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Abstract The 3D genome prediction in cancer is crucial for uncovering the impact of structural variations (SVs) on tumorigenesis, especially when they are present in noncoding regions. We present InfoHiC, a systemic framework for predicting the 3D cancer genome directly from whole-genome sequencing (WGS). InfoHiC utilizes contig-specific copy number encoding on the SV contig assembly, and performs a contig-to-total Hi-C conversion for the cancer Hi-C prediction from multiple SV contigs. We showed that InfoHiC can predict 3D genome folding from all types of SVs using breast cancer cell line data. We applied it to WGS data of patients with breast cancer and pediatric patients with medulloblastoma, and identified neo topologically associating domains. For breast cancer, we discovered super-enhancer hijacking events associated with oncogenic overexpression and poor survival outcomes. For medulloblastoma, we found SVs in noncoding regions that caused super-enhancer hijacking events of medulloblastoma driver genes (GFI1, GFI1B, and PRDM6). In addition, we provide trained models for cancer Hi-C prediction from WGS at https://github.com/dmcb-gist/InfoHiC , uncovering the impacts of SVs in cancer patients and revealing novel therapeutic targets.

Defect centers in two-dimensional materials has shown promise for applications in quantum information and sensing. Lee et al. computationally discover a class of substitutional defect centers in monolayer transition metal dichalcogenides with promising qubit characteristics operating at telecom wavelengths.

Abstract The emergent magnetic two-dimensional (2D) materials provide ideal solid-state platforms for a broad range of applications including miniaturized spintronics, nonreciprocal optics, and magnetoelectric sensors. Owing to the general environmental sensitivity of 2D magnets, the understanding of ambient effects on 2D magnetism is critical. Apparently, the nature of itinerant ferromagnetism potentially makes metallic 2D magnets insensitive to environmental disturbance. Nevertheless, our systematic study showed that the Curie temperature of metallic 2D Fe3GeTe2 decreases dramatically in the air but thick Fe3GeTe2 exhibits self-protection. Remarkably, we found the air exposure effectively promotes the formation of multiple magnetic domains in 2D Fe3GeTe2, but not in bulk Fe3GeTe2. Our first-principles calculations support the scenario that substrate-induced roughness and tellurium vacancies boost the interaction of 2D Fe3GeTe2 with the air. Our elucidation of the thickness-dependent air-catalyzed evolution of Curie temperatures and magnetic domains in 2D magnets provides critical insights for chemically decorating and manipulating 2D magnets.

Karyotyping of cancer genomes at the base-level is technically challenging. Here, the authors introduce InfoGenomeR, an algorithm that can infer cancer genome karyotypes from whole-genome sequencing data, and test their model on breast, ovarian and brain cancer samples; and identify private and shared mutations between primary and metastatic cancer samples.

Abstract We utilize real-time time-dependent density functional theory and Ehrenfest dynamics scheme to investigate excited-state nonadiabatic dynamics of ligand dissociation of cobalt tricarbonyl nitrosyl, Co(CO)3NO, which is a precursor used for cobalt growth in advanced technologies, where the precursor’s reaction is enhanced by electronic excitation. Based on the first-principles calculations, we demonstrate two dissociation pathways of the NO ligand on the precursor. Detailed electronic structures are further analyzed to provide an insight into dynamics following the electronic excitations. This study sheds light on computational demonstration and underlying mechanism of the electronic-excitation-induced dissociation, especially in molecules with complex chemical bonds such as the Co(CO)3NO.

Content-based image retrieval (CBIR) focuses on video searching with fine-tuning of pre-trained off-the-shelf features. CBIR is an intuitive method for image retrieval, although it still requires labeled datasets for fine-tuning due to the inefficiency caused by annotation. Therefore, we explored an unsupervised model for feature extraction of image contents. We used a variational auto-encoder (VAE) expanding channel of neural networks and studied the activation of layer outputs. In this study, the channel expansion method boosted the capability of image retrieval by exploring more kernels and selecting a layer of comparatively activated object region. The experiment included a comparison of channel expansion and visualization of each layer in the encoder network. The proposed model achieved (52.7%) mAP, which outperformed (36.5%) the existing VAE on the MNIST dataset.

Abstract Background Thymic adenocarcinoma is an extremely rare subtype of thymic epithelial tumors. Due to its rarity, there is currently no sequencing approach for thymic adenocarcinoma. Methods We performed whole exome and transcriptome sequencing on a case of thymic adenocarcinoma and performed subsequent validation using Sanger sequencing. Results The case of thymic adenocarcinoma showed aggressive behaviors with systemic bone metastases. We identified a high incidence of genetic aberrations, which included somatic mutations in RNASEL, PEG10, TNFSF15, TP53, TGFB2, and FAT1. Copy number analysis revealed a complex chromosomal rearrangement of chromosome 8, which resulted in gene fusion between MCM4 and SNTB1 and dramatic amplification of MYC and NDRG1. Focal deletion was detected at human leukocyte antigen (HLA) class II alleles, which was previously observed in thymic epithelial tumors. We further investigated fusion transcripts using RNA-seq data and found an intergenic splicing event between the CTBS and GNG5 transcript. Finally, enrichment analysis using all the variants represented the immune system dysfunction in thymic adenocarcinoma. Conclusion Thymic adenocarcinoma shows highly malignant characteristics with alterations in several cancer-related genes.