The p20-ATG8 interaction is important for the VSR activity of p20. by Yongle Zhang (11355546)

Autophagy facilitates CTV infection in <i>Nicotiana benthamiana.</i> by Yongle Zhang (11355546)

CTV p20 interacts with NbSGS3 and mediates autophagic degradation of NbSGS3. by Yongle Zhang (11355546)

CTV p20 interacts with autophagy-related proteins NbATG8a and NbATG8f and induces autophagy. by Yongle Zhang (11355546)

Influencing factor index system. by Yunxia Zhang (30848)

“…The results show that: (1) the level of non-grain of cultivated land in Lianyungang City increased gradually from 6.01% to 11.10% from 2002 to 2022, and grain cultivation was mainly shifted to greenhouse vegetables, construction and development and abandonment. (2) the level of non-grain of cultivated land showed a spatial pattern of high along the north-west-south-east and decreasing to the two sides, and the pattern showed a trend of gradual weakening, with Moran’s I decreased from 0.90 to 0.42. (3) The dominant factors of the spatial differentiation of non-grain of cultivated land in different periods are different, among which GDP, population density, NDVI, and precipitation are always the main influencing factors. …”

A proposed model for citrus tristeza virus (CTV) p20-mediated RNA silencing suppression. by Yongle Zhang (11355546)

CiLV-C p29 acts as an RNA-binding protein and interacts with eukaryotic initiation factor 4A (eIF4A). by Mikhail Oliveira Leastro (5736329)

Single factor detection results. by Tingting Cui (564531)

“…The factors within the Park attributes (G1) and the park’s social media level (G4) showed a two-way interaction strength increase. (4)The coefficients of influence of impact factors on the space heterogeneity of vacation park vitality exhibit significant variation. …”

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Long-term trajectories of ABG parameters. by Kazuma Nagata (3400058)

(a) The impact of changing the epoch at which the proximity factor for infection clusters is activated on the validation infection coverage and <i>F</i>₁ measure. by Ibrahim Almakky (22530341)

Graphs showing the qPCR data from SD, ED, HFD and SDG larvae at 7 dpf (n = 6 for each). by Jessica C. M. Hui (20370522)

FAT1 and MIB2 control tumor growth <i>in vivo.</i> by Rui Li (4631)

Basic features of microtopography. by Qingping Gou (22641045)

“…The results showed that: the difference of shallow soil moisture in different microtopographies on a slope surface was not significant, the difference of deep soil moisture was significant, and the soil moisture overuse was the largest in the gully (GU), amounting to 386.36 mm, and the smallest in the ephemeral gully (EG) (131.02 mm); the GU, the sink hole (SH), and the scarp (SC) showed a trend of decreasing and then increasing with the increase of soil depth, and the platform (PL) has little overall trend of change in soil moisture with the increase of soil depth, and the soil moisture of US and EG shows the trend of “decreasing-then increasing-then decreasing” with the increase of soil depth. …”

Soil bulk density of different microtopographies. by Qingping Gou (22641045)

“…The results showed that: the difference of shallow soil moisture in different microtopographies on a slope surface was not significant, the difference of deep soil moisture was significant, and the soil moisture overuse was the largest in the gully (GU), amounting to 386.36 mm, and the smallest in the ephemeral gully (EG) (131.02 mm); the GU, the sink hole (SH), and the scarp (SC) showed a trend of decreasing and then increasing with the increase of soil depth, and the platform (PL) has little overall trend of change in soil moisture with the increase of soil depth, and the soil moisture of US and EG shows the trend of “decreasing-then increasing-then decreasing” with the increase of soil depth. …”

Chromatin accessibility analysis of perinatal Vps34-deficient Tregs. by Erienne G. Norton (9612079)

“…AP-1-related transcription factors with predicted decreased activity are labeled. …”

Vps34 orchestrates transcriptional and epigenetic remodeling events corresponding to terminal eTreg differentiation during perinatal life. by Erienne G. Norton (9612079)

“…AP-1-related transcription factors with predicted decreased activity are labeled. …”