Logistic regression for overall population. by Krystal Hunter (6820052)

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BMISES_Data_Part1. by Krystal Hunter (6820052)

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Baseline characteristics of HSES/LSES population. by Krystal Hunter (6820052)

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Baseline characteristics of overall population. by Krystal Hunter (6820052)

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Diagram of study population. by Krystal Hunter (6820052)

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Baseline characteristics. by A. H. Ayesha Lavell (16734002)

“…No differences were found regarding patient care roles, prior SARS-CoV-2 infection, years of work experience, or hospital workdays per week. Mental health significantly declined during the COVID-19 pandemic, after which mental health symptoms returned to baseline as the burden of COVID-19 patients decreased and public measures were lifted. …”

De-identified participant survey data. by A. H. Ayesha Lavell (16734002)

“…No differences were found regarding patient care roles, prior SARS-CoV-2 infection, years of work experience, or hospital workdays per week. Mental health significantly declined during the COVID-19 pandemic, after which mental health symptoms returned to baseline as the burden of COVID-19 patients decreased and public measures were lifted. …”

Example of sample data. by Xiying Wang (4859998)

“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”

Structure of BPNN. by Xiying Wang (4859998)

“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”

The workflow of EGA-BPNN. by Xiying Wang (4859998)

“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”

S1 Data - by Xiying Wang (4859998)

“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”

Algorithm flow of the GA-BPNN model. by Xiying Wang (4859998)

“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”

a, Anti-correlation of gene expression between <i>dmdR1</i> and its predicted regulon. Left: Pearson correlation coefficients (PCCs) between <i>dmdR1</i> and all genes with a DmdR1 position weight matrix (PWM) score greater than 15 in their regulatory region. The vertical dashed line marks the refined PWM score threshold of 22.875. The horizontal dotted lines mark PCC = ±0.43, corresponding to an adjusted <i>p</i>-value of 0.05. Right: Target genes immediately downstream of a predicted DmdR1 binding site, ordered by decreasing PWM score. Plus and minus indicate the strand of the target gene.... by Hannah E. Augustijn (10773096)

“…Right: Target genes immediately downstream of a predicted DmdR1 binding site, ordered by decreasing PWM score. Plus and minus indicate the strand of the target gene. …”

Schematic presentation <i>M</i>. <i>bovis</i> PG45 5′ nucleotidase and phylogenetic tree of its homologs. by Aga E. Gelgie (17661720)

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Composition of different formulations with their responses in I optimal design for optimization of Axitinib loaded Spanlastics. by Randa Mohammed Zaki (21637175)

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The composition and validation of the optimized formula with its predicted responses according to I Optimal Design. by Randa Mohammed Zaki (21637175)

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<i>M</i>. <i>bovis</i> 0690::Tn mutant failed to elicit the recruitment of neutrophils into milk spaces. by Aga E. Gelgie (17661720)

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Data supporting the findings of this study. by Aga E. Gelgie (17661720)

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I Optimal design for optimization of Axitinib loaded Spanlastics. by Randa Mohammed Zaki (21637175)

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ANOVA for I optimal design of Axitinib loaded Spanlastics. by Randa Mohammed Zaki (21637175)

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