Browse Articles

Lensless magneto-optical imaging

Abstract Magneto-optical methods, which utilize the interaction of polarized light with the magnetization of the sample in reflection through the magneto-optical Kerr effect or in transmission through the accordant Faraday effect, present prominent and widespread optical microscopy techniques for studying magnetic microstructures. In non-magnetic light microscopy, several alternatives to lens-based imaging have been developed, which offer various advantages, including an improved ratio of field-of-view to magnification. Selected lensless methods also provide access to both intensity and phase information of the probing light field, which presents an additional information channel obtainable from the studied sample. In a proof-of-principle study we verify that the reconstructed magneto-optical intensity obtained from a lensless multiplane recording scheme is in full qualitative agreement with conventional lens-based Faraday microscopy. The additional phase information, not accessible with conventional methods, offers direct access to domain information through the imaginary part of the Faraday or Kerr component in the studied material and allows domain imaging even in a crossed analyzer position or without the use of an analyzer. These findings will open the path to exploit the various established advantages of lensless microscopy for the magneto-optical investigation of magnetic materials.

Identification of mitochondria-related biomarkers for acute respiratory distress syndrome

Efficient enamel subsurface lesion remineralisation and dentine tubule occlusion by high concentration CPP-ACP: a randomised, cross-over in situ study

Development and validation of a gut motility based model for predicting bowel preparation quality

A holistic methodology for evaluating flood vulnerability, generating flood risk map and conducting detailed flood inundation assessment

Abstract Flood risk assessment (FRA) is a process of evaluating potential flood damage by considering vulnerability of exposed elements and consequences of flood events through risk analysis which recommends the mitigation measures to reduce the impact of floods. This flood risk analysis is a technique used to identify and rank the level of flood risk through modeling and spatial analysis. In the present study, Musi River in the Osmansagar basin is taken in to consideration to evaluate the flood risk, which is located at Hyderabad. The input data collected for the study encompasses Hydrological and Meteorological datasets from Gandipet Guage station in Hyderabad, raster grid data for Osmansagar basin along with several indicators data influencing flood vulnerability. The primary research objective is to conduct a quantitative assessment of the Flood vulnerability index (FVI), to develop a comprehensive flood risk map and to evaluate the magnitude of damaging flood parameters, inundated volume and to analyze the regions inundated in the study area. In risk analysis, FVI determines the degree of which an area is susceptible to the negative impact of flood through various influencing indicators, Flood hazard map segregate the regions based on flood risk level through spatial analysis in Arc-GIS. A part of this study includes an integrated methodology for assessing flood inundation using Quantum Geographic Information Systems (QGIS) data modelling for spatial analysis, Hydraulic Engineering Center’s River Analysis System (HEC-RAS) hydraulic modelling for unsteady flow analysis and a machine learning technique i.e. XGBoost, to enhance the accuracy and efficiency of flood risk assessment. Subsequently, inundation map produced using HEC-RAS is superimposed with building footprints to identify vulnerable structures. The results obtained by risk analysis using hydraulic modeling, GIS analysis, and machine learning technique illustrates the flood vulnerability, areas having high flood risk and inundated volume along with predicted flood levels for next 10 years. These findings demonstrate the efficiency of the holistic approach in identifying vulnerability, flood-prone areas and evaluating potential impacts on infrastructure and communities. The outcomes of the study assist the decision-makers to gain valuable insights into flood risk management strategies.

Sensory properties of fermented Zamné (Senegalia macrostachya seeds) and their influence on the broth quality and sensory profile

Relationship between height of cheer basket toss and specific physical ability of bases from a kinematic perspective

Sleep mediates the association between stroke and all cause mortality in the NHANES cohort

Time series analysis of urethral obstruction in male cats in a veterinary teaching hospital in São paulo, Brazil

Thermodynamic analysis and intelligent modeling of statin drugs solubility in supercritical carbon dioxide

Prediction of aggregation in monoclonal antibodies from molecular surface curvature

Abstract Protein aggregation is one of the key challenges in the biopharmaceutical industry as its control is crucial in achieving long-term stability and efficacy of biopharmaceuticals. Attempts have been made to develop regression models for predicting the aggregation of monoclonal antibodies in solution using machine learning methods. These efforts have yielded varying levels of success, with current state-of-the-art AI approaches achieving good prediction accuracies ( $$r=0.86$$ ). Here, we demonstrate the prediction of aggregation rate in monoclonal antibodies with beyond state-of-the-art reliability using a coupled AI-MD-Molecular surface curvature modelling platform. The scientific novelty of this approach lies in using local geometrical surface curvature of proteins as the core element for protein stability analysis. By combining local surface curvature and hydrophobicity, as derived from time-dependent MD simulations, we are able to construct aggregation predictive features that, when coupled with linear regression machine learning techniques, give a high prediction accuracy ( $$r=0.91$$ ) on a dataset of 20 molecules. More generally, this approach shows significant potential for quantitative in silico screening and prediction of protein aggregation, which is of great scientific and industrial relevance, particularly in biopharmaceutics.

A machine learning approach for significant utilization of high-ash Indian coals by metal chloride modification

Glycemic levels and cardiovascular events in type 2 diabetes: A cohort study of drugs with different hypoglycemic potentials

Statistical optimization of process variables for enhanced serratiopeptidase production from soil Serratia marcescens VS56

Identification and classification method of landslide pattern in the soil water index-based early warning system

The contribution of integrated and non-integrated pig farms to epidemiological dynamics of porcine reproductive and respiratory syndrome virus in Italy

Progress of chronic kidney disease and associated predictors among patients under treatment at Gambi and Felege-Hiwote hospitals

Using five exposure metrics to explore the association between ambient PM2.5 and the hospital admissions for COPD in Tianshui city, China

Dentists’ role in oral care for head and neck cancer patients in GCC countries

UPLC/MSn analysis of Bougainvillea glabra leaves and investigation of antioxidant activities and enzyme inhibitory properties

Abstract Bougainvillea glabra is a well-known and well-documented ornamental plant belonging to family Nyctaginaceae, always planted for its beautifully colored flowers. The current study was performed for profiling the metabolites of B. glabra leaf extract. Further, the leaf extract was evaluated using the total phenolic and total flavonoid assays and profiling using UPLC/MSn for the leaf extract’s secondary metabolites. The leaf extract was also subjected to several antioxidant assays, viz. DPPH, ABTS, CUPRAC, FRAP, metal chelating, and phosphomolybdenum tests, as well as enzyme inhibition assays such as α-amylase, α-glucosidase, acetylcholinesterase (AChE), and butyrylcholinesterase (BChE). The results showed that the total phenolic and flavonoid contents were 27.68 mg GAE/g and 31.76 mg RE/g, respectively. Besides, twenty-one metabolites were tentatively identified and quantified, where flavonoids and phenolic acids constituted the most abundant classes of compounds. The molecular docking experimentss showed that the most abundant components, namely rhamnocitrin-O-rutinoside, sagerinic acid, tri-O-caffeoyl-shikimic acid, and chlorogenic acid, had the best scores when docked in the vicinity of the selected enzyme targets. The extract recorded a potentially powerful antioxidant activity, compared to the used standards, while it showed a good inhibitory effect against AChE (2.40 mg GALAE/g) and BChE (1.95 mg GALAE/g). The tyrosinase-inhibiting effect was 48.23 mg CAE/g. The amylase and glucosidase inhibitory effects were 0.30 mmol ACAE/g and 0.03 mmol ACAE/g, respectively. Thus, this study suggests that B. glabra may not only act as an ornamental plant, but also it may be a promising source for effective phytochemicals that act as antioxidants and enzyme inhibitors, which may play a role in reversing the aging process and age-related ailments like diabetes.