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1. Clusters of acidic and hydrophobic residues can predict acidic transcriptional activation domains from protein sequence

   https://doi.org/10.1093/genetics/iyad131  

   Kotha, Sanjana R; Staller, Max Valentín (July 2023, GENETICS)

   Kaplan, C (Ed.)

   Abstract Transcription factors activate gene expression in development, homeostasis, and stress with DNA binding domains and activation domains. Although there exist excellent computational models for predicting DNA binding domains from protein sequence, models for predicting activation domains from protein sequence have lagged, particularly in metazoans. We recently developed a simple and accurate predictor of acidic activation domains on human transcription factors. Here, we show how the accuracy of this human predictor arises from the clustering of aromatic, leucine, and acidic residues, which together are necessary for acidic activation domain function. When we combine our predictor with the predictions of convolutional neural network (CNN) models trained in yeast, the intersection is more accurate than individual models, emphasizing that each approach carries orthogonal information. We synthesize these findings into a new set of activation domain predictions on human transcription factors. 

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2. Optimal local truncation error method to solution of 2‐D time‐independent elasticity problems with optimal accuracy on irregular domains and unfitted Cartesian meshes

   https://doi.org/10.1002/nme.6952  

   Idesman, Alexander; Dey, Bikash (March 2022, International Journal for Numerical Methods in Engineering)

   Abstract Recently we have developed the optimal local truncation error method (OLTEM) for scalar PDEs on irregular domains and unfitted Cartesian meshes. Here, OLTEM is extended to a much more general case of a system of PDEs for the 2‐D time‐independent elasticity equations on irregular domains. Compact 9‐point uniform and nonuniform stencils (with the computational costs of linear finite elements) are used with OLTEM. The stencil coefficients are assumed to be unknown and are calculated by the minimization of the local truncation error. It is shown that the second order of accuracy is the maximum possible accuracy for 9‐point stencils independent of the numerical technique used for their derivations. The special treatment of the Neumann boundary conditions has been developed that does not increase the size of the stencils. The numerical examples are in agreement with the theoretical findings. They also show that due to the minimization of the local truncation error, OLTEM is much more accurate than linear finite elements and than quadratic finite elements (up to engineering accuracy of 0.1%–1%) at the same numbers of degrees of freedom. Due to the computational efficiency and trivial unfitted Cartesian meshes that are independent of irregular domains, the proposed technique with no remeshing for the shape change of irregular domains will be effective for many engineering applications. 

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3. AI-Assisted Scientific Data Collection with Iterative Human Feedback

   Mandel, Travis; Boyd, James; Carter, Sebastian J. (May 2021, Proceedings of the AAAI Conference on Artificial Intelligence)

   Although artificial intelligence has revolutionized data analysis, significantly less work has focused on using AI to improve scientific data collection. Past work in AI for data collection has typically assumed the objective function is well-defined by humans before starting an experiment; however, this is a poor fit for scientific domains where new discoveries and insights are made as data is being collected. In this paper we present a new framework to allow AI systems to work together with humans (e.g. scientists) to collect data more effectively in simple scientific domains. We present a novel algorithm, TESA, which seeks to achieve good performance by learning from past human behavior how to direct data to places that are likely to become scientifically interesting in the future. We analyze the problem theoretically, defining a novel notion of regret in this setting and showing that TESA is zero regret. Next, we show that TESA outperforms other related algorithms in simulations using real data drawn from three diverse domains (economics, mental health, and cognitive psychology). Finally, we run experiments with human subjects across these scientific domains to compare our iterative human-in-the-loop process to a (more standard) workflow in which information is communicated to the AI a priori. 

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4. Electric field response of ferroelectric domains near non-polar precipitates in BaTiO3-based ceramics

   https://doi.org/10.1063/5.0211248  

   Taylor, Odin; Chaffee, Ethan; Liu, Binzhi; Zhao, Changhao; Rödel, Jürgen; Zhou, Lin; Tan, Xiaoli (May 2024, Applied Physics Letters)

   Precipitates have recently been found to significantly enhance the mechanical quality factor in piezoelectric ceramics. Such a piezoelectric hardening effect was attributed to strong interactions between ferroelectric domains and precipitates. In the present work, the response of domains to applied electric fields is observed in situ via transmission electron microscopy in aged (Ba, Ca)TiO3 ceramics with precipitates to reveal the underlying mechanism of this phenomenon. Ferroelectric domains in the Ba-rich matrix grain are observed to be more concentrated near non-polar Ca-rich precipitates. With increasing applied voltage, domains separate from precipitates merge together first, while those near precipitates persist to higher voltages. During ramping down, domains nucleate from precipitates. These direct observations confirm the strong interactions between ferroelectric domains and precipitates in piezoelectric ceramics. 

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5. Simple biochemical features underlie transcriptional activation domain diversity and dynamic, fuzzy binding to Mediator

   https://doi.org/10.7554/ELIFE.68068  

   Sanborn, Adrian L; Yeh, Benjamin T; Feigerle, Jordan T; Hao, Cynthia V; Townshend, Raphael JL; Lieberman Aiden, Erez; Dror, Ron O; Kornberg, Roger D (April 2021, eLife)

   null (Ed.)

   Cells adapt and respond to changes by regulating the activity of their genes. To turn genes on or off, they use a family of proteins called transcription factors. Transcription factors influence specific but overlapping groups of genes, so that each gene is controlled by several transcription factors that act together like a dimmer switch to regulate gene activity. The presence of transcription factors attracts proteins such as the Mediator complex, which activates genes by gathering the protein machines that read the genes. The more transcription factors are found near a specific gene, the more strongly they attract Mediator and the more active the gene is. A specific region on the transcription factor called the activation domain is necessary for this process. The biochemical sequences of these domains vary greatly between species, yet activation domains from, for example, yeast and human proteins are often interchangeable. To understand why this is the case, Sanborn et al. analyzed the genome of baker’s yeast and identified 150 activation domains, each very different in sequence. Three-quarters of them bound to a subunit of the Mediator complex called Med15. Sanborn et al. then developed a machine learning algorithm to predict activation domains in both yeast and humans. This algorithm also showed that negatively charged and greasy regions on the activation domains were essential to be activated by the Mediator complex. Further analyses revealed that activation domains used different poses to bind multiple sites on Med15, a behavior known as ‘fuzzy’ binding. This creates a high overall affinity even though the binding strength at each individual site is low, enabling the protein complexes to remain dynamic. These weak interactions together permit fine control over the activity of several genes, allowing cells to respond quickly and precisely to many changes. The computer algorithm used here provides a new way to identify activation domains across species and could improve our understanding of how living things grow, adapt and evolve. It could also give new insights into mechanisms of disease, particularly cancer, where transcription factors are often faulty. 

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