The progenitors of type Ia supernovae in the semidetached binaries with red giant donors

Context. The companions of the exploding carbon-oxygen white dwarfs (CO WDs) for producing type Ia supernovae (SNe Ia) are still not conclusively confirmed. A red-giant (RG) star has been suggested to be the mass donor of the exploding WD, named as the symbiotic channel. However, previous studies on the this channel gave a relatively low rate of SNe Ia. Aims. We aim to systematically investigate the parameter space, Galactic rates and delay time distributions of SNe Ia from the symbiotic channel by employing a revised mass-transfer prescription. Methods. We adopted an integrated mass-transfer prescription to calculate the mass-transfer process from a RG star onto the WD. In this prescription, the mass-transfer rate varies with the local material states. Results. We evolved a large number of WD+RG systems, and found that the parameter space of WD+RG systems for producing SNe Ia is significantly enlarged. This channel could produce SNe Ia with intermediate and old ages, contributing to at most 5% of all SNe Ia in the Galaxy. Our model increases the SN Ia rate from this channel by a factor of 5. We suggest that the symbiotic systems RS Oph and T CrB are strong candidates for the progenitors of SNe Ia.Comment: 8 pages, 6 figure

All too human?

Review of three books: 'Music and humanism: an essay in the aesthetics of music' by R A Sharpe; 'The spheres of music: a gathering of essays' by Leonard B Meyer; Critical entertainments: music old and new' by Charles Rosen, which appeared in Musical Times Autumn 2001

Quaternary vegetation history in Hungary

Detailed information of SSR markers used for genotyping the RIL population. (XLSX 49 kb

LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction

<div><p>Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs’ potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases’ statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of the training data and a <i>L</i>₁-norm constraint to select important miRNA/disease features for prediction. The strength of dimensionality reduction enabled the model to be easily extended to much higher dimensional datasets than those exploited in this study. Experimental results showed that LRSSLMDA outperformed ten previous models: the AUC of 0.9178 in global leave-one-out cross validation (LOOCV) and the AUC of 0.8418 in local LOOCV indicated the model’s superior prediction accuracy; and the average AUC of 0.9181+/-0.0004 in 5-fold cross validation justified its accuracy and stability. In addition, three types of case studies further demonstrated its predictive power. Potential miRNAs related to Colon Neoplasms, Lymphoma, Kidney Neoplasms, Esophageal Neoplasms and Breast Neoplasms were predicted by LRSSLMDA. Respectively, 98%, 88%, 96%, 98% and 98% out of the top 50 predictions were validated by experimental evidences. Therefore, we conclude that LRSSLMDA would be a valuable computational tool for miRNA-disease association prediction.</p></div

Table_1_v1_Status Quo and Influencing Factors of Discharge Readiness of Patients with Bilateral Ureteral Stoma After Radical Cystectomy.docx

Bladder cancer is a common malignancy of the urinary system, which occurs mostly in elderly men, and the incidence is increasing year by year. To analyze the status quo and related factors of discharge readiness of patients with bilateral ureteral stoma after radical cystectomy, a retrospective, noncomparative was performed. 544 patients with bilateral ureteral stoma after radical cystectomy in our hospital from December 2018 to December 2020 were selected. The self-designed questionnaire, discharge readiness scale (RHDS) and discharge guidance quality scale (QDTS) were used to investigate the general data, and multiple linear regression was used to analyze the related influencing factors. The total score of RHDS was (72.57 ± 18.56) and the total score of QDTS was (105.63 ± 24.18); the total score of RHDS was positively correlated with the total score of QDTS (r = 0.882, p = 0.000); the results of multiple linear regression showed that age, discharge direction and care mode were the main factors influencing the discharge readiness of patients (p < 0.05). In conclusions, the discharge readiness of patients with bilateral ureteral stoma after radical cystectomy is in the medium level, and there is a large space for improvement. Nurses should strengthen the guidance and nursing of patients’ discharge preparation to reduce the incidence of postoperative complications and readmission rate.</p

Prediction of the top 50 potential Colon Neoplasms-related miRNAs based on known associations in HMDD v2.0 database.

<p>The first column records top 1–25 related miRNAs. The third column records the top 26–50 related miRNAs. The evidences for the associations were either dbDEMC and miR2Disease or more recent experimental literatures with the corresponding PMIDs.</p

Performance comparison between LRSSLMDA and ten previous disease-miRNA association prediction models (PBMDA, MaxFlow, MCMDA, NCPMDA, HGIMDA, MiRAI, WBSMDA, HDMP, RLSMDA and RWRMDA) in terms of ROC curves and AUCs based on global and local LOOCV.

<p>As a result, LRSSLMDA outperformed other models by achieving an AUC of 0.9178 in global LOOCV and an AUC of 0.8418 in local LOOCV.</p

To evaluate the predictability of different feature profiles in our study, the statistical profile and the graph theoretical profile were used separately for prediction in global LOOCV, local LOOCV and 5-fold cross validation.

<p>The corresponding AUCs are shown in the second and third columns, and compared with the AUCs for LRSSLMDA with both profiles in the fourth column.</p

Prediction of the top 50 potential Breast Neoplasms-related miRNAs based on known associations in the old version of HMDD, that is, HMDD v1.0.

<p>The first column records top 1–25 related miRNAs. The third column records the top 26–50 related miRNAs. The evidences for the associations were either HMDD v2.0, dbDEMC and miR2Disease or more recent experimental literatures with the corresponding PMIDs.</p

Prediction of the top 50 potential Lymphoma-related miRNAs based on known associations in HMDD v2.0 database.

<p>The first column records top 1–25 related miRNAs. The third column records the top 26–50 related miRNAs. The evidences for the associations were either dbDEMC and miR2Disease or more recent experimental literatures with the corresponding PMIDs.</p