Browse Articles

FDA approves cell-sheet-based gene therapy for severe skin disease

Oral curative therapeutic study of N-nitrosodiethylamine-induced mouse liver damage of chitosan-coated ursolic acid niosomes

Understanding LAG3 immune checkpoint function

Novel approximate adaptive carry lookahead adder for error resilient applications with generic method for error analysis

Abstract The paper presents a novel architecture of approximate adder and a novel generic error analysis method. The proposed architecture judiciously make use of time axis based parallelism of components to improve delay and simultaneously improvement in error parameters due to adaptive increase in group-size of carry generating blocks. The synthesis of architectures for same bit length has shown area, power, and delay improvement by 4.91%, 5.59%, 14.92% respectively with respect to state of the art architectures based on truncation of carry chain when synthesized under same constraints and same operating conditions. In comparison to ETA-I, ETA-II and GeAr, proposed method has shown improvement in delay by 9%, 17.9% and 21.3% respectively. Error analysis is done for proposed adder using random probabilistic method and generic analysis method. The generic error analysis method has shown error parameter results are in close agreement with values found with application of binary random numbers, with very large sample size, for all adder architectures with different size, group-size and window-size. Generic analysis method and random probabilistic method based error rate calculation varies by the least, with a value of 0.49% and a maximum of 13.85%.

Ramping up mitochondrial DNA replication

Possible nonstellar explanation for the unexpected brightness of the earliest galaxies observed by the James Webb Space Telescope

TIM-3 regulates microglial function

Enhanced manganese ion removal from aqueous solution using graphene oxide nanocollector in ion flotation: mechanism, efficiency, and recyclability

Lasso peptide targets drug-resistant bacteria

Functional metabolites and inhibitory efficacy of kombucha beverage on pathogenic bacteria, free radicals and inflammation

Constructing a predictive model of negative academic emotions in high school students based on machine learning methods

Targeting RNA structure to treat prostate cancer

Bird mortality at wind farms in a tropical desert

Sequential structure probing of cotranscriptional RNA folding intermediates

Leaf functional metabolic traits reveal the adaptation strategies of larch trees along the R/B ratio gradient at the stand level

New antifungal tackles drug-resistant infections

A naturally occurring SNP modulates thermotolerance divergence among grapevines

Magnetohydrodynamic Maxwell hybrid nanofluid flow and heat transfer over a moving needle in porous media

Mechanism of ATP hydrolysis in the Hsp70 BiP nucleotide-binding domain

Abstract The 70 kDa heat shock protein (Hsp70) family of molecular chaperones ensures protein biogenesis and homeostasis, driven by ATP hydrolysis. Here, we introduce in-cyclo NMR , an experimental setup that combines high-resolution NMR spectroscopy with an ATP recovery and a phosphate removal system. In-cyclo NMR simultaneously resolves kinetic rates and structural information along functional cycles of ATP-driven molecular machines. We benchmark the method on the nucleotide binding domain (NBD) of the human Hsp70 chaperone BiP. The protein cycles through ATP binding, hydrolysis, and two parallel pathways of product release. We determine the kinetic rates of all eleven underlying elementary reactions and show these to match independent measurements. The two product release pathways regulate the cycle duration dependent on the products concentration. Under physiological conditions, they are both used. The in-cyclo NMR method will serve as a platform for studies of ATP-driven functional cycles at a remarkable level of detail.

Enhancing pH prediction accuracy in Al2O3 gated ISFET using XGBoost regressor and stacking ensemble learning

Abstract An ion-sensitive field-effect transistor (ISFET) is widely used in environmental and biomedical applications due to its rapid response, miniaturization, and cost-effectiveness. In this study, a numerical model of an Al₂O₃-gated ISFET was developed to detect pH levels. The effects of gate dielectric thickness, doping concentration, and temperature on ISFET’s performance were evaluated using IDS–VDS characteristics. An eXtreme Gradient Boosting (XGBoost) regression model was employed to predict pH levels using data obtained from IDS–VDS characteristics. Further, Hyperparameter optimization was performed to tune critical XGBoost-hyperparameters such as maximum depth, minimum child weight, estimators, learning rate, α, and λ. The optimization strategies such as random search, grid search and Bayesian optimization were utilized to improve the efficacy of regressor by minimizing errors and maximizing accuracy in prediction. A stacking ensemble learning approach was also implemented to integrate multiple models, enhancing prediction accuracy and thereby capturing additional information. The XGBoost regressor achieved superior results with R2 = 0.9846, MSE = 0.2342, and MAE = 0.2317, compared to other regressor models. Therefore, the use of XGBoost regressors with hyperparameter optimization and stacking ensemble learning approach is found to be highly effective for pH prediction from ISFET under various operating conditions.