Substrate Access Mechanism in a Novel Membrane-Bound Phospholipase A of Pseudomonas aeruginosa Concordant with Specificity and Regioselectivity by Sabahuddin Ahmad (733766)

“…PlaF is a cytoplasmic membrane-bound phospholipase A₁ from Pseudomonas aeruginosa that alters the membrane glycerophospholipid (GPL) composition and fosters the virulence of this human pathogen. …”

Substrate Access Mechanism in a Novel Membrane-Bound Phospholipase A of Pseudomonas aeruginosa Concordant with Specificity and Regioselectivity by Sabahuddin Ahmad (733766)

“…PlaF is a cytoplasmic membrane-bound phospholipase A₁ from Pseudomonas aeruginosa that alters the membrane glycerophospholipid (GPL) composition and fosters the virulence of this human pathogen. …”

Table1_A Comprehensive Analysis of HAVCR1 as a Prognostic and Diagnostic Marker for Pan-Cancer.DOCX by Sheng Liu (279488)

“…Overall, we provided compelling evidence that HAVCR1 could be a prognostic and diagnostic marker for Liver hepatocellular carcinoma and Pancreatic adenocarcinoma.…”

Substrate Access Mechanism in a Novel Membrane-Bound Phospholipase A of Pseudomonas aeruginosa Concordant with Specificity and Regioselectivity by Sabahuddin Ahmad (733766)

“…PlaF is a cytoplasmic membrane-bound phospholipase A₁ from Pseudomonas aeruginosa that alters the membrane glycerophospholipid (GPL) composition and fosters the virulence of this human pathogen. …”

IG feature selection process. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

RFE feature selection process. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

CICID2017 dataset information. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

Shows the basic architecture of an autoencoder. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

Architecture of deep neural networks. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

Proposed model framework. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

WUSTL-EHMS-2020 dataset information. by Ahmed Muqdad Alnasrallah (21647492)

“…The proposed model employs Information Gain (IG) and Recursive Feature Elimination (RFE) in parallel to select the top 50% of features, from which intersection and union subsets are created, followed by a deep autoencoder (DAE) to reduce dimensionality without losing important data. …”

Simulation of a Predator–PRESS Experiment. by Takehito Yoshida (273131)

“…<p>We used the Abrams-Matsuda [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0050235#pbio-0050235-b038" target="_blank">38</a>] model for this simulation. …”

Maintenance of weight loss or stability in subjects with obesity: a retrospective longitudinal analysis of a real-world population by Maral DerSarkissian (504144)

“…</p> <p><b>Methods:</b> A retrospective observational longitudinal study of subjects with obesity was conducted using the General Electric Centricity electronic medical record database. …”

Summary of subgroup analysis results. by Yongki Welliam (21030005)

“…Meta-analysis revealed that increasing OPN (SMD = 5.52, 95% CI = 1.59–9.44, p = 0.01) and KIM-1 (SMD = 1.45, 95% CI = 0.50–2.39, p = 0.0027), as well as decreasing Fetuin-A level (SMD = -1.31, 95% CI = -2.37 – -0.26, p = 0.01) were significant in CKD patients with ESRD. …”

PRISMA Flow Chart 2020. by Yongki Welliam (21030005)

“…Meta-analysis revealed that increasing OPN (SMD = 5.52, 95% CI = 1.59–9.44, p = 0.01) and KIM-1 (SMD = 1.45, 95% CI = 0.50–2.39, p = 0.0027), as well as decreasing Fetuin-A level (SMD = -1.31, 95% CI = -2.37 – -0.26, p = 0.01) were significant in CKD patients with ESRD. …”

Included and excluded studies. by Yongki Welliam (21030005)

“…Meta-analysis revealed that increasing OPN (SMD = 5.52, 95% CI = 1.59–9.44, p = 0.01) and KIM-1 (SMD = 1.45, 95% CI = 0.50–2.39, p = 0.0027), as well as decreasing Fetuin-A level (SMD = -1.31, 95% CI = -2.37 – -0.26, p = 0.01) were significant in CKD patients with ESRD. …”

Characteristics of included studies. by Yongki Welliam (21030005)

“…Meta-analysis revealed that increasing OPN (SMD = 5.52, 95% CI = 1.59–9.44, p = 0.01) and KIM-1 (SMD = 1.45, 95% CI = 0.50–2.39, p = 0.0027), as well as decreasing Fetuin-A level (SMD = -1.31, 95% CI = -2.37 – -0.26, p = 0.01) were significant in CKD patients with ESRD. …”

Extraction data table. by Yongki Welliam (21030005)

“…Meta-analysis revealed that increasing OPN (SMD = 5.52, 95% CI = 1.59–9.44, p = 0.01) and KIM-1 (SMD = 1.45, 95% CI = 0.50–2.39, p = 0.0027), as well as decreasing Fetuin-A level (SMD = -1.31, 95% CI = -2.37 – -0.26, p = 0.01) were significant in CKD patients with ESRD. …”

Constructing a proteomic growth model. by Kerry A. Geiler-Samerotte (462866)

“…‘HSP82’ is induced in strains with decreased growth rate. Colors correspond to strain pairs in (<b>A</b>). …”

Tensile Properties of Ultrathin Bisphenol‑A Polycarbonate Films by Woo Jin Choi (1692100)

“…We propose a mechanism to explain these observed changes in the mechanical properties for ultrathin BPA PC films.…”