ScGAN: a generative adversarial network to predict hypothetical superconductors.

Despite having been discovered more than three decades ago, high temperature superconductors (HTSs) lack both an explanation for their mechanisms and a systematic way to search for them. To aid this search, this project proposes ScGAN, a generative adversarial network (GAN) to efficiently predict new superconductors. ScGAN was trained on compounds in Open Quantum Materials Database and then transfer learned onto the SuperCon database or a subset of it. Once trained, the GAN was used to predict superconducting candidates, and approximately 70% of them were determined to be superconducting by a classification model-a 23-fold increase in discovery rate compared to manual search methods. Furthermore, more than 99% of predictions were novel materials, demonstrating that ScGAN was able to potentially predict completely new superconductors, including several promising HTS candidates. This project presents a novel, efficient way to search for new superconductors, which may be used in technological applications or provide insight into the unsolved problem of high temperature superconductivity.

Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge.

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.

Association of Atrial Septal Defects and Bronchopulmonary Dysplasia in Premature Infants.

OBJECTIVE: To evaluate the association between the presence of an atrial septal defect (ASD) and the odds of developing bronchopulmonary dysplasia (BPD) in premature infants. STUDY DESIGN: We identified a cohort of infants that underwent at least one echocardiogram assessment, birth weight 501-1249 g, and gestational age 23-30 weeks discharged from the neonatal intensive care unit from 2004 to 2016. We used a BPD risk estimator to calculate the predicted risk of developing BPD at 6 postnatal ages within the first 28 days of life. We examined the association between the presence of an ASD and the development of BPD using 2 multivariable logistic regression models for each BPD risk severity on each postnatal day. The first model adjusted for predicted BPD risk and the second added therapeutic interventions for BPD. RESULTS: Of 20 496 infants from 228 NICUs who met inclusion criteria, 8892 (43%) were diagnosed with BPD and 1314 (6%) had an ASD. BPD was present in 48% of infants with an ASD and 43% of infants without an ASD. In infants with an ASD, the OR of developing BPD was higher after adjusting for predicted risk of BPD plus therapeutic interventions, regardless of postnatal age or predicted BPD risk severity. CONCLUSIONS: The presence of an ASD was associated with an increased odds of BPD in this cohort. Future trials should consider ASD as a potentially modifiable risk factor in this vulnerable population.