BP neural network structure diagram. by Hongqi Wang (2208238)

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Structure diagram of GBDT model. by Hongqi Wang (2208238)

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Model prediction error analysis index. by Hongqi Wang (2208238)

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Fitting curve parameter table. by Hongqi Wang (2208238)

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Model prediction error analysis. by Hongqi Wang (2208238)

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Predictors used in the modelling analysis. The ‘Snail count’ model refers to the Generalised Linear Mixed Model (GLMM) used to model snail counts, ‘Prevalence’ refers to the diseas... by Roland Proud (11764892)

Facilities offering modern family planning services by division in 2014 (A) and 2017 (B). by Rajon Banik (12066099)

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Modern family planning service readiness score by division in 2014 (A) and 2017 (B). by Rajon Banik (12066099)

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South African cassava mosaic virus (SACMV) genome structure and the effect of SACMV on cassava leaf symptoms. by Bulelani L. Sizani (21485746)

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Transcript levels of MAPK, NIK, NLR and SA-depended gene pathways and responsive genes. by Bulelani L. Sizani (21485746)

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Structure-based alignment and homology modelling of MeTIR domains. by Bulelani L. Sizani (21485746)

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Transcript levels of MAPK, NIK, NLR and SA-dependent gene pathways and responsive genes. by Bulelani L. Sizani (21485746)

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Differentially-expressed NLR genes in susceptible T200 and tolerant TME3 genotypes infected with SACMV. by Bulelani L. Sizani (21485746)

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RTA408 significantly reduces RGCs loss. by Hongmei Qian (538647)

Expression profile of WRKY-TFs and ROS accumulation in susceptible T200 and tolerant TME3 cassava genotypes. by Bulelani L. Sizani (21485746)

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Hypothetical model of the molecular mechanism underlying the ability of SACMV to suppress the host RNA silencing mechanism (PTGS) and systemic acquired resistance (SAR). by Bulelani L. Sizani (21485746)

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Total count of HiFi reads indicating gene variation in 24 cassava genotypes. by Bulelani L. Sizani (21485746)

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MiR-129-5p levels were decreased in mouse depression models. by Qiaozhen Qin (13159201)

“…</b> The KEGG pathway analysis identifies the number of target genes within each pathway. Those with a p-value < 0.05 are considered significant. <b>L.…”

Summary of miRNAs sequencing data quality. by Zhaowen Tan (12283502)

“…</p><p>Results</p><p>1) Hemodynamic tests revealed that the blood pressure of SHRs in the BFRT decreased significantly and that the blood pressure level of SHRs in the BFRT decreased more significantly than that of the simple resistance training groups did (P < 0.05). 2) Cardiac function tests revealed that the EF, FS, and MV E/A of SHRs in the BFRT significantly increased, whereas the HR, IVSd, IVSs, LVIDd, LIVDs, LVPWd, LVPWs and LV mass significantly decreased (P < 0.05). 3) Transcriptome sequencing revealed 9 differentially expressed miRNAs in the BFRT group compared with the SHR-SED group (2 miRNAs were significantly upregulated, and 7 miRNAs were significantly downregulated), with P < 0.05 and |log2FoldChange| ≥ 1 used as the criteria for differential significance. …”

The miRNA target genes. by Zhaowen Tan (12283502)

“…</p><p>Results</p><p>1) Hemodynamic tests revealed that the blood pressure of SHRs in the BFRT decreased significantly and that the blood pressure level of SHRs in the BFRT decreased more significantly than that of the simple resistance training groups did (P < 0.05). 2) Cardiac function tests revealed that the EF, FS, and MV E/A of SHRs in the BFRT significantly increased, whereas the HR, IVSd, IVSs, LVIDd, LIVDs, LVPWd, LVPWs and LV mass significantly decreased (P < 0.05). 3) Transcriptome sequencing revealed 9 differentially expressed miRNAs in the BFRT group compared with the SHR-SED group (2 miRNAs were significantly upregulated, and 7 miRNAs were significantly downregulated), with P < 0.05 and |log2FoldChange| ≥ 1 used as the criteria for differential significance. …”