Encoding monomorphic and polymorphic types

Many automatic theorem provers are restricted to untyped logics, and existing translations from typed logics are bulky or unsound. Recent research proposes monotonicity as a means to remove some clutter when translating monomorphic to un-typed first-order logic. Here we pursue this approach systematically, analysing formally a variety of encodings that further improve on efficiency while retaining soundness and completeness. We extend the approach to rank-1 polymorphism and present alternative schemes that lighten the translation of polymorphic symbols based on the novel notion of “cover”. The new encodings are implemented in Isabelle/HOL as part of the Sledgehammer tool. We include informal proofs of soundness and correctness, and have formalized the monomorphic part of this work in Isabelle/HOL. Our evaluation finds the new encodings vastly superior to previous schemes

Learning Interpretable Dynamics from Images of a Freely Rotating 3D Rigid Body

In many real-world settings, image observations of freely rotating 3D rigid bodies, such as satellites, may be available when low-dimensional measurements are not. However, the high-dimensionality of image data precludes the use of classical estimation techniques to learn the dynamics and a lack of interpretability reduces the usefulness of standard deep learning methods. In this work, we present a physics-informed neural network model to estimate and predict 3D rotational dynamics from image sequences. We achieve this using a multi-stage prediction pipeline that maps individual images to a latent representation homeomorphic to $\mathbf{SO}(3)$, computes angular velocities from latent pairs, and predicts future latent states using the Hamiltonian equations of motion with a learned representation of the Hamiltonian. We demonstrate the efficacy of our approach on a new rotating rigid-body dataset with sequences of rotating cubes and rectangular prisms with uniform and non-uniform density.Comment: 8 pages, 7 figure

Characterization and Purification of Polydisperse Reconstituted Lipoproteins and Nanolipoprotein Particles

Heterogeneity is a fact that plagues the characterization and application of many self-assembled biological constructs. The importance of obtaining particle homogeneity in biological assemblies is a critical goal, as bulk analysis tools often require identical species for reliable interpretation of the results—indeed, important tools of analysis such as x-ray diffraction typically require over 90% purity for effectiveness. This issue bears particular importance in the case of lipoproteins. Lipid-binding proteins known as apolipoproteins can self assemble with liposomes to form reconstituted high density lipoproteins (rHDLs) or nanolipoprotein particles (NLPs) when used for biotechnology applications such as the solubilization of membrane proteins. Typically, the apolipoprotein and phospholipids reactants are self assembled and even with careful assembly protocols the product often contains heterogeneous particles. In fact, size polydispersity in rHDLs and NLPs published in the literature are frequently observed, which may confound the accurate use of analytical methods. In this article, we demonstrate a procedure for producing a pure, monodisperse NLP subpopulation from a polydisperse self-assembly using size exclusion chromatography (SEC) coupled with high resolution particle imaging by atomic force microscopy (AFM). In addition, NLPs have been shown to self assemble both in the presence and absence of detergents such as cholate, yet the effects of cholate on NLP polydispersity and separation has not been systematically examined. Therefore, we examined the separation properties of NLPs assembled in both the absence and presence of cholate using SEC and native gel electrophoresis. From this analysis, NLPs prepared with and without cholate showed particles with well defined diameters spanning a similar size range. However, cholate was shown to have a dramatic affect on NLP separation by SEC and native gel electrophoresis. Furthermore, under conditions where different sized NLPs were not sufficiently separated or purified by SEC, AFM was used to deconvolute the elution pattern of different sized NLPs. From this analysis we were able to purify an NLP subpopulation to 90% size homogeneity by taking extremely fine elutions from the SEC. With this purity, we generate high quality NLP crystals that were over 100 μm in size with little precipitate, which could not be obtained utilizing the traditional size exclusion techniques. This purification procedure and the methods for validation are broadly applicable to other lipoprotein particles

Isolation, Characterization, and Stability of Discretely-Sized Nanolipoprotein Particles Assembled with Apolipophorin-III

Background: Nanolipoprotein particles (NLPs) are discoidal, nanometer-sized particles comprised of self-assembled phospholipid membranes and apolipoproteins. NLPs assembled with human apolipoproteins have been used for myriad biotechnology applications, including membrane protein solubilization, drug delivery, and diagnostic imaging. To expand the repertoire of lipoproteins for these applications, insect apolipophorin-III (apoLp-III) was evaluated for the ability to form discretely-sized, homogeneous, and stable NLPs. Methodology: Four NLP populations distinct with regards to particle diameters (ranging in size from 10 nm to.25 nm) and lipid-to-apoLp-III ratios were readily isolated to high purity by size exclusion chromatography. Remodeling of the purified NLP species over time at 4uC was monitored by native gel electrophoresis, size exclusion chromatography, and atomic force microscopy. Purified 20 nm NLPs displayed no remodeling and remained stable for over 1 year. Purified NLPs with 10 nm and 15 nm diameters ultimately remodeled into 20 nm NLPs over a period of months. Intra-particle chemical cross-linking of apoLp-III stabilized NLPs of all sizes. Conclusions: ApoLp-III-based NLPs can be readily prepared, purified, characterized, and stabilized, suggesting their utilit

Ultradeep Sequencing of a Human Ultraconserved Region Reveals Somatic and Constitutional Genomic Instability

Ultradeep sequencing of genomes permits the detection of very low-level genomic instability in non-neoplastic tissues of patients with the most common form of inherited colorectal cancer

RADICL-seq identifies general and cell type–specific principles of genome-wide RNA-chromatin interactions

Mammalian genomes encode tens of thousands of noncoding RNAs. Most noncoding transcripts exhibit nuclear localization and several have been shown to play a role in the regulation of gene expression and chromatin remodeling. To investigate the function of such RNAs, methods to massively map the genomic interacting sites of multiple transcripts have been developed; however, these methods have some limitations. Here, we introduce RNA And DNA Interacting Complexes Ligated and sequenced (RADICL-seq), a technology that maps genome-wide RNA-chromatin interactions in intact nuclei. RADICL-seq is a proximity ligation-based methodology that reduces the bias for nascent transcription, while increasing genomic coverage and unique mapping rate efficiency compared with existing methods. RADICL-seq identifies distinct patterns of genome occupancy for different classes of transcripts as well as cell type-specific RNA-chromatin interactions, and highlights the role of transcription in the establishment of chromatin structure

Sequencing three crocodilian genomes to illuminate the evolution of archosaurs and amniotes

The International Crocodilian Genomes Working Group (ICGWG) will sequence and assemble the American alligator (Alligator mississippiensis), saltwater crocodile (Crocodylus porosus) and Indian gharial (Gavialis gangeticus) genomes. The status of these projects and our planned analyses are described

Planned Products of the Mars Structure Service for the InSight Mission to Mars

Abstract The InSight lander will deliver geophysical instruments to Mars in 2018, including seismometers installed directly on the surface (Seismic Experiment for Interior Structure, SEIS). Routine operations will be split into two services, the Mars Structure Service(MSS) and Marsquake Service (MQS), which will be responsible, respectively, for defining the structure models and seismicity catalogs from the mission. The MSS will deliver a series of products before the landing, during the operations, and finally to the Planetary Data System (PDS) archive. Prior to the mission, we assembled a suite of a priori models of Mars, based on estimates of bulk composition and thermal profiles. Initial models during the mission will rely on modeling surface waves and impact-generated body waves independent of prior knowledge of structure. Later modeling will include simultaneous inversion of seismic observations for source and structural parameters. We use Bayesian inversion techniques to obtain robust probability distribution functions of interior structure parameters. Shallow structure will be characterized using the hammering of the heatflow probe mole, as well as measurements of surface wave ellipticity. Crustal scale structure will be constrained by measurements of receiver function and broadband Rayleigh wave ellipticity measurements. Core interacting body wave phases should be observable above modeled martian noise levels, allowing us to constrain deep structure. Normal modes of Mars should also be observable and can be used to estimate the globally averaged 1D structure, while combination with results from the InSight radio science mission and orbital observations will allow for constraint of deeper structure

Customer satisfaction at Wisconsin's One-Stop Job Centers: development and implementation of a customer satisfaction instrument

Includes bibliographical references

Stability of Nanolipoprotein Particles (NLPs) at Different Temperatures and Complex Media

Nanolipoprotein particles (NLPs) have the potential to be versatile platforms for many different kinds of medical applications, from cancer therapeutics to vaccine delivery. These particles can be very useful in medicine because they are composed of molecules that are naturally made by the body and therefore have a lower potential of having harmful side effects. However, there are many questions that are unknown about the function of NLPs within an organism. How long can NLPs circulate within the blood? How stable are NLPs within the blood? At what point do NLPs degrade and is this enough time for them to reach its destination in the body? This particular project tests the stability of NLPs in biological media to understand how NLPs may behave within the blood. Understanding its stability in blood can be useful in cancer therapeutic applications because NLPs can be targeted specifically to cancer cells and deliver drugs directly to the tumor if they are able to degrade only after reaching the target tissue