Classification results and most selected features. by María Goñi (12963621)

2P excitation of DiO/DPA enables AP detection in a L5 pyramidal cell. A. by Ann E. Fink (312543)

“…Fluorescence traces were not inverted. <b>C</b>. Average of 50 trials showing fAPs and a DiO/DPA optical response to a 6.8 mV depolarization. …”

Multiple Knee Points in the Degradation of a Commercial Lithium-ion Battery: A Case Study of the NCM/Graphite System (Supporting Information) by Yui FUJIHARA (17772971)

“…Further studies are required to continuously track changes over time and elucidate the source of knee points during operation. Herein, we conduct degradation cycle tests using a large-format commercial lithium-ion battery (>50 Wh, LiNi_0.5Co_0.2Mn_0.3O₂/graphite) and analyze the knee points during cycling by employing electrochemical impedance spectroscopy (EIS) at different states of charge (SOCs) to track the degradation state over time, in combination with differential analysis and post-mortem methods. …”

Zpg does not function as a hemichannel. by Yanina-Yasmin Pesch (13884117)

“…<p>(A-D) <i>zpg</i> mutants rescued with genomic rescue constructs in which one or more cysteine residues were mutated, hindering the formation of gap junctions, have rudimentary testes and no Zpg is detected by antibody staining (green in A-D; single channels depicted in grey in A’-D’; wt in A-A’; <i>zpg</i> C6S, B-B’:, <i>zpg</i> C145S, C-C’; <i>zpg</i> C236S, D-D’). …”

Characteristics of Gasless Combustion of Core–Shell Al@NiO Microparticles with Boosted Exothermic Performance by Shina Maini (19413980)

“…The PM composite was not able to be ignited at all by a 5 W laser, while the core–shell counterpart ignited at 2.55 ms and was completely combusted within 6.50 ms accompanying a violent impulse.…”

Characteristics of Gasless Combustion of Core–Shell Al@NiO Microparticles with Boosted Exothermic Performance by Shina Maini (19413980)

“…The PM composite was not able to be ignited at all by a 5 W laser, while the core–shell counterpart ignited at 2.55 ms and was completely combusted within 6.50 ms accompanying a violent impulse.…”

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. …”

image2_Effects of Angiotensin II Type 1A Receptor on ACE2, Neprilysin and KIM-1 in Two Kidney One Clip (2K1C) Model of Renovascular Hypertension.tif by Laale F. Alawi (10049429)

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. …”

Cell motility depends on a QSF secreted by cells. by Laurent Golé (198154)

“…Cells were recorded during 50 min, 1.5 hours after cells were washed in a fresh HL5 medium. …”

Effects of GSI treatment on MAPK, PI3K/AKT and NF-κB pathway activation in LPS/IC-activated macrophages. by Wipawee Wongchana (5367968)

“…The arrows indicate cells with decreased or no p50 nuclear translocation (green). …”

Liquid cultures in RPMI-1640. by Nicholas C. Schena (16541975)

CgA silencing, processing enzyme inhibition and functional analysis of CgA peptides in SI-NEN cell lines. by Francesco Giovinazzo (487256)

“…Inhibition of the CgA processing enzyme prohormone convertase using Decanoyl-Arg-Val-Lys-Arg-CMK also decreased proliferation of H-STS cells (25 µM [<i>data not shown</i>] and 50 µM, <b>4D</b>, *<i>p</i><0.05). …”

CDK2/Speedy A localization in <i>Cdk2</i>^<i>T160A</i> spermatocytes. by Nathan Palmer (264897)

“…Data are presented as individual foci counts for WT (orange bars, N = 72) and <i>Cdk2</i>^<i>T160A</i> (blue bars, N = 50); error bars are indicative of the mean and SD. …”