<i>Kif15</i> deficiency leads to impaired development of dendritic spines in PFC and hippocampus of mice. by Xiaomei He (795333)

Supplementary data. by Malte Ostendarp (13217973)

“…In contrast, the addition of hydrogen at 26 °C caused a significant decrease in the photophysiology of both <i>Acropora</i> sp. and <i>P. verrucosa</i>. …”

Lipidomic analysis of epidermis using LC-IMS-CID-MS reveals major changes in CKO// epidermis. by Kevin J. Mills (9270966)

“…(I) Significantly changed ceramides (Cer) in CKO vs K5Cre epidermis (p0.05 ANOVA) heatmap with mean fold change shown for each ceramide.…”

Localization and conditional deletion of C/EBP in SKH1 mouse epidermis. by Kevin J. Mills (9270966)

<b>Data for s</b><b>easonal variations in coral lipids and their significance for energy maintenance in the </b><b>South China Sea</b> by Hongyan Mo (19721569)

“…<p dir="ltr">In recent years, the intensification of global warming and extreme climate have led to an increase in the frequency and severity of coral bleaching. Coral bleaching means a decrease in symbiotic zooxanthellae density (ZD). …”

Targeted analysis of cholesterol and cholesterol esters in epidermis reveals changes in specific cholesterol esters in CKO epidermis. by Kevin J. Mills (9270966)

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. …”

Results of RF algorithm screening factors. 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. …”

This is the raw data used for this study. by James M. Friedman (22522150)

“…Average age was 48. 37% were male. 89% of patients who received a preoperative subcoracoid injection reported a significant decrease in presenting symptoms. 6 months after PM release, median VAS pain scores decreased from 8 to 2. …”

Pre-operative versus post-operative symptoms. by James M. Friedman (22522150)

“…Average age was 48. 37% were male. 89% of patients who received a preoperative subcoracoid injection reported a significant decrease in presenting symptoms. 6 months after PM release, median VAS pain scores decreased from 8 to 2. …”

Patient Demographics. by James M. Friedman (22522150)

“…Average age was 48. 37% were male. 89% of patients who received a preoperative subcoracoid injection reported a significant decrease in presenting symptoms. 6 months after PM release, median VAS pain scores decreased from 8 to 2. …”

Diagnostic Criteria for Human Disharmony Loop. by James M. Friedman (22522150)

“…Average age was 48. 37% were male. 89% of patients who received a preoperative subcoracoid injection reported a significant decrease in presenting symptoms. 6 months after PM release, median VAS pain scores decreased from 8 to 2. …”

Gaussian model fitted of the number of dogs detections of SARS-CoV-2, depending on the cycle threshold (Ct) values of RT-qPCR tests for SARS-CoV-2. by Víctor M. Vidal-Martínez (18707335)

Subjects:

Cycle threshold values at which a specific number of dogs out of 11 dogs was able to detect SARS-CoV-2 in samples from Merida, Yucatán, México. by Víctor M. Vidal-Martínez (18707335)

Subjects:

Results of a dog’s detections after exposure to sweat samples from positive and negative people to SARS-CoV-2, together with the Ct values of the RT-qPCR test of each person partic... by Víctor M. Vidal-Martínez (18707335)

Subjects:

Sensitivity and specificity of 11 dogs trained to detect SARS -CoV-2 from the sweat of positive and negative people from Mérida, Yucatán compared with the reference test (RT-qPCR).... by Víctor M. Vidal-Martínez (18707335)

Subjects:

Primers sequences of the genes examined by real-time PCR. by Takafumi Kabuto (14797727)

Subjects: