A comparison of Paraquat contamination^a numbers and percentages in different concentrations (unit:ppm). by Vivat Keawdounglek (9710710)

Overexpression of miR-130a inhibits translation of mRNA containing the 3′UTR of FOG-2. by Gene H. Kim (371958)

“…<p>In (A), northern analysis using 20 µg total RNA from COS-7 or NIH 3T3 cell lines with a probe specific for miR-130a. …”

Table 2_Predicting ventilator-associated lower respiratory tract infection outcomes using sequencing-based early microbiological response: a proof-of-concept prospective study.docx by Ji Zhou (481629)

“…The RQR was calculated as the quantification of A. baumannii determined by QtNGS after treatment to pretreatment. …”

Image 1_Predicting ventilator-associated lower respiratory tract infection outcomes using sequencing-based early microbiological response: a proof-of-concept prospective study.tif by Ji Zhou (481629)

“…The RQR was calculated as the quantification of A. baumannii determined by QtNGS after treatment to pretreatment. …”

Table 3_Predicting ventilator-associated lower respiratory tract infection outcomes using sequencing-based early microbiological response: a proof-of-concept prospective study.docx by Ji Zhou (481629)

“…The RQR was calculated as the quantification of A. baumannii determined by QtNGS after treatment to pretreatment. …”

Table 1_Predicting ventilator-associated lower respiratory tract infection outcomes using sequencing-based early microbiological response: a proof-of-concept prospective study.doc by Ji Zhou (481629)

“…The RQR was calculated as the quantification of A. baumannii determined by QtNGS after treatment to pretreatment. …”

S1 Data - by Marc S. Penn (16624541)

“…Whether such declines signify decreased risk of mortality remains unknown.</p><p>Design</p><p>Cox proportional hazard models were generated using data from a retrospective cohort study of prospectively collected measures.…”

K-cores of a network. by Matteo Serafino (11599593)

Change in the kinetic and potential energy during the periods T1 and T3 as a function of slope at each speed. by Arthur H. Dewolf (4537918)

Identifying radiation-induced survivorship syndromes affecting bowel health in a cohort of gynecological cancer survivors by Gunnar Steineck (226792)

“…<div><p>Background</p><p>During radiotherapy unwanted radiation to normal tissue surrounding the tumor triggers survivorship diseases; we lack a nosology for radiation-induced survivorship diseases that decrease bowel health and we do not know which symptoms are related to which diseases.…”

Model of provider exposure to UV light. by Rachael Guenter (15147776)

Impact on different areas of health. by Margherita Molaro (17803989)

The mean parasite concentration in the excreted urine of <i>Trypanosoma cruzi</i>-infected <i>Rhodnius prolixus</i> maintained at (A) 26°C and (B) 28°C at different triatomine deve... by Henri Loshouarn (17896676)

Respiratory and hemodynamic outcome parameters. by Denis Witham (12517162)

“…PaCO2 reduced after 20 minutes with both techniques (IAPV: from 65 to 52 mmHg, p < 0.01, relative effect (CI) 0.15 (0.01–0.28); ERCC: from 61 to 51 mmHg, p= < 0.01, relative effect (CI) 0.22 (0.07–0.37)). A transient decrease in oxygenation was fully and rapidly reversible. …”

Baseline characteristics of the patients. by Denis Witham (12517162)

“…PaCO2 reduced after 20 minutes with both techniques (IAPV: from 65 to 52 mmHg, p < 0.01, relative effect (CI) 0.15 (0.01–0.28); ERCC: from 61 to 51 mmHg, p= < 0.01, relative effect (CI) 0.22 (0.07–0.37)). A transient decrease in oxygenation was fully and rapidly reversible. …”

Increased ventilation and decarboxylation by IAPV and ERCC. by Denis Witham (12517162)

Training set data expansion. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Structural plane recognition effect. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Structural plane classification. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Mixup data expansion. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”