We report results of our study on the adsorption of CO on CuPd surfaces with bulk stoichiometric and nonstoichiometric layers using density functional theory (DFT). We found that the presence of Pd atoms in the subsurface layer promotes the adsorption of CO. We also observed CO-induced Pd segregation on the CuPd surface and we attribute this to the strong CO-Pd interaction. Lastly, we showed that the adsorption of CO promotes Pd-Pd interaction as compared to the pristine surface which promotes strong Cu-Pd interaction. These results indicate that CO adsorption on CuPd surfaces can be tuned by taking advantage of the CO-induced segregation and by considering the role of subsurface Pd atoms.

Machine learning (ML) is poised as a transformational approach uniquely positioned to discover the hidden biological interactions for better prediction and diagnosis of complex diseases. In this work, we integrated ML-based models for feature selection and classification to quantify the risk of individual susceptibility to asthma using single nucleotide polymorphism (SNP). Random forest (RF) and recursive feature elimination (RFE) algorithm were implemented to identify the SNPs with high implication to asthma. K-nearest neighbor (kNN) and support vector machine (SVM) algorithms were trained to classify the identified SNPs whether associated with non-asthmatic or asthmatic samples. Feature selection step showed that RF outperformed RFE and the feature importance score derived from RF was consistently high for a subset of SNPs, indicating the robustness of RF in selecting relevant features associated with asthma. Model comparison showed that the integration of RF-SVM obtained the highest model performance with an accuracy, precision, and sensitivity of 62.5%, 65.3%, and 69%, respectively, when compared to the baseline, RF-kNN, and an external MeanDiff-kNN models. Furthermore, results show that the occurrence of asthma can be predicted with an Area under the Curve (AUC) of 0.62 and 0.64 for RF-SVM and RF-kNN models, respectively. This study demonstrates the integration of ML models to augment traditional methods in predicting genetic predisposition to multifactorial diseases such as asthma.

Recent studies show the potential of artificial intelligence (AI) as a screening tool to detect COVID-19 pneumonia based on chest x-ray (CXR) images. However, issues on the datasets and study designs from medical and technical perspectives, as well as questions on the vulnerability and robustness of AI algorithms have emerged. In this study, we address these issues with a more realistic development of AI-driven COVID-19 pneumonia detection models by generating our own data through a retrospective clinical study to augment the dataset aggregated from external sources. We optimized five deep learning architectures, implemented development strategies by manipulating data distribution to quantitatively compare study designs, and introduced several detection scenarios to evaluate the robustness and diagnostic performance of the models. At the current level of data availability, the performance of the detection model depends on the hyperparameter tuning and has less dependency on the quantity of data. InceptionV3 attained the highest performance in distinguishing pneumonia from normal CXR in two-class detection scenario with sensitivity (Sn), specificity (Sp), and positive predictive value (PPV) of 96%. The models attained higher general performance of 91-96% Sn, 94-98% Sp, and 90-96% PPV in three-class compared to four-class detection scenario. InceptionV3 has the highest general performance with accuracy, F1-score, and g-mean of 96% in the three-class detection scenario. For COVID-19 pneumonia detection, InceptionV3 attained the highest performance with 86% Sn, 99% Sp, and 91% PPV with an AUC of 0.99 in distinguishing pneumonia from normal CXR. Its capability of differentiating COVID-19 pneumonia from normal and non-COVID-19 pneumonia attained 0.98 AUC and a micro-average of 0.99 for other classes.

Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the “missing heritability” problem. This study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis. The proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1) single nucleotide polymorphisms (SNPs) considered significant by GWAS and (2) SNPs with the highest feature importance scores obtained using random forest. The resulting SNP-sets from the cluster analyses were subsequently tested for trait-association. Three susceptibility loci possibly associated with HBsAg seroclearance were identified: (1) SNP rs2399971, (2) gene LINC00578, and (3) locus 11p15. SNP rs2399971 is a biomarker reported in the literature to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment. The latter two loci are linked with diseases influenced by the presence of hepatitis B virus infection. These findings demonstrate the potential of the proposed integrated framework in identifying disease-associated susceptibility loci. With further validation, results herein could aid in better understanding complex disease etiologies and provide inputs for a more advanced disease risk assessment for patients.

Classical nucleation theory predicts that the evolution of mean island density with temperature during growth in one-dimensional systems obeys the Arrhenius relation. In this study, kinetic Monte Carlo simulations of a suitable atomistic lattice-gas model were performed to investigate the experimentally observed non-Arrhenius scaling behavior of island density in the case of one-dimensional Al islands grown on Si(100). Previously, it was proposed that adatom desorption resulted in a transition temperature signaling the departure from classical predictions. Here, the authors demonstrate that desorption above the transition temperature is not possible. Instead, the authors posit that the existence of a transition temperature is due to a combination of factors such as reversibility of island growth, presence of C-defects, adatom diffusion rates, as well as detachment rates at island ends. In addition, the authors show that the anomalous non-Arrhenius behavior vanishes when adatom binds irreversibly with C-defects as observed in In on Si(100) studies.

Nanostructured deposits of ammonia (NH 3 ) sensitive ZnO and ZnO-CuO composites were fabricated on a graphite electrode via electrophoretic deposition (EPD). Deposition was done by holding the applied voltage and deposition time constant at room temperature. Testing of sensing properties of the deposits was conducted using Wheatstone bridge circuit. SEM micrographs show a more open structure and more exposed surface area of the pure ZnO deposit compared to the ZnO-CuO deposit. The average particle size deposited at 500V for ZnO and ZnO-CuO were 241nm and 260nm respectively; whereas at 750V the average particle size is 195nm and 276nm, respectively. Deposits with greater surface area, smaller particle sizes and thicker deposits exhibit high gas sensitivity. On the other hand, addition of CuO resulted to a more compact and dense surface structure and decreased gas sensitivity. Thus, particle size and the surface structure of the deposits dictate the sensitivity of the material.

IntroductionIdentifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the “missing heritability” problem.ObjectivesThis study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis.MethodsThe proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1) single nucleotide polymorphisms (SNPs) considered significant by GWAS and (2) SNPs with the highest feature importance scores obtained using random forest. The resulting SNP-sets from the cluster analyses were subsequently tested for trait-association.ResultsThree susceptibility loci possibly associated with HBsAg seroclearance were identified: (1) SNP rs2399971, (2) gene LINC00578, and (3) locus 11p15. SNP rs2399971 is a biomarker reported in the literature to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment. The latter two loci are linked with diseases influenced by the presence of hepatitis B virus infection.ConclusionThese findings demonstrate the potential of the proposed integrated framework in identifying disease-associated susceptibility loci. With further validation, results herein could aid in better understanding complex disease etiologies and provide inputs for a more advanced disease risk assessment for patients.

The dynamic scaling of the island size distribution (ISD) in the submonolayer growth regime of low-dimensional nanostructured systems is a long standing problem in epitaxial growth. In this study, kinetic Monte Carlo simulations of a realistic atomistic lattice-gas model describing the one-dimensional nucleation and growth of Al on Si(100):2×1 were performed to investigate the scaling behavior under varied growth conditions. Consistent with previous predictions, our results show that the shape of the scaled island size distribution can be altered by controlling the temperature and the C-defect density. The shifts in the scaled ISD are opposite to each other with temperature depending on the density of C-defects. For low C-defect density, a shift from a monomodal to a monotonically decreasing distribution as temperature increases is observed. We attribute the monomodal distribution to enhanced nucleation and aggregation whereas a monotonically decreasing distribution is attributed to restricted aggregation with defects playing only a minor role. At higher C-defect density, we show that the scaled ISD shift is from a monotonically decreasing to a monomodal distribution with increasing temperature. We argue that the reversal of the shift is due to competing effects introduced by high C-defect concentration. In addition, results show that the ISD is generally insensitive to flux variations and that at a high coverage regime the shift in the scaling behavior vanishes. Lastly, we posit that the shift in the scaled ISD indicates the departure of the island density's temperature dependence from predictions of classical nucleation theory.