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. …”

Histogram representative of the flow cytometric analysis of the labeling of T-Bet, GATA3 and FoxP3 transcription factors. by Sidnei Ferro Costa (8115260)

Supplementary materials. 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. …”

Cashtags become substantially more popular after Musk purchased X. by Daniel Hickey (20714822)

Deletion of <i>Ulk1</i> inhibits neointima formation by enhancing KAT2A/GCN5-mediated acetylation of TUBA/α-tubulin <i>in vivo</i> by Changhan Ouyang (3637546)

“…</p> <p><b>Abbreviations:</b> ACTA2/α-SMA: actin, alpha 2, smooth muscle, aorta; ACTB: actin beta; ATAT1: alpha tubulin acetyltransferase 1; ATG: autophagy related; BECN1: beclin 1; BP: blood pressure; CAL: carotid artery ligation; CQ: chloroquine diphosphate; EC: endothelial cells; EEL: external elastic layer; FBS: fetal bovine serum; GAPDH: glyceraldehyde 3-phosphate dehydrogenase; HASMCs: human aortic smooth muscle cells; HAT1: histone acetyltransferase 1; HDAC: histone deacetylase; IEL: inner elastic layer; IP: immunoprecipitation; KAT2A/GCN5: K(lysine) acetyltransferase 2A; KAT8/hMOF: lysine acetyltransferase 8; MAP1LC3: microtubule associated protein 1 light chain 3; MYH11: myosin heavy chain 11; PBS: phosphate-buffered saline; PDGF: platelet derived growth factor; PECAM1/CD31: platelet and endothelial cell adhesion molecule 1; RAC3: Rac family small GTPase 3; SIRT2: sirtuin 2; SPP1/OPN: secreted phosphoprotein 1; SQSTM1/p62: sequestosome 1; TAGLN/SM22: transgelin; TUBA: tubulin alpha; ULK1: unc-51 like autophagy activating kinase; VSMC: vascular smooth muscle cell; VVG: Verhoeff Van Gieson; WT: wild type.…”

Minimal dataset. by Herindita Puspitaningtyas (11618232)

“…During 2008–2019, trends of colon cancer in age <50 increased by 8.15% annually while rectal cancer displayed a 9.71% increase annually prior to 2017, followed by a 17.23% decrease until 2019.…”

Canonical correspondence analysis of mosquito assemblages and landscape structure classifications at each sampling unit. by Leonardo Suveges Moreira Chaves (5554235)

Principal components dimensions showed in PCA. by Leonardo Suveges Moreira Chaves (5554235)

TG-DTG curves at different heating rates. by Meihui Wu (8586993)

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. …”