Scatter plots of the linear regression for the relationship between the duration of the vegetative phase and the average mass of single inflorescences. by Torsten Schober (20485754)

Fig 5 - by Torsten Schober (20485754)

Fig 4 - by Torsten Schober (20485754)

Fig 6 - by Torsten Schober (20485754)

<i>SLC2A2</i> is associated with IGF1R pathways differentiation. by Yejin Kim (740789)

The involvement of GCN2 in eIF2 phosphorylation and <i>HaCAD, HaABCC2 and HaABCC3</i> expression. by Caihong Zhang (1633294)

Primer sequences for RT-qPCR. by Wenlong Shen (9313937)

“…Data from TCGA showed that NCOA4 shows greater downgrade in tumor tissues than in non-tumor tissues and the overall survival (OS) of patients with low NCOA4 expression was significantly shorter than that of patients with high NCOA4 expression.The qPCR results showed that NCOA4 was expressed at low levels in cholangiocarcinoma tissue specimens; the mRNA expression of NCOA4 decreased after knocking down NCOA4 in cells. …”

siRNA sequences and negative controls sequences. by Wenlong Shen (9313937)

“…Data from TCGA showed that NCOA4 shows greater downgrade in tumor tissues than in non-tumor tissues and the overall survival (OS) of patients with low NCOA4 expression was significantly shorter than that of patients with high NCOA4 expression.The qPCR results showed that NCOA4 was expressed at low levels in cholangiocarcinoma tissue specimens; the mRNA expression of NCOA4 decreased after knocking down NCOA4 in cells. …”

Antibodies used in the study. by Wenlong Shen (9313937)

“…Data from TCGA showed that NCOA4 shows greater downgrade in tumor tissues than in non-tumor tissues and the overall survival (OS) of patients with low NCOA4 expression was significantly shorter than that of patients with high NCOA4 expression.The qPCR results showed that NCOA4 was expressed at low levels in cholangiocarcinoma tissue specimens; the mRNA expression of NCOA4 decreased after knocking down NCOA4 in cells. …”

Histogram of the area factorψ. by Hong Zhang (25820)

“…The measured <i>I</i>_s decreases following a power-law trend as <i>β</i> and <i>D</i> increase, with weathering reducing the sensitivity of <i>I</i>_s to <i>β</i> but not significantly altering its sensitivity to <i>D</i>. …”

Raw dataset Fig. 6. by Hong Zhang (25820)

“…The measured <i>I</i>_s decreases following a power-law trend as <i>β</i> and <i>D</i> increase, with weathering reducing the sensitivity of <i>I</i>_s to <i>β</i> but not significantly altering its sensitivity to <i>D</i>. …”

Schematic for measuring <i>D</i> and <i>D</i>′ values. by Hong Zhang (25820)

“…The measured <i>I</i>_s decreases following a power-law trend as <i>β</i> and <i>D</i> increase, with weathering reducing the sensitivity of <i>I</i>_s to <i>β</i> but not significantly altering its sensitivity to <i>D</i>. …”

Design of the D-trial. by Torsten Schober (20485754)

“…In the context of standardized production, we therefore advocate high-density production systems that increase the proportion of desired inflorescence fractions from upper canopy layers.…”

Raw data V-trial. by Torsten Schober (20485754)

“…In the context of standardized production, we therefore advocate high-density production systems that increase the proportion of desired inflorescence fractions from upper canopy layers.…”

Raw data D-trial. by Torsten Schober (20485754)

“…In the context of standardized production, we therefore advocate high-density production systems that increase the proportion of desired inflorescence fractions from upper canopy layers.…”

Sound evoked cortical activity across vigilance states before and after NOE. by Linus Milinski (10532121)

“…Panels left to right: Ferret 1, Ferret 2, Ferret 3). Due to decreased signal quality in the six months post NOE assessment in Ferrets 2 and 3, AERs could not be quantitatively assessed for these timepoints. …”

Fig 9 - by Torsten Schober (20485754)

Fig 8 - by Torsten Schober (20485754)

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML by Ayush Garg (21090944)

“…The important findings of our studies are as follows: (i) there is no effect of threshold optimization on ranking metrics such as AUC and AUPR, but AUC and AUPR get affected by class-weighting and SMOTTomek; (ii) for ML methods RF and SVM, significant percentage improvement up to 375, 33.33, and 450 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy, which are suitable for performance evaluation of imbalanced data sets; (iii) for AutoML libraries AutoGluon-Tabular and H2O AutoML, significant percentage improvement up to 383.33, 37.25, and 533.33 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy; (iv) the general pattern of percentage improvement in balanced accuracy is that the percentage improvement increases when the class ratio is systematically decreased from 0.5 to 0.1; in the case of F1 score and MCC, maximum improvement is achieved at the class ratio of 0.3; (v) for both ML and AutoML with balancing, it is observed that any individual class-balancing technique does not outperform all other methods on a significantly higher number of data sets based on F1 score; (vi) the three external balancing techniques combined outperformed the internal balancing methods of the ML and AutoML; (vii) AutoML tools perform as good as the ML models and in some cases perform even better for handling imbalanced classification when applied with imbalance handling techniques. …”

Effect of aging on cellular senescence in the mouse kidney. by Lihua Qiu (178071)