Analysis of PtWRKY genes. by Yuzhao Ma (12840632)

“…Based on RNA-Seq, we identified 83 <i>WRKYs</i> significantly respond to cadmium (Cd) stress. Subsequently, we conducted a study on <i>WRKY95</i>, which was significantly up-regulated in the roots, stems, and leaves under Cd stress. …”

The primer sequences for RT-qPCR. by Yuzhao Ma (12840632)

“…Based on RNA-Seq, we identified 83 <i>WRKYs</i> significantly respond to cadmium (Cd) stress. Subsequently, we conducted a study on <i>WRKY95</i>, which was significantly up-regulated in the roots, stems, and leaves under Cd stress. …”

<i>PsnWRKY95</i> transgenic plants selection and functional analysis of <i>PsnWRKY95</i> in cadmium resistance. by Yuzhao Ma (12840632)

Analysis of <i>PtWRKYs</i> expression in response to Cd. by Yuzhao Ma (12840632)

Impact of endophytic fungi on hemp leaf water content, under non-stress, salinity and drought stress conditions. by Shasha Hu (5880887)

Impact of endophytic fungi on the concentration of malondialdehyde (MDA) in hemp leaves under non-stress, salinity and drought stress conditions. by Shasha Hu (5880887)

Impact of endophytic fungi on hemp leaf pigments, under non-stress, salinity and drought stress conditions. by Shasha Hu (5880887)

Impact of endophytic fungi on weight of hemp leaf, stem and root, under non-stress, salinity and drought stress conditions. by Shasha Hu (5880887)

Impact of endophytic fungi on soil humidity, under non-stress, salinity and drought stress conditions. by Shasha Hu (5880887)

Impact of endophytic fungi on the concentration of protoporphyrin (PPIX) and magnesium protoporphyrin (MGPP) in hemp leaves under non-stress, salinity and drought stress conditions... by Shasha Hu (5880887)

Impact of endophytic fungi on the expression patterns <i>CsNAC3</i> in hemp under non-stress, salinity and drought stress conditions. by Shasha Hu (5880887)

Implementation framework. by Amos Asiedu (4996946)

“…HMIS data showed statistically significant improvement in data completeness (from 62 to 87% (p < 0.001)) and decreased error rate (from 37 to 18% (p < 0.001)) after completion of the coaching visit series. …”

DHIMS2 data quality over time. by Amos Asiedu (4996946)

“…HMIS data showed statistically significant improvement in data completeness (from 62 to 87% (p < 0.001)) and decreased error rate (from 37 to 18% (p < 0.001)) after completion of the coaching visit series. …”

Mean ± SD values of unipedal stance parameters evaluated at the different time points of the postural training session. by Cristina Dolciotti (16317330)

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Mean ± SD of the different parameters relative to bipedal stance on soft support with the eyes open recorded in the Chewing and Hand Grip groups. by Cristina Dolciotti (16317330)

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Time course of Path Length, LFS and Velocity during postural training. by Cristina Dolciotti (16317330)

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Relation of the changes in Y Sway Amplitude and LFS induced by postural training with those in 95% Area. by Cristina Dolciotti (16317330)

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Training-induced changes in bipedal stance. by Cristina Dolciotti (16317330)

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Between-group differences in 95% Area, Y Sway Amplitude and LFS during postural training. by Cristina Dolciotti (16317330)

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Relation between training-related changes in unipedal and bipedal stance. by Cristina Dolciotti (16317330)

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