The field of materials discovery is undergoing an unprecedented transition from laboratory tocomputer. Behind this transition is the new ability to accurately compute material properties, especially
energetic stability, from first principles with density functional theory (DFT). However,
DFT remains computationally expensive, and DFT-based materials discovery is intractable, especially
in high...
Cite this article: Shen, J., McFarland, A.G., Blaustein, R.A. et al. An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics. Microbiome 10, 206 (2022). https://doi.org/10.1186/s40168-022-01412-x and Background: Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. Metagenomics-based techniques are promising due to their untargeted nature but are currently challenged by several limitations: (1) they are not powerful enough to extract valid signals out of the background noise for low-biomass samples,...
“Where Do We Come From? What Are We? Where Are We Going?” is the name for one of French artist Paul Gauguin’s most influential paintings. Unsurprisingly, these very questions have occupied the minds of countless philosophers, artists, and scholars since the beginning of human civilization. These questions become especially salient...
Engineering design is a systematic process of identifying needs and their translation into functional systems. This is a cyclic process that alternates between the acquisition of data and the synthesis of said data to inform design decisions. Conventionally, data from physical experiments are used to explore the efficacy of alternative...
Metal–organic frameworks (MOFs) are a class of crystalline materials composed of metal nodes connected by organic linkers. Due to their high degree of synthetic tunability, MOFs have been considered for a wide range of applications, including many that rely on a change in oxidation state. While most MOFs are generally...
In the short amount of time that genetic manipulation has been possible through CRISPR technology, myriad applications have been developed. Results from one of the most promising applications of this technology, pooled screens, have shown that single guide RNAs (sgRNAs), RNA sequences used to target specific regions of the genome,...
Sound is one of the most important mediums to understand the environment around us. Identifying a sound event in prerecorded audio (such as a police siren, a dog bark, or a creaking door in soundscapes) leads to a better understanding of the context where the sound events occurred. To do...
In machine learning, classification that assigns a label to a sample is a fundamental problem and serves a building block for various applications of artificial intelligence such as speech recognition, sentimental analysis, and image recognition. During the last years, deep learning rejuvenates artificial intelligence; in particular, it leads to tremendous...
Functional electronic materials are difficult to design due to the complex interplay among chemistry, atomic structure, and electrical properties. This dilemma is further amplified in transition metal compounds which can defy the band-theory description of non-correlated electrons. Exploring the vast possible design space completely with experiments or first-principles simulations is...
Biology is entering the exciting world of big data. Modern high-throughput experimental techniques often produce large datasets that aim to capture complex relationships often found in biological systems. While these larger data sets contain vast amounts of useful information, the answers are often locked behind a wall of numbers. As...
Unstructured data like text is plentiful and possibly contains valuable insights leading to a better decision-making process. Manually obtaining these insights can be costly and time-consuming. Text mining, also known as Text analytics, is developed to derive meaningful information from textual data. It is widely applied in various domains such...
This dissertation uses several interrelated methods derived from corpus linguistics, statistics, and machine learning to infer a number of historically significant voice-leading schemas in a corpus of eighteenth-century Neapolitan solfeggi (exercises for voice with bass accompaniment). The goal of this work is to gain insights not only into the characteristics...
Moving away from fossil fuels requires environmentally friendly and economically viable alternative energy sources. A wide adoption of new technologies for energy production and storage depends on better performing materials. Computational methods, such as electronic structure calculations and machine learning, hold the promise to work in conjunction with traditional experimentation...
Machine learning has been widely applied to solve intricate problems in finance. Yet in options theory, machine learning methods are less visited due to the structural complexity of the derivatives market. This dissertation focuses on using machine learning algorithms to obtain optimal decisions for three distinct option-related problems. In the...
DNA methylation in repetitive elements (RE) suppresses their mobility and maintains genomic stability, and decreases in it are frequently observed in tumor and/or surrogate tissues. Averaging methylation across RE in the genome is widely used to quantify global methylation. Methylation of RE in humans is considered a surrogate for global...