Risk of bias summary. by Duc Tran Quang (20564967)

“…The observed decrease in body weight could be partially attributed to factors influencing energy balance, as evidenced by the significantly lower mean calorie intake at the end of the intervention (1694.71 kcal/day, 95% CI: 1498.57–1890.85) compared to the baseline intake (2000.64 kcal/day, 95% CI: 1830–2172.98), despite the absence of intentional efforts to restrict energy intake by the participants. …”

Criteria for study selection. by Duc Tran Quang (20564967)

“…The observed decrease in body weight could be partially attributed to factors influencing energy balance, as evidenced by the significantly lower mean calorie intake at the end of the intervention (1694.71 kcal/day, 95% CI: 1498.57–1890.85) compared to the baseline intake (2000.64 kcal/day, 95% CI: 1830–2172.98), despite the absence of intentional efforts to restrict energy intake by the participants. …”

Detailed information of the observation datasets. by Weidong Ji (129916)

“…Generally speaking, there is no clear linear relationship between scores and the other variables. On longer time scales (6–24 hours), the score and correlation between ERA5 and observations further increased, while the centered root-mean-square error (CRMSE) and standard deviation decrease. 4) Hourly wind data with a regular spatial distribution in ERA5 reanalysis provides valuable information for further detailed research on meteorology or renewable energy perspectives, but some inherent shortcomings should be considered.…”

General technical specification for GW154/6700. by Weidong Ji (129916)

“…Generally speaking, there is no clear linear relationship between scores and the other variables. On longer time scales (6–24 hours), the score and correlation between ERA5 and observations further increased, while the centered root-mean-square error (CRMSE) and standard deviation decrease. 4) Hourly wind data with a regular spatial distribution in ERA5 reanalysis provides valuable information for further detailed research on meteorology or renewable energy perspectives, but some inherent shortcomings should be considered.…”

Summary of related studies. by Most. Fatematuz Zohora (19429152)

“…The DBDAA introduces real-time processing for safety checks, significantly improving system efficiency and reducing the risk of deadlocks. …”

Process execution halted due to deadlock. by Most. Fatematuz Zohora (19429152)

“…The DBDAA introduces real-time processing for safety checks, significantly improving system efficiency and reducing the risk of deadlocks. …”

The effect of feeding and starvation on the proliferative competence and cell cycle re-entry dynamics of Vasa2+/Piwi1+ cells. by Eudald Pascual-Carreras (12115380)

Overexpression of miR-129-5p alleviated the depressive-like phenotypes of CRS and LPS treated mice. by Qiaozhen Qin (13159201)

Subjects:

Growth of Sf14 in various media. by Nykki D. Ross (20933968)

Subjects:

Growth of Sf14 in various media and temperatures. by Nykki D. Ross (20933968)

Subjects:

The TOR inhibitors Rapamycin and AZD-8055 strongly reduce RPS6 phosphorylation and cell proliferation in Vasa2+/Piwi1+ cells. by Eudald Pascual-Carreras (12115380)

“…<i>n</i> = 2–4 biological replicates per condition, with 15 individuals per replicate. Significance levels for Student <i>t</i> test are indicated for adjusted <i>p</i> values: *<i>p</i> < 0.05, ***<i>p</i> < 0.001, ***<i>p</i> < 0.0001. d: day(s), n.s.: non-significant. …”

Lys823 in NLRP3 is essential for its K27-linked ubiquitination and autophagic degradation. by Huaji Jiang (16907514)

VANGL2 specifically inhibits the activation of NLRP3 inflammasome. by Huaji Jiang (16907514)

Overexpression of miR-129-5p alleviated depression-like behaviors by increasing ATP content. by Qiaozhen Qin (13159201)

Subjects:

VANGL2 recruits NLRP3 to OPTN for selective autophagic degradation. by Huaji Jiang (16907514)

Testing set error. by Xiangjuan Liu (618000)

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. …”

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

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. …”

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

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. …”

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

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. …”

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

“…<div><p>This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. …”