Nanomaterials have been predicted to play a key role as catalysts in the renewable biofuels production process by altering the reaction mechanism. In biomass to the biofuels production process, iron ...performs major activity as the cofactor of sugar and biofuels producing enzymes which support microbial growth. The use of iron-based nanomaterials improves the biomass to biofuels production process and perhaps reduces the production cost due to its use in very low amount as the catalyst. Additionally, iron-based nanomaterials prepared via green route are known to support low cost of the biomass to renewable energy production through thermochemical and biochemical routes wherein the major cost is perhaps due to the catalyst synthesis. Though the green synthesis route of nanomaterial is non-toxic and sustainable, the lack of detailed information about the green synthesis and its mechanism continues to be an important issue. In this review different existing routes to synthesis of iron-based nanomaterials like using microorganism, green plants and biomass have been discussed in details along with their possible mechanisms involved therein. Additionally, impacts of various parameters employed in the green synthesis route have been discussed to explore the exact picture on the physicochemical properties of the synthesized nanomaterials. Finally, applications of iron-based nanomaterials as catalysts in thermochemical and biochemical energy production (e.g. liquid hydrocarbon, hydrogen) are presented and discussed. This comprehensive review provides a new insight on the bioinspired synthesis of iron-based nanomaterials and their applications to advance the existing biofuels production processes towards its sustainable commercialization through waste to value added technology.
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•Comprehensive review of bioinspired synthesis of iron-based nanomaterials.•Biological routes e.g. microorganism, green plants and biomass mediated synthesis.•Mechanisms involved in bio-induced synthesis of iron-based nanomaterials.•Application as catalyst in thermochemical and biochemical energy production.•Implement to advanced the existing biofuels production technology.
Objective
To estimate the etiology, outcome, and risk factors for mortality in children with community-acquired acute kidney injury (CA-AKI).
Methods
Between October, 2020 and December, 2021, ...consecutive hospitalized children aged 2 mo-12 years with a minimum 24 hours of stay, and at least one serum creatinine level measured at or within 24 hours of hospitalization were prospectively enrolled. CA-AKI was labelled in children with an elevated serum creatinine level at admission and subsequent fall during hospitalization.
Results
Of 2780 children, 215 were diagnosed as CA-AKI (7.7%, 95% CI 6.7–8.6). Diarrhea with dehydration (39%) and sepsis (28%) were the most common causes of CA-AKI. 24 children (11%) died during hospitalization. Requirement of inotropes was an independent predictor of mortality. Out of 191 children discharged, 168 (88%) had complete renal recovery. At 3 months, out of 22 children without complete renal recovery, 10 progressed to chronic kidney disease (CKD), with 3 becoming dialysis dependent.
Conclusions
CA-AKI is common in hospitalized children, and is associated with increased risk of progression to CKD, especially in those with incomplete renal recovery.
Epitopes are the cornerstones for the development of rational vaccine design strategies. Conventionally, epitopes are used by chemical conjugation with the carrier protein. This chapter describes our ...computational epitope grafting methodology to identify the preferential grafting site in a carrier protein/scaffold. We have used the mota epitope as an example, as it was already experimentally validated by an independent group. In this chapter, we have provided sufficient details to enable the wet experimentalist to employ this computational methodology in their research objective. Scripts/programs are extensively described in this chapter and freely accessible through the provided link.
The focus of this paper revolves around the examination of flow of ternary hybrid nanofluid, specifically the Al2O3–Cu-CNT/water mixture, with buoyancy effect, across three distinct geometries: a ...wedge, a flat plate, and a cone. The study takes into account the presence of quadratic thermal radiation and heat source/sink of non-uniform nature. To develop the model, the Cattaneo–Christov theory is utilized. The equations governing the flow are solved by applying similarity transformations and employing the "bvp4c function in MATLAB” for numerical analysis and solution. Conventional methods for conducting parametric studies often face challenges in producing significant conclusions owing to the inherent complex form of the model and the method involved. To address the aforementioned issue, this paper explores the potential of machine learning methods to foresee the conduct of the flow characterized by multiple interconnected parameters. By utilizing simulated data, an artificial neural network is trained using the Levenberg-Marquardt algorithm to learn and comprehend the underlying patterns. Subsequently, the trained neural network is employed to estimate the Nusselt number on the surfaces of all three geometries. This approach offers a promising alternative to traditional parametric studies, enabling more precise predictions and insights into the behavior of complex systems. The Nusselt number is highest for THNF flow over the cone. The mean squared error (MSE) values for the ANN algorithm, across all analyzed cases, range from 0 to 0.03972. The findings contribute to an improved understanding of the characteristics and dynamics of ternary hybrid nanofluid flow in various geometries, assisting in the design and optimization of heat transfer systems involving such fluids.
Abstract
The late 2019 outbreak of Coronavirus Disease (COVID-19) had an indelible imprint on the humanity. The world is recovering from the outbreak but there is danger of a second wave of the ...outbreak. To get rid of the outbreak it is necessary to prevent the viral transmission and it is need of the hour to maintain social distancing and wear masks in public areas. The governments are providing strict guidelines to wear masks in public places. It is not manually feasible to check if people are wearing masks or not. In this paper, process of detecting face masks in public places is automated using Convolutional Neural Networks by performing comparative analysis on Sequential bi-layered CNN, VGG-16 CNN and MobileNetV2 CNN architectures. Among these three architectures MobileNetV2 outperformed with a performance accuracy of 99.2%. The efficient Deep Learning architecture of detecting face masks can be achieved with the help of IoT (Internet of Things) devices and cameras, of those who are not following guidelines in public places. Such a system is very useful in post outbreak period and can be installed in public places such as Railway Stations, Airports, Parks, Schools, colleges, offices etc. to track and ensure wearing of masks by people. The contribution of this paper is not to reel-off the finding from the original paper on Face Mask detection with various architectures rather to provide results on the efficiency of using the MobileNetV2 architecture in comparison with Sequential CNN and VGG-16 architectures for crowd analysis mask detection.
Leucine Rich Repeats-receptor-like protein kinases (LRR-RLKs) regulate several critical biological processes ranging from growth and development to stress response. Thinopyrum elongatum harbours many ...desirable traits such as biotic and abiotic stress resistance and therefore commonly used by wheat breeders. In the present investigation, in-silico analysis of LRR-RLKs yielded 589 genes of which 431 were membrane surface RLKs and 158 were receptor like cytoplasmic kinases. An insight into the gene and protein structure revealed quite a conserved nature of these proteins within subgroups. A large expansion in LRR-RLKs was due to tandem and segmental duplication event. Maximum number of tandem and segmentally duplicated pairs was observed in LRR-VI and LRR-XII subfamily, respectively. Furthermore, syntenic analyses revealed that chromosome 6 harboured more (48) tandem duplicated genes while chromosome 7 possessed more (47) segmentally duplicated genes. A detailed analysis about the gene duplication events coupled with expression profiles during Fusarium graminearum infection and water deficiency unravelled the expansion of the gene family with sub functionalization and neofunctionalization. Interaction network analysis showed that LRR-RLKs can heterodimerize upon ligand binding to perform various plant functional attributes.
•Overexpression of SbMT-2 gene (cloned from Salicornia brachiata) in tobacco plant.•SbMT-2 enhanced Zn-stress tolerance by increased photosynthesis efficiency.•SbMT-2 might be involved in the ...selective translocation of Zn2+.•Microarray revealed differential expression of key metal-associated genes.•SbMT-2: A potential candidate for metal tolerance and heavy metal phytoremediation.
Metallothioneins are cysteine-rich proteins, which play key roles in metal detoxification, intracellular ion homeostasis maintenance, and protection against intracellular oxidative damage. This study reports the characterization of SbMT-2 cloned from Salicornia brachiata concerning physiology, molecular, and photosynthesis efficiency. Overexpression of SbMT-2 conferred enhanced tolerance to Zn stress in transgenic tobacco supported by increased photosynthesis efficiency and crop quality compared to wild-type. The growth of transgenic and WT plants was comparable in control conditions; transgenic plants showed better growth than WT plants under stress (20 mM ZnSO4 for 30 days). After 30 days of stress, transgenic plants completed their life cycle with early maturation, whereas the WT plants did not and matured very late. About 35 pods with a total weight of 3.5 g per plant were measured in transgenic compared to WT (about 19 pods with total weight 2.5 g). Further, gas exchange and fluorescence measurements established that the SbMT-2 gene improved the photosynthetic efficiency of the transgenic line compared to WT plants during stress conditions. In addition, SbMT-2 might be involved in the selective translocation of Zn2+ under long-term stress condition. Microarray analysis showed that the expression of many Zn transporters, Zn-binding proteins, and Zn-associated proteins encoding genes was up-regulated 4–6 (log2) fold. However the expression of genes encoding other metal-binding proteins, metal metabolism-associated enzymes, and metal-inducible transcription factors were down-regulated. Furthermore, transcript expression analysis elucidated that overexpression of the SbMT-2 gene may regulate the expression pattern of metal transporter encoding genes under stress conditions. Thus, SbMT-2 is an important gene, which plays a positive role by detoxifying reactive oxygen species and maintaining photosynthesis efficiency under Zn stress conditions.
Titanium allergy: a literature review Goutam, Manish; Giriyapura, Chandu; Mishra, Sunil Kumar ...
Indian journal of dermatology,
11/2014, Volume:
59, Issue:
6
Journal Article
Peer reviewed
Open access
Titanium has gained immense popularity and has successfully established itself as the material of choice for dental implants. In both medical and dental fields, titanium and its alloys have ...demonstrated success as biomedical devices. Owing to its high resistance to corrosion in a physiological environment and the excellent biocompatibility that gives it a passive, stable oxide film, titanium is considered the material of choice for intraosseous use. There are certain studies which show titanium as an allergen but the resources to diagnose titanium sensivity are very limited. Attention is needed towards the development of new and precise method for early diagnosis of titanium allergy and also to find out the alternative biomaterial which can be used in place of titanium. A review of available articles from the Medline and PubMed database was done to find literature available regarding titanium allergy, its diagnosis and new alternative material for titanium.
This study is an attempt to quantitatively test and compare novel advanced-machine learning algorithms in terms of their performance in achieving the goal of predicting flood susceptible areas in a ...low altitudinal range, sub-tropical floodplain environmental setting, like that prevailing in the Middle Ganga Plain (MGP), India. This part of the Ganga floodplain region, which under the influence of undergoing active tectonic regime related subsidence, is the hotbed of annual flood disaster. This makes the region one of the best natural laboratories to test the flood susceptibility models for establishing a universalization of such models in low relief highly flood prone areas. Based on highly sophisticated flood inventory archived for this region, and 12 flood conditioning factors viz. annual rainfall, soil type, stream density, distance from stream, distance from road, Topographic Wetness Index (TWI), altitude, slope aspect, slope, curvature, land use/land cover, and geomorphology, an advanced novel hybrid model Adaptive Neuro Fuzzy Inference System (ANFIS), and three metaheuristic models-based ensembles with ANFIS namely ANFIS-GA (Genetic Algorithm), ANFIS-DE (Differential Evolution), and ANFIS-PSO (Particle Swarm Optimization), have been applied for zonation of the flood susceptible areas. The flood inventory dataset, prepared by collected flood samples, were apportioned into 70:30 classes to prepare training and validation datasets. One independent validation method, the Area-Under Receiver Operating Characteristic (AUROC) Curve, and other 11 cut-off-dependent model evaluation metrices have helped to conclude that the ANIFS-GA has outperformed other three models with highest success rate AUC = 0.922 and prediction rate AUC = 0.924. The accuracy was also found to be highest for ANFIS-GA during training (0.886) & validation (0.883). Better performance of ANIFS-GA than the individual models as well as some ensemble models suggests and warrants further study in this topoclimatic environment using other classes of susceptibility models. This will further help establishing a benchmark model with capability of highest accuracy and sensitivity performance in the similar topographic and climatic setting taking assumption of the quality of input parameters as constant.
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•This is the first time use of ANFIS and its advanced ensembles for flood susceptibility mapping in the low relief topographic environment such as MGP•We have trained and evaluated the model through multiple dependent and independent metrices•The reported model performance is the best achieved performance by any fuzzy approach for the region
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