Structure–Activity Relationships of Azaquinazolinone Derivatives as ¹⁸F‑Labeled PET Probes Targeting Ghrelin Receptors by Hiroyuki Watanabe (312793)

Subjects:

Expression of Caspase-3 in HCT 116 and Hep 3B: <i>2A. and 2B.</i> by Vidya G. Bettada (22208808)

Thioflavin-T (ThT) fluorescence analysis of amyloid fibril formation in wild-type and p.R163C mutant αB-crystallin under thermochemical stress. by Aref Baharvand (21713886)

Synthesis and Biological Evaluation of MEK/mTOR Multifunctional Inhibitors as Novel Anticancer Agents by Marcian E. Van Dort (21374093)

“…Additionally, compound LP-65 demonstrated significant modulation of MEK and mTOR signaling activity in human glioma cells (D54) and human melanoma cells (A375), with a corresponding decrease in cellular proliferation and migration. …”

Study Data. by Jonathan S. Jahr (2709088)

“…It has also been hypothesized that the bradycardia and rare instances of cardiac arrest occurring after the use of sugammadex may be due to a transient decrease in circulating corticosteroids, causing a temporary ‘mini Addisonian crisis.’ …”

Study outcomes. by Jonathan S. Jahr (2709088)

“…It has also been hypothesized that the bradycardia and rare instances of cardiac arrest occurring after the use of sugammadex may be due to a transient decrease in circulating corticosteroids, causing a temporary ‘mini Addisonian crisis.’ …”

Number of visits per clinic type, 2013-2015. by Jacob Dreiher (14856863)

“…During PE, statistically significant longer waiting times were found for surgery (+1.0 day) and imaging (+1.1 days), while a 2.4 days decrease was noted in pediatrics, controlled for age, sex, ethnicity and the daily number of visits. …”

Patient characteristics. by Jonathan S. Jahr (2709088)

“…It has also been hypothesized that the bradycardia and rare instances of cardiac arrest occurring after the use of sugammadex may be due to a transient decrease in circulating corticosteroids, causing a temporary ‘mini Addisonian crisis.’ …”

Treatment with vitamin D3 reduced the viability of cancer cell lines: <i>1A & 1B.</i> by Vidya G. Bettada (22208808)

“…Mouse EAC cells showed a decrease in cell viability starting from 250 µM at 24h of treatment. …”

Vitamin D3, but not the Cisplatin, could moderately reduce STZ-induced hyperglycemia in mice (a) Schematic representation of experimental protocol followed in the study: After accl... by Vidya G. Bettada (22208808)

“…(c) Vitamin D3 and the positive control Cisplatin differently modulated FBG in hyperglycaemic mice: Intraperitoneal administration of vitamin D3 very minimally decreased FBG compared to vehicle control at the end of the study. …”

Comparative expression between platelets and leukocytes in levels of transcripts related to thrombosis, coagulation and platelet-leukocyte interactions. by Heather Learnard (22075952)

PRR expression in platelets and leukocytes and their changes with SARS-CoV-2 infection. by Heather Learnard (22075952)

Testing set error. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Internal structure of an LSTM cell. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Prediction effect of each model after STL. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

The kernel density plot for data of each feature. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Analysis of raw data prediction results. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Flowchart of the STL. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

SARIMA predicts season components. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

BWO-BiLSTM model prediction results. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”