(A–C) Effect of malonate, (D–F) effect of KCN and (A, B, D, E) proton leak kinetics by Nadeene Parker (53449)

“…Succinate (4 mM) was added at =− 1.5 min (see A) in the absence (open symbols) or presence (closed symbols) of 50 μM HNE. …”

S3 Fig - by Yaqi Wang (1555240)

Characterization of vesicles and neurites in different developmental periods. by Guangming Gan (622715)

Cross sectional study of mode of delivery and maternal and perinatal outcomes in mainland China by Lei Hou (274359)

“…Intrapartum indicated CD was associated with an increased risk of PPH (aOR = 1.68, CI = 1.50–1.89) compared to SVD. Compared to SVD, antepartum non-indicated CD was associated with lower likelihood of neonatal death (aOR = 0.14, CI = 0.06–0.34), neonatal ICU admission (aOR = 0.50, CI = 0.36–0.69), 5-minute Apgar<4 (aOR = 0.06, CI = 0.10–0.36), and respiratory distress syndrome (RDS) (aOR = 0.31, CI = 0.16–0.58), but not significantly associated with changes in rates of infection, hypoxic ischemic encephalopathy (HIEE), birth trauma or meconium aspiration rates.…”

Schematic representation of the crosstalk between nerve fibers and osteogenic cells in the periosteum. by Cédric Mauprivez (819048)

Recurrence risk assessment of LARC patients undergoing nCRT by ctDNA monitoring. by Yaqi Wang (1555240)

Efficacy of Anti-Inflammatory Therapy in a Model of Acute Seizures and in a Population of Pediatric Drug Resistant Epileptics by Nicola Marchi (4967)

“…The clinical study included pediatric drug resistant epileptic subjects receiving add on GC treatments. Decreased seizure frequency (≥50%) or interruption of <em>status epilepticus</em> was observed in the majority of the subjects, regardless of the underlying pathology. …”

Plasmid constructs used in this study. by Zahra Hosseininia (22265113)

“…The <i>PRAME-AS</i> knockout cells showed a decrease in migration, proliferation, stemness, and viability. …”

Oligonucleotides used in this study. by Zahra Hosseininia (22265113)

“…The <i>PRAME-AS</i> knockout cells showed a decrease in migration, proliferation, stemness, and viability. …”

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. …”

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. …”