Van der Waals Epitaxially Grown Molecular Crystal Dielectric Sb2O3 for 2D Electronics.

Two-dimensional (2D) semiconducting materials have attracted significant interest as promising candidates for channel materials owing to their high mobility and gate tunability at atomic-layer thickness. However, the development of 2D electronics is impeded due to the difficulty in formation of high-quality dielectrics with a clean and nondestructive interface. Here, we report the direct van der Waals epitaxial growth of a molecular crystal dielectric, Sb2O3, on 2D materials by physical vapor deposition. The grown Sb2O3 nanosheets showed epitaxial relations of 0 and 180° with the 2D template, maintaining high crystallinity and an ultrasharp vdW interface with the 2D materials. As a result, the Sb2O3 nanosheets exhibited a high breakdown field of 18.6 MV/cm for 2L Sb2O3 with a thickness of 1.3 nm and a very low leakage current of 2.47 × 10-7 A/cm2 for 3L Sb2O3 with a thickness of 1.96 nm. We also observed two types of grain boundaries (GBs) with misorientation angles of 0 and 60°. The 0°-GB with a well-stitched boundary showed higher electrical and thermal stabilities than those of the 60°-GB with a disordered boundary. Our work demonstrates a method to epitaxially grow molecular crystal dielectrics on 2D materials without causing any damage, a requirement for high-performance 2D electronics.

Evaluation and optimization of sequence-based gene regulatory deep learning models.

Neural networks have emerged as immensely powerful tools in predicting functional genomic regions, notably evidenced by recent successes in deciphering gene regulatory logic. However, a systematic evaluation of how model architectures and training strategies impact genomics model performance is lacking. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast, to best capture the relationship between regulatory DNA and gene expression. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. While some benchmarks produced similar results across the top-performing models, others differed substantially. All top-performing models used neural networks, but diverged in architectures and novel training strategies, tailored to genomics sequence data. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide any given model into logically equivalent building blocks. We tested all possible combinations for the top three models and observed performance improvements for each. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets. Overall, we demonstrate that high-quality gold-standard genomics datasets can drive significant progress in model development.

Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences.

The breakthrough high-throughput measurement of the cis-regulatory activity of millions of randomly generated promoters provides an unprecedented opportunity to systematically decode the cis-regulatory logic that determines the expression values. We developed an end-to-end transformer encoder architecture named Proformer to predict the expression values from DNA sequences. Proformer used a Macaron-like Transformer encoder architecture, where two half-step feed forward (FFN) layers were placed at the beginning and the end of each encoder block, and a separable 1D convolution layer was inserted after the first FFN layer and in front of the multi-head attention layer. The sliding k-mers from one-hot encoded sequences were mapped onto a continuous embedding, combined with the learned positional embedding and strand embedding (forward strand vs. reverse complemented strand) as the sequence input. Moreover, Proformer introduced multiple expression heads with mask filling to prevent the transformer models from collapsing when training on relatively small amount of data. We empirically determined that this design had significantly better performance than the conventional design such as using the global pooling layer as the output layer for the regression task. These analyses support the notion that Proformer provides a novel method of learning and enhances our understanding of how cis-regulatory sequences determine the expression values.

Evaluation of lake modification alternatives for Lake Sihwa, Korea.

After the construction of an embankment at the Bay of Sihwa in Korea, a lake of 56.5 km2 surface area and 330 million m3 volume was created. Because of rapid socioenvironmental changes and the lowering of water quality in Lake Sihwa, various external measures have been proposed and some of them are being implemented. In this paper, we examine two alternatives for in-lake modification: one alternative is zoning of the lake by constructing two submerged dams and the other is channeling of the lake through reclamation. Water quality modeling was conducted for both alternatives to assess their effects. Results of the modeling revealed that the reduction of the lake size through two different approaches, when accompanied with other external measures, would improve the water quality, but to different degrees. The zonation is expected to improve the freshwater quality up to the level supplying 45 million m3 of water per year for agricultural use. The quality of channeled water would be inappropriate for agricultural use, but suitable for outdoor activities such as recreation or fishing regardless of reclamation plans considered.