Data for: Impacts of iron redistribution on the formation of pedogenic magnetic minerals in a basaltic red soil on Hainan Island, China

The attached files (ordered from 1 to 6) are the geochemical and magnetic parameters for a 49-m red soil core derived from multi-layer basalt in northern Hainan Island, China. File 1 to 4 are excel format. File 5 and 6 were compressed files. File 1 contains data of geochemical elements (Fe, Al, Ti and Th) of the 49 m-red soil. File 2 contains basic magnetic parameters, such as χ, ARM and IRM. File 3 are hysteresis data for day-plot. File 4 are data for selected hysteresis loops. File 5 are data for first order reversal curves (FORC) diagram (.frc format); it can be viewed as txt or processed with FORC software, such as FORCinell. File 6 are data for IRM acquisiton and unmixing curves.</p

Image_1_Efficiency and safety of vitrification of surplus oocytes following superovulation: a comparison of different clinical indications of oocyte cryopreservation in IVF/ICSI cycles.jpeg

ObjectiveTo evaluate the effectiveness and safety of utilizing the small number of remaining vitrified oocytes after the failure of adequate fresh sibling oocytes. The outcome of present study would provide more comprehensive information about possible benefits or disadvantage to cryopreserve supernumerary oocytes for patients who have plenty oocytes retrieved.MethodsThis retrospective cohort study included 791 IVF/ICSI cycles using 6344 oocytes that had been vitrified in the Reproductive Hospital affiliated to Shandong University between January 2013 and December 2019.They were divided into three groups: SOC group (supernumerary oocytes cryopreservation), relative-MOC group (relative male factor-oocyte cryopreservation), and absolute-MOC group (absolute male factor-oocyte cryopreservation). Laboratory and clinical outcomes were analysed, and multivariate regression analysis was used to study the effect of different indications of vitrification on CLBR.ResultsThe CLBR was highest in absolute-MOC, and lowest in SOC (39.0% vs 28.9%, P=0.006); however, after adjusting for confounding factors, the difference was not statistically significant. Multivariable regression analysis showed no impact of indications of vitrified oocytes on CLBR according to controlled age, BMI, preservation duration, use of donor sperm or not, use of PESA/TESA or not, number of oocytes retrieved, number of oocytes thawed, and oocyte survival rate. The preliminary data of safety showed no significant differences in the perinatal and neonatal outcoms after ET and FET between the SOC and MOC groups.ConclusionDifferent indications of vitrification did not affect CLBR. The CLBR of vitrified oocytes for different indications was correlated with age and number of warmed oocytes. For women who have plenty oocytes retrieved, the strategy of cryopreserving a small number of oocytes is a valuable option and might benefit them in the future. Additional data from autologous oocyte vitrification research employing a large-scale and variable-controlled methodology with extending follow-up will complement and clarify the current results.</p

Expression and nuclear localization of p.Y5D mutant.

<p>HEK293T cells were transiently transfected with empty vector (Mock), wild-type (WT) or mutant (MT) pEGFP-C3-Sf1 expression vector. The nuclei were counterstained with Hoechst33342 (blue). Scale bars = 10 um.</p

Molecular and phenotypic features of premature ovarian failure (POF) cases in 46,XY DSD families with <i>NR5A1</i> mutations.

a<p>All mutations are heterozygous except for c.877G>A (p.D293N).</p><p>46, XY DSD: 46, XY disorder of sex development; PA: primary amenorrhea; SA: secondary amenorrhea; LBD: ligand binding domain.</p

Transactivation activity assay.

<p>Co-transfection of empty (Mock), wild-type (WT), or mutant (MT) Sf1 expression vectors and a <i>Amh</i> (A), <i>Inhibin-a</i> (B), <i>Cyp11a1</i> (C) or <i>Cyp19a1</i> (D) promoter reporter were performed in HEK293T cells. Results are expressed as a percentage of WT activity (RLU WT%). The potential dominant negative effect of the p.Y5D mutant was assessed by co-transfecting WT expression vector with empty or MT vector (1∶1) (A, B, C, D) and increasing MT (0, 10, 20, 30, 40 ng) with 10 ng empty (−) or 10 ng WT (+) vector (Amh only) (E) in HEK293T cells. ** P<0.01, *** P<0.001. RLU, relative light units.</p

Molecular and phenotypic features of 46,XX sporadic premature ovarian failure (POF) cases with <i>NR5A1</i> mutations.

a<p>The table only refers to novel non-synonymous mutations and all mutations are heterozygous.</p><p>PA: primary amenorrhea; SA: secondary amenorrhea; DBD: DNA binding domain; LBD: ligand binding domain.</p

Mutations in <i>NR5A1</i> gene associated with POF.

<p>(A). Schematic presentation of the distribution of <i>NR5A1</i> mutations associated with POF. DBD: DNA-binding domain, LBD: ligand binding domain, AF2: activation function domain 2. (B). Sequence alignment of SF1 among orthologs with tyrosine residue highlighted.</p

DataSheet_1_Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning.csv

BackgroundWe attempted to develop a progression prediction model for local advanced rectal cancer(LARC) patients who received preoperative neoadjuvant chemoradiotherapy(NCRT) and operative treatment to identify high-risk patients in advance.MethodsData from 272 LARC patients who received NCRT and total mesorectal excision(TME) from 2011 to 2018 at the Fourth Hospital of Hebei Medical University were collected. Data from 161 patients with rectal cancer (each sample with one target variable (progression) and 145 characteristic variables) were included. One Hot Encoding was applied to numerically represent some characteristics. The K-Nearest Neighbor (KNN) filling method was used to determine the missing values, and SmoteTomek comprehensive sampling was used to solve the data imbalance. Eventually, data from 135 patients with 45 characteristic clinical variables were obtained. Random forest, decision tree, support vector machine (SVM), and XGBoost were used to predict whether patients with rectal cancer will exhibit progression. LASSO regression was used to further filter the variables and narrow down the list of variables using a Venn diagram. Eventually, the prediction model was constructed by multivariate logistic regression, and the performance of the model was confirmed in the validation set.ResultsEventually, data from 135 patients including 45 clinical characteristic variables were included in the study. Data were randomly divided in an 8:2 ratio into a data set and a validation set, respectively. Area Under Curve (AUC) values of 0.72 for the decision tree, 0.97 for the random forest, 0.89 for SVM, and 0.94 for XGBoost were obtained from the data set. Similar results were obtained from the validation set. Twenty-three variables were obtained from LASSO regression, and eight variables were obtained by considering the intersection of the variables obtained using the previous four machine learning methods. Furthermore, a multivariate logistic regression model was constructed using the data set; the ROC indicated its good performance. The ROC curve also verified the good predictive performance in the validation set.ConclusionsWe constructed a logistic regression model with good predictive performance, which allowed us to accurately predict whether patients who received NCRT and TME will exhibit disease progression.</p

DataSheet_2_Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning.csv

BackgroundWe attempted to develop a progression prediction model for local advanced rectal cancer(LARC) patients who received preoperative neoadjuvant chemoradiotherapy(NCRT) and operative treatment to identify high-risk patients in advance.MethodsData from 272 LARC patients who received NCRT and total mesorectal excision(TME) from 2011 to 2018 at the Fourth Hospital of Hebei Medical University were collected. Data from 161 patients with rectal cancer (each sample with one target variable (progression) and 145 characteristic variables) were included. One Hot Encoding was applied to numerically represent some characteristics. The K-Nearest Neighbor (KNN) filling method was used to determine the missing values, and SmoteTomek comprehensive sampling was used to solve the data imbalance. Eventually, data from 135 patients with 45 characteristic clinical variables were obtained. Random forest, decision tree, support vector machine (SVM), and XGBoost were used to predict whether patients with rectal cancer will exhibit progression. LASSO regression was used to further filter the variables and narrow down the list of variables using a Venn diagram. Eventually, the prediction model was constructed by multivariate logistic regression, and the performance of the model was confirmed in the validation set.ResultsEventually, data from 135 patients including 45 clinical characteristic variables were included in the study. Data were randomly divided in an 8:2 ratio into a data set and a validation set, respectively. Area Under Curve (AUC) values of 0.72 for the decision tree, 0.97 for the random forest, 0.89 for SVM, and 0.94 for XGBoost were obtained from the data set. Similar results were obtained from the validation set. Twenty-three variables were obtained from LASSO regression, and eight variables were obtained by considering the intersection of the variables obtained using the previous four machine learning methods. Furthermore, a multivariate logistic regression model was constructed using the data set; the ROC indicated its good performance. The ROC curve also verified the good predictive performance in the validation set.ConclusionsWe constructed a logistic regression model with good predictive performance, which allowed us to accurately predict whether patients who received NCRT and TME will exhibit disease progression.</p