<i>shk-1</i> mutants exhibit defects in mitochondrial homeostasis. by Longjun Pu (20685717)

Effect of the cannabis extracts on the accumulation of scavenger receptors. by Bruno Musetti (9527085)

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. …”

Sociodemographic characteristics of the sample. by Senay Yildirim-Kahriman (22693464)

“…A moderate negative correlation was observed between CAM and CAM-MYCS scores (r = −0.511; p < 0.001), indicating that increased awareness of real causes is associated with decreased belief in myths. Regression analysis revealed that CAM scores were significantly predicted by academic year, being in the normal Body Mass Index (BMI) spectrum, and by having attended an oncology course (R² = 0.142; p < 0.001), whereas CAM-MYCS scores were predicted by academic year and high BMI values (R² = 0.072; p < 0.001). …”

Means scores of CAM and CAM-MYCS. by Senay Yildirim-Kahriman (22693464)

“…A moderate negative correlation was observed between CAM and CAM-MYCS scores (r = −0.511; p < 0.001), indicating that increased awareness of real causes is associated with decreased belief in myths. Regression analysis revealed that CAM scores were significantly predicted by academic year, being in the normal Body Mass Index (BMI) spectrum, and by having attended an oncology course (R² = 0.142; p < 0.001), whereas CAM-MYCS scores were predicted by academic year and high BMI values (R² = 0.072; p < 0.001). …”

<i>V. cholerae</i> exposure substantially remodels gene expression. by Longjun Pu (20685717)

Proteome and Metabolome Profiling of Anticoagulant Disorders Induced by Familial Protein S Deficiency by Caiping Zhang (6745976)

“…The proteome and metabolome of PSD were obviously disturbed, and the biological pathway of coagulation and complement cascades was the most affected. During PSD, overall levels of anticoagulant protein decreased and negative regulation of thrombin production was reduced, causing the formation of fibrin clots and platelet aggregation. …”

Proteome and Metabolome Profiling of Anticoagulant Disorders Induced by Familial Protein S Deficiency by Caiping Zhang (6745976)

“…The proteome and metabolome of PSD were obviously disturbed, and the biological pathway of coagulation and complement cascades was the most affected. During PSD, overall levels of anticoagulant protein decreased and negative regulation of thrombin production was reduced, causing the formation of fibrin clots and platelet aggregation. …”

Proteome and Metabolome Profiling of Anticoagulant Disorders Induced by Familial Protein S Deficiency by Caiping Zhang (6745976)

“…The proteome and metabolome of PSD were obviously disturbed, and the biological pathway of coagulation and complement cascades was the most affected. During PSD, overall levels of anticoagulant protein decreased and negative regulation of thrombin production was reduced, causing the formation of fibrin clots and platelet aggregation. …”

Proteome and Metabolome Profiling of Anticoagulant Disorders Induced by Familial Protein S Deficiency by Caiping Zhang (6745976)

“…The proteome and metabolome of PSD were obviously disturbed, and the biological pathway of coagulation and complement cascades was the most affected. During PSD, overall levels of anticoagulant protein decreased and negative regulation of thrombin production was reduced, causing the formation of fibrin clots and platelet aggregation. …”

Proteome and Metabolome Profiling of Anticoagulant Disorders Induced by Familial Protein S Deficiency by Caiping Zhang (6745976)

“…The proteome and metabolome of PSD were obviously disturbed, and the biological pathway of coagulation and complement cascades was the most affected. During PSD, overall levels of anticoagulant protein decreased and negative regulation of thrombin production was reduced, causing the formation of fibrin clots and platelet aggregation. …”

Proteome and Metabolome Profiling of Anticoagulant Disorders Induced by Familial Protein S Deficiency by Caiping Zhang (6745976)

“…The proteome and metabolome of PSD were obviously disturbed, and the biological pathway of coagulation and complement cascades was the most affected. During PSD, overall levels of anticoagulant protein decreased and negative regulation of thrombin production was reduced, causing the formation of fibrin clots and platelet aggregation. …”

Proteome and Metabolome Profiling of Anticoagulant Disorders Induced by Familial Protein S Deficiency by Caiping Zhang (6745976)

“…The proteome and metabolome of PSD were obviously disturbed, and the biological pathway of coagulation and complement cascades was the most affected. During PSD, overall levels of anticoagulant protein decreased and negative regulation of thrombin production was reduced, causing the formation of fibrin clots and platelet aggregation. …”

Comparison of the results of our model and other methods. by Weijia Zhu (65481)

Some examples of selected Chinese characters. by Weijia Zhu (65481)

“…Our model shows clear enhancements in structural accuracy (SSIM improved to 0.91), pixel-level fidelity (RMSE reduced to 2.68), perceptual quality aligned with human vision (LPIPS reduced to 0.07), and stylistic realism (FID decreased to 13.87). …”

Orthogonal experiment scheme and results. by Xueyong Pan (20390363)

“…<div><p>An efficient battery pack-level thermal management system was crucial to ensuring the safe driving of electric vehicles. …”

Design variables and range of values. by Xueyong Pan (20390363)

“…<div><p>An efficient battery pack-level thermal management system was crucial to ensuring the safe driving of electric vehicles. …”

Comparison of precision of various proxy models. by Xueyong Pan (20390363)

“…<div><p>An efficient battery pack-level thermal management system was crucial to ensuring the safe driving of electric vehicles. …”

Comparison between actual and predicted values. by Xueyong Pan (20390363)

“…<div><p>An efficient battery pack-level thermal management system was crucial to ensuring the safe driving of electric vehicles. …”

Sample points and numerical simulation results. by Xueyong Pan (20390363)

“…<div><p>An efficient battery pack-level thermal management system was crucial to ensuring the safe driving of electric vehicles. …”