Genome modeling system: A knowledge management platform for genomics

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms

Construction of asynchronous communicating systems: weak termination guaranteed!

Correctness of asynchronously communicating systems (ACS) is known to be a hard problem, which became even more actual after the introduction of Service Oriented Architectures and Service Oriented Computing. In this paper, we focus on one particular correctness property, namely weak termination: at any moment of the system execution, at least one option to terminate should be available. We present a compositional method for constructing an ACS that guarantees weak termination. The method allows for refinement of single components, refinement of compositions of components and the creation of new components in the system. For two important classes of ACS, weak termination follows directly from their structure. These classes focus on the concurrency over components and on the implementation of protocols and communicating choices

Fermionic Wave Functions from Neural-Network Constrained Hidden States

We introduce a systematically improvable family of variational wave functions for the simulation of strongly correlated fermionic systems. This family consists of Slater determinants in an augmented Hilbert space involving "hidden" additional fermionic degrees of freedom. These determinants are projected onto the physical Hilbert space through a constraint which is optimized, together with the single-particle orbitals, using a neural network parametrization. This construction draws inspiration from the success of hidden particle representations but overcomes the limitations associated with the mean-field treatment of the constraint often used in this context. Our construction provides an extremely expressive family of wave functions, which is proven to be universal. We apply this construction to the ground state properties of the Hubbard model on the square lattice, achieving levels of accuracy which are competitive with and in some cases superior to state-of-the-art computational methods

Carbon nanotubes: Synthesis, structure, functionalization, and characterization

Carbon nanotubes have generated great expectations in the scientific arena, mainly due to their spectacular properties, which include a high aspect ratio, high strain resistance, and high strength, along with high conductivities. Nowadays, carbon nanotubes are produced by a variety of methods, each of them with advantages and disadvantages. Once produced, carbon nanotubes can be chemically modified, using a wide range of chemical reactions. Functionalization makes these long wires much easier to manipulate and dispersible in several solvents. In addition, the properties of carbon nanotubes can be combined with those of organic appendages. Finally, carbon nanotubes need to be carefully characterized, either as pristine or modified materials