Journals
2025 EN
Majumder Aditi · Sidenko Alexander · Jha Bharati
+3 more
We present an automated projection and interaction software thatcreates a multi‐projector display on objects of any size and shapein minutes by precision manipulation of light via computervision‐based algorithms. We call it “liquid light” due to the fluidlikecapability it imparts to the illumination, adapting quickly todifferent surfaces.
Journals
2025 EN
Gomaa Mona H. · Abdallah Samah S. · Ghayad Ibrahim M.
+2 more
ABSTRACT The synthesis of antibacterial nanoparticles is one of the most promising strategies to get rid of the primary threat that pathogenic bacteria pose to public health. Silver nanoparticles (AgNPs) are a promising antibacterial agent with robust and broad antibacterial characteristics that have the potential to resolve this issue. A straightforward reduction–impregnation approach for creating an Ag‐loaded SiO 2 nanocomposite has been presented in the present study. First, the well‐known Stöber method was employed to produce silica nanoparticles, which were subsequently examined utilizing an X‐ray diffraction (XRD), a field emission scanning electron microscope (FE‐SEM), and a high‐resolution transmission electron microscope (HR‐TEM). After that, a composite made of Ag@SiO 2 was produced and examined. AgNPs, which are loaded with silica and exhibit instantaneous and synergistic antibacterial activity against Gram‐positive “ Bacillus subtilis ” ( B. subtilis ) bacteria, were demonstrated. The coating layer that was produced also showed strong adherence to the steel substrate, a high inhibitory effect against the ( B. subtilis ), and versatility in its application across various sectors.
Journals
2025 EN
Heiling Hillary M. · Rashid Naim U. · Li Quefeng
+3 more
ABSTRACT Identifying and characterizing relationships between treatments, exposures, or other covariates and time‐to‐event outcomes has great significance in a wide range of biomedical settings. In research areas such as multi‐center clinical trials, recurrent events, and genetic studies, proportional hazard mixed effects models (PHMMs) are used to account for correlations observed in clusters within the data. In high dimensions, proper specification of the fixed and random effects within PHMMs is difficult and computationally complex. In this paper, we approximate the proportional hazards mixed effects model with a piecewise constant hazard mixed effects survival model. We estimate the model parameters using a modified Monte Carlo expectation conditional minimization (MCECM) algorithm, allowing us to perform variable selection on both the fixed and random effects simultaneously. We also incorporate a factor model decomposition of the random effects in order to more easily scale the variable selection method to larger dimensions. We demonstrate the utility of our method using simulations, and we apply our method to a multi‐study pancreatic ductal adenocarcinoma gene expression dataset to select features important for survival.
Journals
2025 EN
Hauser Paloma · Tan Xianming · Chen Fang
+1 more
ABSTRACT Vaccine safety monitoring is a critical component of public health given the extensive vaccination rate among the general population. However, most signal detection approaches overlook the inherently related biological nature of adverse events (AEs). We hypothesize that integrating AE field knowledge into the statistical process can facilitate and improve the accuracy of identifying vaccine‐AE associations. For this purpose, we propose a Bayesian generalized linear multiple low‐rank mixed model (GLMLRM) for analyzing high‐dimensional post‐market drug safety databases. The GLMLRM combines integration of AE ontology in the form of outcome‐level groupings, low‐rank matrices corresponding to these groupings to approximate the high‐dimensional regression coefficient matrix, a factor analysis model to describe the dependence among responses, and a sparse coefficient matrix to capture uncertainty in both the imposed low‐rank structures and user‐specified groupings. An efficient Metropolis/Gamerman‐within‐Gibbs sampling procedure is employed to obtain posterior estimates of the regression coefficients and other model parameters, from which testing of outcome‐covariate pair associations is based. The proposed approach is evaluated through simulation studies and is further illustrated by an application to the Vaccine Adverse Event Reporting System (VAERS).
Journals
2025 EN
Quinter Alexander · Tan Xianming · Zeng Donglin
+1 more
ABSTRACT Factor analysis provides an intuitive approach for dimension reduction when working with big data, allowing researchers to represent an extensive number of correlated variables via a subset of underlying latent factors. Traditional methods of factor analysis, such as Structural Equation Modeling (SEM) and factor regression, lack properties desirable for analyzing big data, such as the ability to handle high‐dimensionality or the ability to enforce sparsity on the estimates of the factor loading matrices. These methods also assume that the number of latent constructs is known beforehand, a problem unique to factor analysis that often goes unaddressed or overlooked, with ad hoc methods being the most common ways to deal with such a fundamental question. Although recent developments in the literature have attempted to remedy these issues, particularly with regard to expanding SEM to high‐dimensional and sparse applications, there is a noticeable lack of such methods that do so using likelihood theory. To rectify this shortcoming, we propose a new SEM‐based method for estimation that utilizes maximum likelihood theory while simultaneously addressing some of the most common problems associated with big data. We substantiate our method through simulation studies, indicating that the proposed method can correctly identify the latent factors underlying the independent and dependent sets of variables, while also accurately estimating the entries of and enforcing sparsity upon the factor loading matrix estimates. We apply this method to the COVIDiSTRESS Global Survey dataset, a global survey collected to further our understanding of how the COVID‐19 pandemic affected the human experience. Doing so demonstrates the performance of the model while simultaneously identifying the latent constructs intrinsic to the data.
Journals
2025 EN
Zhang Hongtao · Anderson Keaven M. · Zimmer Zachary
+3 more
ABSTRACT With the robust uptick in the applications of Bayesian external data borrowing, eliciting a prior distribution with the proper amount of information becomes increasingly critical. The prior effective sample size (ESS) is an intuitive and efficient measure for this purpose. The majority of ESS definitions have been proposed in the context of borrowing control information. Meanwhile, Bayesian borrowing is frequently conducted on the treatment effect scale to extrapolate evidence in pediatric or global trials. While many Bayesian models can be naturally extended to leveraging external information on the treatment effect scale, very little attention has been directed to computing the prior ESS in this setting. In this research, we bridge this methodological gap by extending the popular expected local information ratio (ELIR) ESS definition. We lay out the general framework, and derive the ESS for various types of endpoints and treatment effect measures. The desirable predictive consistency property of ELIR ESS is examined and found to only be preserved for the difference between two normal endpoints. The methods are implemented in R programs available on GitHub: https://github.com/squallteo/TrtEffESS .
Journals
2025 EN
Alt Ethan M. · Anderson Anil · Li Qing
+3 more
ABSTRACT A common issue in randomized clinical trials (RCTs) is the identification of subgroups and the estimation of their effects. Typically, RCTs are not powered to estimate the effects of subgroups. However, in some circumstances, treatment may work for some groups and not others, and it is of interest to identify these subgroups and estimate their treatment effects. In this paper, we introduce a novel hierarchical grouped horseshoe prior (HGHP) for subgroup identification and estimation. We show via simulation that our proposed approach yields superior positive predictive value and narrower credible intervals compared to other shrinkage priors. We apply our method to a real clinical trial for COVID‐19.
Journals
2025 EN
Mountessou Bel Youssouf G. · Mbah Maraf B. · Wandji Nadine T.
+5 more
Abstract In the discovery of antiviral drugs, targeting the 3‐chymotrypsin‐like cysteine protease (3CLpro) of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is advantageous for developing a broad‐spectrum antiviral agent for the treatment of coronaviruses and other respiratory viruses, such as rhinoviruses. The promising biological activities of linear furanocoumarins reported in the literature, in addition to their quantum electronic properties, led to the in silico study of these compounds. Analysis of chemical reactivity descriptors of 3‐methoxypsoralen ( 1 ) and 3,5‐dimethoxypsoralen ( 2 ) using DFT and ab initio Hartree–Fock (HF) methods indicates that the compounds are more water‐soluble, which is a favorable characteristic for clinical applications. Vibrational spectroscopy data are also presented. Molecular docking results revealed that 1 and 2 strongly bind to 3CLpro with relative affinities of −5.5 and −5.6 kcal mol −1 , respectively. They were also docked against the human rhinovirus HRV3C main protease, with relative affinities of −8.4 and −7.3 kcal mol −1 , respectively.
Journals
2025 EN
Othman Sara Ibrahim · Kamel Fouad H.
Abstract This study reports the green synthesis of silver nanoparticles (AgNps) by using Lactobacillus reuteri . Characterized AgNps exhibited near‐spherical morphology with an average size of 86.54 nm, as revealed by SEM (scanning electron microscopy), TEM (transmission electron microscopy), and AFM (atomic force microscopy) analyses. The nanoparticles displayed a slightly rough surface, with some particles exhibiting surface protrusions and sharp edges. The zeta potential measurement of −30.16 mV and electrophoretic mobility of −2.36 cm 2 /Vs that indicated the presence of negatively charged functional groups on the AgNps surface, suggesting stabilizing biomolecules that prevent nanoparticle aggregation in solution. The hydrodynamic size of the AgNps was determined to be 140.3 nm, and XRD (X‐ray diffraction) analysis revealed a crystallinity of 76.89% with an average crystal size of 8.35 nm and an interplanar distance of 0.233 nm. AgNps demonstrated crystallinity and potent antibacterial activity against Pseudomonas aeruginosa (92.9% killing efficiency) and an ATCC reference strain (76.8%). Molecular docking revealed moderate interactions between an optimized Ag 3 cluster and key amino acid residues (Arg277, Gln279, Trp280, Asp273, and Val276) in the P. aeruginosa protein . These interactions, with binding energies ranging from −1.89 to −3.68 kcal/mol, suggested a potential mechanism for the observed antibacterial activity. A charge transfer interaction involving Asp273, contributing a stabilization energy of 3.66 kcal/mol, was identified as a key factor in the Ag‐protein complex formation.
Journals
2025 EN
Rauf Abdur · Ibrahim Muhammad · Ahmad Zubair
+9 more
Abstract This study explored the synthesis and characterization of the iron nanoparticles (FeNPs) using Micromeria biflora extract. The rapid reduction of iron ions, evidenced by a distinct color change, signifies an efficient interaction, leading to successful FeNPs formation. UV–visible spectroscopy confirmed the synthesis, revealing an absorption peak at 295 nm that intensified over time. Fourier transform infrared (FTIR) spectroscopy demonstrates phytochemical involvement. Field emission scanning electron microscopy (FESEM) images displayed cuboctahedron‐shaped NPs with various facet formations, which are crucial for diverse applications. DISCUS package was used to simulate the shape and decorate the surface with organic molecules obtained from the extract. Energy dispersive X‐ray spectroscopy (EDS) was used to confirm the elemental composition. Additionally, potential applications, including enzyme effects and sedative and anti‐inflammatory properties, were explored. The extract and FeNPs showed anticancer effects against MDR2780AD cell lines, with IC 50 values of 1.99 and 0.91, respectively. The tested FeNPs showed 92.22%, 76.22%, and 88.23% inhibitory effects against urease, CA‐II, and XO, respectively. The maximum percentage analgesic effects of the extract (100 mg/kg) and FeNPs (10 mg/kg) were 65 and 82, respectively. The maximum anti‐inflammatory effect was observed at the third hour of treatment. The anti‐inflammatory effect of FeNPs (90%) was superior to that of the extract (60%).