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Aims: Mammography, commonly used for breast cancer screening in women, can also predict cardiovascular disease. We developed mammography-based deep learning models for predicting coronary artery calcium (CAC) scores, an established predictor of coronary events., Methods and Results: We evaluated a subset of Korean adults who underwent image mammography and CAC computed tomography and randomly selected approximately 80% of the participants as the training dataset, used to develop a convolutional neural network (CNN) to predict detectable CAC. The sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), and overall accuracy of the model's performance were evaluated. The training and validation datasets included 5235 and 1208 women, respectively [mean age, 52.6 (±10.2) years], including non-zero cases (46.8%). The CNN-based deep learning prediction model based on the Resnet18 model showed the best performance. The model was further improved using contrastive learning strategies based on positive and negative samples: sensitivity, 0.764 (95% CI, 0.667-0.830); specificity, 0.652 (95% CI, 0.614-0.710); AUROC, 0.761 (95% CI, 0.742-0.780); and accuracy, 70.8% (95% CI, 68.8-72.4). Moreover, including age and menopausal status in the model further improved its performance (AUROC, 0.776; 95% CI, 0.762-0.790). The Framingham risk score yielded an AUROC of 0.736 (95% CI, 0.712-0.761)., Conclusion: Mammography-based deep learning models showed promising results for predicting CAC, performing comparably to conventional risk models. This indicates mammography's potential for dual-risk assessment in breast cancer and cardiovascular disease. Further research is necessary to validate these findings in diverse populations, with a particular focus on representation from national breast screening programmes., Competing Interests: Conflict of interest: All authors report no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In this work, the enhanced resistive switching of ZrN-based resistive switching memory is demonstrated by embedding TiO 2 layer between Ag top electrode and ZrN switching layer. The Ag/ZrN/n-Si device exhibits unstable resistive switching as a result of the uncontrollable Ag migration. Both unipolar and bipolar resistive switching with high RESET current were observed. Negative-SET behavior in the Ag/ZrN/n-Si device makes set-stuck, causing permanent resistive switching failure. On the other hand, the analogue switching in the Ag/TiO 2 /ZrN/n-Si device, which could be adopted for the multi-bit data storage applications, is obtained. The gradual switching in Ag/TiO 2 /ZrN/n-Si device is achieved, possibly due to the suppressed Ag diffusion caused by TiO 2 inserting layer. The current-voltage (I-V) switching characteristics of Ag/ZrN/n-Si and Ag/TiO 2 /ZrN/n-Si devices can be well verified by pulse transient. Finally, we established that the Ag/TiO 2 /ZrN/n-Si device is suitable for neuromorphic application through a comparison study of conductance update. This paper paves the way for neuromorphic application in nitride-based memristor devices.

Biclustering is a process of finding groups of genes that behave similarly under a subset of conditions. In this article, we propose an efficient biclustering algorithm, namely RN + , to identify biologically meaningful biclusters in gene expression data. The RN + algorithm finds biologically meaningful biclusters through a novel gene filtering using protein-protein interaction network, gene searching, gene grouping, and queuing process. It also efficiently removes duplicate biclusters. We tested the proposed RN + on five real microarray datasets, and compared its performance with seven competitive biclustering algorithms. The experimental results show that RN + efficiently finds functionally enriched and biologically meaningful biclusters for large gene expression datasets, and outperforms the other tested biclustering algorithms on real datasets.