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)

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.…”

A flow diagram outlining the number of dogs in each group during the study (visit 1 = day 0, visit 2 = day 35, and visit 3 = day 70). by Nattapon Riengvirodkij (11268720)

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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. …”

NgR1 KO mice exhibited an increase in excitatory synapses and a decrease in inhibitory synapses, indicating an imbalance of synaptic transmission. by Jinwei Zhang (462455)

“…The inhibitory synaptic density of NgR1 mice showed a significant decrease when compared to WT mice (***P <  0.001). …”

Analytical framework and statistical methods. by Na Chen (153323)

“…Using cross-country data, we analyze the prevalence of income reduction and the effectiveness of social assistance programs in high-income, upper middle-income, lower middle-income, and low-income countries. Our findings reveal significant variations in income insecurity and social protection responses across these groups. the pandemic had a significant impact on household incomes globally, with lower-middle-income countries experiencing the most significant income reductions. …”