Data_Sheet_1_Nitrate Feed Improves Growth and Ethanol Production of Clostridium ljungdahlii With CO2 and H2, but Results in Stochastic Inhibition Events.pdf by Christian-Marco Klask (8802542)

“…A decreasing pH level throughout time due to undissociated acetic acid accumulation is anticipated under uncontrolled pH conditions such as in bottle experiments. …”

Substitutent Effects in the Periphery of 2,9-Bisaryl-tetraazaperopyrene Dyes by Susanne C. Martens (1785169)

“…The UV−vis absorption spectra of all compounds recorded in trifluoroacetic acid are almost superimposable and display a characteristic visible absorption band between 460 and 490 nm (log ε = 4.64−5.01) with a strong vibrational progression of 1173−1475 cm⁻¹. …”

Data_Sheet_3_Nitrate Feed Improves Growth and Ethanol Production of Clostridium ljungdahlii With CO2 and H2, but Results in Stochastic Inhibition Events.xlsx by Christian-Marco Klask (8802542)

“…A decreasing pH level throughout time due to undissociated acetic acid accumulation is anticipated under uncontrolled pH conditions such as in bottle experiments. …”

Substitutent Effects in the Periphery of 2,9-Bisaryl-tetraazaperopyrene Dyes by Susanne C. Martens (1785169)

“…The UV−vis absorption spectra of all compounds recorded in trifluoroacetic acid are almost superimposable and display a characteristic visible absorption band between 460 and 490 nm (log ε = 4.64−5.01) with a strong vibrational progression of 1173−1475 cm⁻¹. …”

Time-Dependent Decay of mRNA and Ribosomal RNA during Platelet Aging and Its Correlation with Translation Activity by Catherine Angénieux (145933)

“…The “RNA Content” normalized to the number of platelets could be 20 to 40 fold higher when 80–90% of the cells were reticulated (20–40 fg/platelet), than when only 5–10% of control cells were TO^bright (less than 1fg/platelet). …”

Data_Sheet_1_Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data.docx by Natalia Jaworska (823848)

“…For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features.</p><p>Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha₁/theta eLORETA features showed the highest predictive value. …”

Data_Sheet_2_Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data.docx by Natalia Jaworska (823848)

“…For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features.</p><p>Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha₁/theta eLORETA features showed the highest predictive value. …”

Ran knock down by RNAi results in abnormal neurite morphology in mouse neurons. by Katharine J. Sepp (50335)

“…Control Bins: II = 0.7+/−0.3; III = 22.9+/−1.5; IV = 59.6+/−0.2; V = 13.1+/−1.2; VI = 3.6+/−0.5. …”

Decay rate comparison for Pavlovian experiment. by Weston Fleming (10924744)

“…Neurons that exhibited a significant decrease in firing following reward omission (24/65 neurons, 36.9% of population for low gamma; 22/65 neurons, 33.6% of population for median gamma; 25/65 neurons, 38.5% of population for high gamma) are darker green. …”

The heatmap of RNA sequencing by Xinbin Zheng (13028184)

Fbxw8 is essential for morphogenesis of the Golgi apparatus in neurons. by Nadia Litterman (221903)

“…Fbxw8 knockdown significantly decreased the percentage of granule neurons with trafficked VSVG-ts at the 60-, 90-, and 120-min time points. …”

Table_1_Perinatal Ureaplasma Exposure Is Associated With Increased Risk of Late Onset Sepsis and Imbalanced Inflammation in Preterm Infants and May Add to Lung Injury.xlsx by Kirsten Glaser (846928)

“…Presence of Ureaplasma was associated with a 7-fold risk of late onset sepsis (LOS) (95% CI 1.80–27.39; p = 0.014). …”

Table_8_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

“…Introduction<p>Semen quality has decreased gradually in recent years, and lifestyle changes are among the primary causes for this issue. …”

Table_1_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

“…Introduction<p>Semen quality has decreased gradually in recent years, and lifestyle changes are among the primary causes for this issue. …”

Table_9_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

“…Introduction<p>Semen quality has decreased gradually in recent years, and lifestyle changes are among the primary causes for this issue. …”

Table_7_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

“…Introduction<p>Semen quality has decreased gradually in recent years, and lifestyle changes are among the primary causes for this issue. …”

Table_4_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

“…Introduction<p>Semen quality has decreased gradually in recent years, and lifestyle changes are among the primary causes for this issue. …”

Table_2_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

“…Introduction<p>Semen quality has decreased gradually in recent years, and lifestyle changes are among the primary causes for this issue. …”

Table_6_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

“…Introduction<p>Semen quality has decreased gradually in recent years, and lifestyle changes are among the primary causes for this issue. …”

Table_10_Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.docx by Mingjuan Zhou (12880019)

“…Introduction<p>Semen quality has decreased gradually in recent years, and lifestyle changes are among the primary causes for this issue. …”