant growth.

Green synthesis of iron oxide nanorods using Withania coagulans extract improved photocatalytic degradation and antimicrobial activity.

Present work compares the green synthesis of iron oxide nanorodes (NRs) using Withania coagulans and reduction precipitation based chemical method. UV/Vis confirmed the sharp peak of Iron oxide NRs synthesized by biologically and chemically on 294 and 278 nm respectively. XRD and SEM showed highly crystalline nature of NRs with average size 16 ± 2 nm using Withania extract and less crystalline with amorphous Nanostructure of 18 ± 2 nm by chemical method. FTIR analysis revealed the involvement of active bioreducing and stabilizing biomolecules in Withania coagulans extract for synthesis of NRs. Moreover, EDX analysis indicates 34.91% of Iron oxide formation in biological synthesis whereas 25.8% of iron oxide synthesis in chemical method. The degradation of safranin dye in the presence of Withania coagulans based NRs showed 30% more effectively than chemically synthesized Nanorods which were verified by the gradual decrease in the peak intensity at 553 nm and 550 nm respectively under solar irradiation. Furthermore, Withania coagulans based NRs showed effective Antibacterial activity against S.aureus and P. aeuroginosa as compared to NRs by chemical method. Finally, we conclude that green synthesized NRs are more effective and functionally more efficient than chemically prepared NRs. Therefore, our work will help the researchers to boost the synthesis of nanoparticles via biological at commercial level.