Fit indices for latent class growth models for anxiety, affective problems, and oppositional-defiant problems. by Markus A. Landolt (8057528)

Subjects:

Overview of the WeARTolerance program. by Ana Beato (20489933)

“…<div><p>The stigma surrounding mental health remains a significant barrier to help-seeking and well-being in youth populations. …”

BA attenuated the decrease in the integrity and increase in the permeability of the epithelial barrier injury induced by LPS in Caco2 cell monolayers. by Luqiong Liu (11537092)

Factors associated with dermatological, oral, and neurological problems. by Sreshtha Chowdhury (12407350)

Subjects:

Developmental trajectories of total behavioral problems in the child residents. by Ted C. T. Fong (8959985)

Subjects:

Gender differences in alcohol problems or use disorders among servers and bartenders. by Rupa Jose (700465)

Subjects:

Prevalence of dermatological, oral and neurological problems from face mask use. by Sreshtha Chowdhury (12407350)

Subjects:

Risk factors of mental health problems and service use (N = 4,064). by Peter G. van der Velden (8160264)

Parasite burden is significantly decreased in the small intestine of <i>taiep</i> infected rats. by Karen Elizabeth Nava-Castro (6940340)

Statistical significance of the proposed method. by Jamila Akhter (21144252)

Subjects:

Main feature classes used to train the OCS algorithm for task-scheduling problems. by Simon Taub (12262035)

Subjects:

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. …”

Bias reduction achieved for youth in Problem Resolution Only (PRO). by Douglas C. Smith (1301511)

Subjects:

Y-27632 collaborated with BA to attenuate the increase in the integrity and decrease in the permeability of epithelial barrier injury induced by LPS in Caco2 monolayers. by Luqiong Liu (11537092)

“…<p>(<b>A)</b> Y-27632 collaborated with BA to attenuate the effect of LPS on TEER in Caco2 cells on days 1–22. (<b>B)</b> Y-27632 collaborated with BA to attenuate the effect of LPS on TEER in Caco2 cells on day 22. …”

Y-27632 synergized with BA to alleviate the decrease in the expression of and abnormal localization of ZO-1 and Occludin in LPS-induced Caco2 cells. by Luqiong Liu (11537092)

Metrics used to evaluate the models by the type of problems addressed in the SLR sample. by Hilson Gomes Vilar de Andrade (21609670)

Health-related quality of life on survivors from stroke in the first 28 days with: no problems (Level 1); some problems (Level 2); Extreme Problems (Level 3). by Igor Samuel Dobe (21781641)

Problem statement. by Jinqu Chen (20258031)

Predicting drinking problems or use disorders in servers and bartenders (N = 1,001). by Rupa Jose (700465)

Subjects:

Conservation planning steps and associated problems. by Vanessa M. Adams (11876348)