Symbolic regression is a powerful technique that can discover analytical equations describe data, which lead to explainable models and generalizability outside of the training data set. In contrast, neural networks have achieved amazing levels accuracy on image recognition natural language processing tasks, but are often seen as black-box difficult interpret typically extrapolate poorly. Here we use network-based architecture for symbolic called Equation Learner (EQL) network integrate it...

Experimental data are often affected by uncontrolled variables that make analysis and interpretation difficult. For spatiotemporal systems, this problem is further exacerbated their intricate dynamics. Modern machine learning methods particularly well suited for analyzing modeling complex datasets, but to be effective in science, the result needs interpretable. We demonstrate an unsupervised technique extracting interpretable physical parameters from noisy building a transferable model of...

Symbolic regression is a machine learning technique that can learn the equations governing data and thus has potential to transform scientific discovery. However, symbolic still limited in complexity dimensionality of systems it analyze. Deep learning, on other hand, transformed its ability analyze extremely complex high-dimensional datasets. We propose neural network architecture extend parametric where some coefficient may vary, but structure underlying equation remains constant....

The kinetics for imino hydrogen exchange, at individual base pairs in the DNA sequence corresponding to lactose operon operator of Escherichia coli, has been examined by NMR saturation recovery measurements as a function temperature. Three 17-base-pair subsections lac were chemically synthesized these studies. results support our previous observations 36-base-pair complete fragment that used indicate faster opening GTG/CAC is also site mutations leading highest level constitutive...

Identifying the governing equations of a nonlinear dynamical system is key to both understanding physical features and constructing an accurate model dynamics that generalizes well beyond available data. We propose machine learning framework for discovering these using only partial observations, combining encoder state reconstruction with sparse symbolic model. Our tests show this method can successfully reconstruct full identify underlying variety ODE PDE systems.

Conservation laws are key theoretical and practical tools for understanding, characterizing, modeling nonlinear dynamical systems. However, many complex systems, the corresponding conserved quantities difficult to identify, making it hard analyze their dynamics build stable predictive models. Current approaches discovering conservation often depend on detailed information or rely black box parametric deep learning methods. We instead reformulate this task as a manifold problem propose...

Our objective was to examine the relationship between patient weight and dose of clomiphene required for pregnancy so as assess validity recommendations that be limited 100 mg. We retrospectively analysed weight-dose in 1681 pregnancies pregnancy, births, multiple number pre-ovulatory follicles endometrial thickness 2841 cycles treatment, 25-250 mg, 5 days before intrauterine insemination (IUI). Doses >100 mg/day were used 27.4% patients who weighed >90 kg 14.7% all pregnancies. In IUI...

By comparing the fluorescence emission properties of wild type lac repressor with two repressors altered at tryptophan 190 and 209, respectively, we show that residue 209 has its environment changed, either by own motion, or surrounding amino acids when an inducer molecule is bound.Substitution this other results in molecules reduced affinity for molecules, indicating geometry affects inducer-binding site.From potassium iodide quenching from tryptophans, attempts to react native...

This paper describes the isolation of 3-fluorotyrosine-substituted lac repressor, and its 19F nuclear magnetic resonance spectrum. From spectrum, one can conclude that for each four identical subunits repressor there are or five surface tyrosines, two buried internal tyrosine with an phenolic group ionized involved in a hydrogen bond. Conditions described be used 3-fluorotyrosine substitution variety Escherichia coli proteins studies.

By using a systematic genetic approach, the resonances in 19F NMR spectrum of 3-fluorotyrosine-substituted lac repressor protein have been assigned. The data indicate that each monomer consists two distinct and independent domains. One domain, NH2-terminal sixth primary sequence, which has shown to be very important for DNA binding, is mobile. remaining COOH-terminal sequence more rigid. Ligands repressor, affect its binding capability, lead conformational changes domain. approach assignment...

We show here the changes in NMR spectra of Escherichia coli lac repressor when bound to isolated operator DNA. The observations focus on aromatic residues--four tyrosines and a single histidine--in amino-terminal DNA binding domain repressor. There is good correlation between chemical shift seen by 19F compared with 1 H otherwise identical repressor--DNA complexes. results suggest that do not intercalate spectral similarly sized fragments, containing sequence, are different. Thus,...

We explore the initial moments of impact between two dense granular clusters in a two-dimensional geometry. The particles are composed solid CO$_{2}$ and levitated on hot surface. Upon collision, propagation dynamic "jamming front" produces distinct regime for energy dissipation gas which translational kinetic decreases by over 90%. Experiments associated simulations show that loss obeys power law time, $\Delta E=-Kt^{3/2}$, form can be predicted from arguments.

Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating discovery novel materials. Recently, publicly available data repositories have grown rapidly. This growth encompasses not only more materials, but also a greater variety quantity their associated properties. Existing machine learning efforts in science focus primarily on single-modality tasks, i.e., relationships between single physical property, thus...

In a granular gas, inelastic collisions produce an instability in which the constituent particles cluster heterogeneously. These clusters then interact with each other, further decreasing their kinetic energy. We report experiments of free dense two-dimensional geometry. The are composed solid CO${}_{2}$, float nearly frictionlessly on hot surface due to sublimated vapor. After two $\ensuremath{\approx}$100 collide, there distinct stages evolution. First, translational energy rapidly...