Influence of Mortar Rheology on Aggregate Settlement

The influence of the rheology of fresh concrete on the settlement of aggregate is examined. Fresh concrete exhibits a yield stress that, under certain conditions, prevents the settlement of coarse aggregate, although its density is larger than that of the suspending mortar. Calculations, based on estimates of the yield stress obtained from slump tests, predict that aggregate normally used in concrete should not sink. To test this prediction, the settlement of a stone in fresh mortar is monitored. The stone does not sink in the undisturbed mortar (which has a high yield stress), but sinks when the mortar is vibrated, presumably due to a large reduction in its yield stress. This implies that during placement of concrete, the aggregate settles only while the concrete is being vibrated. A unique experimental method for measuring aggregate settlement is also introduced and demonstrated

Controlling colloidal sedimentation using time dependent shear

Employing a recently developed dynamical density functional theory we study the response of a colloidal sediment above a wall to shear, demonstrating the time dependent changes of the density distribution and its center-of-mass after switching shear either on or off and under oscillatory shear. Following the onset of steady shear we identify two dynamical mechanisms, distinguished by their timescales. Shortly after the onset, a transient enhancement of the packing structure at the wall reflects the self-organization into lanes. On a much longer timescale these effects are transmitted to the bulk, leading to migration away from the wall and an increase in the center-of-mass. Under oscillatory shear flow the center-of-mass enters a stationary state, reminiscent of a driven damped oscillator.Comment: 6 pages, 4 figure

Thixotropy in macroscopic suspensions of spheres

An experimental study of the viscosity of a macroscopic suspension, i.e. a suspension for which Brownian motion can be neglected, under steady shear is presented. The suspension is prepared with a high packing fraction and is density-matched in a Newtonian carrier fluid. The viscosity of the suspension depends on the shear rate and the time of shearing. It is shown for the first time that a macroscopic suspension shows thixotropic viscosity, i.e. shear-thinning with a long relaxation time as a unique function of shear. The relaxation times show a systematic decrease with increasing shear rate. These relaxation times are larger when decreasing the shear rates, compared to those observed after increasing the shear. The time scales involved are about 10000 times larger than the viscous time scale and about 1000 times smaller than the thermodynamic time scale. The structure of the suspension at the outer cylinder of a viscometer is monitored with a camera, showing the formation of a hexagonal structure. The temporal decrease of the viscosity under shear coincides with the formation of this hexagonal pattern

Models of Somatic Hypermutation Targeting and Substitution Based on Synonymous Mutations from High-Throughput Immunoglobulin Sequencing Data

Analyses of somatic hypermutation (SHM) patterns in B cell immunoglobulin (Ig) sequences contribute to our basic understanding of adaptive immunity, and have broad applications not only for understanding the immune response to pathogens, but also to determining the role of SHM in autoimmunity and B cell cancers. Although stochastic, SHM displays intrinsic biases that can confound statistical analysis, especially when combined with the particular codon usage and base composition in Ig sequences. Analysis of B cell clonal expansion, diversification, and selection processes thus critically depends on an accurate background model for SHM micro-sequence targeting (i.e., hot/cold-spots) and nucleotide substitution. Existing models are based on small numbers of sequences/mutations, in part because they depend on data from non-coding regions or non-functional sequences to remove the confounding influences of selection. Here, we combine high-throughput Ig sequencing with new computational analysis methods to produce improved models of SHM targeting and substitution that are based only on synonymous mutations, and are thus independent of selection. The resulting “S5F” models are based on 806,860 Synonymous mutations in 5-mer motifs from 1,145,182 Functional sequences and account for dependencies on the adjacent four nucleotides (two bases upstream and downstream of the mutation). The estimated profiles can explain almost half of the variance in observed mutation patterns, and clearly show that both mutation targeting and substitution are significantly influenced by neighboring bases. While mutability and substitution profiles were highly conserved across individuals, the variability across motifs was found to be much larger than previously estimated. The model and method source code are made available at http://clip.med.yale.edu/SH

Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity in order to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic and (iv) machine learning methods applied to dissect, quantify and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.Comment: 27 pages, 2 figure

Identification of Subject-Specific Immunoglobulin Alleles From Expressed Repertoire Sequencing Data

The adaptive immune receptor repertoire (AIRR) contains information on an individuals' immune past, present and potential in the form of the evolving sequences that encode the B cell receptor (BCR) repertoire. AIRR sequencing (AIRR-seq) studies rely on databases of known BCR germline variable (V), diversity (D), and joining (J) genes to detect somatic mutations in AIRR-seq data via comparison to the best-aligning database alleles. However, it has been shown that these databases are far from complete, leading to systematic misidentification of mutated positions in subsets of sample sequences. We previously presented TIgGER, a computational method to identify subject-specific V gene genotypes, including the presence of novel V gene alleles, directly from AIRR-seq data. However, the original algorithm was unable to detect alleles that differed by more than 5 single nucleotide polymorphisms (SNPs) from a database allele. Here we present and apply an improved version of the TIgGER algorithm which can detect alleles that differ by any number of SNPs from the nearest database allele, and can construct subject-specific genotypes with minimal prior information. TIgGER predictions are validated both computationally (using a leave-one-out strategy) and experimentally (using genomic sequencing), resulting in the addition of three new immunoglobulin heavy chain V (IGHV) gene alleles to the IMGT repertoire. Finally, we develop a Bayesian strategy to provide a confidence estimate associated with genotype calls. All together, these methods allow for much higher accuracy in germline allele assignment, an essential step in AIRR-seq studies

sumrep: a summary statistic framework for immune receptor repertoire comparison and model validation

In studying the binding of host antibodies to the surface antigens of pathogens, the structural and functional characterization of antibody–antigen complexes by X-ray crystallography and binding assay is important. However, the characterization requires experiments that are typically time consuming and expensive: thus, many antibody–antigen complexes are under-characterized. For vaccine development and disease surveillance, it is often vital to assess the impact of amino acid substitutions on antibody binding. For example, are there antibody substitutions capable of improving binding without a loss of breadth, or antigen substitutions that lead to antigenic escape? The questions cannot be answered reliably from sequence variation alone, exhaustive substitution assays are usually impractical, and alanine scans provide at best an incomplete identification of the critical residue–residue interactions. Here, we show that, given an initial structure of an antibody bound to an antigen, molecular dynamics simulations using the energy method molecular mechanics with Generalized Born surface area (MM/GBSA) can model the impact of single amino acid substitutions on antibody–antigen binding energy. We apply the technique to three broad-spectrum antibodies to influenza A hemagglutinin and examine both previously characterized and novel variant strains observed in the human population that may give rise to antigenic escape. We find that in some cases the impact of a substitution is local, while in others it causes a reorientation of the antibody with wide-ranging impact on residue–residue interactions: this explains, in part, why the change in chemical properties of a residue can be, on its own, a poor predictor of overall change in binding energy. Our estimates are in good agreement with experimental results—indeed, they approximate the degree of agreement between different experimental techniques. Simulations were performed on commodity computer hardware; hence, this approach has the potential to be widely adopted by those undertaking infectious disease research. Novel aspects of this research include the application of MM/GBSA to investigate binding between broadly binding antibodies and a viral glycoprotein; the development of an approach for visualizing substrate–ligand interactions; and the use of experimental assay data to rescale our predictions, allowing us to make inferences about absolute, as well as relative, changes in binding energy

Common Traits of Unicorn Companies in Latin America

The amount of companies reaching a $1B valuations, unicorn status, has been growing constantly for the last few years. This has been a global tendency. In this research, we aimed to analyze high-growth companies from the Latin America region in search of common traits. We evaluate 11 privately held companies backed by venture capital firms with valuations of$1B or more, from the Latin America region. By creating an overview of the market, the company history, founders‘ history, business model, and financing, between other aspects, we compared the findings and establish common traits found in our sample group. We have found similarities in founders‘ profiles, market conditions, and financing tendencies. From an entrepreneurial perspective, this study has created a better description of the main characteristics of high-growth companies in Latin America to provide entrepreneurs a better understanding of each company‘s conditions, that in order might represent the entrepreneurial environment in Latin America.The amount of companies reaching a $1B valuations, unicorn status, has been growing constantly for the last few years. This has been a global tendency. In this research, we aimed to analyze high-growth companies from the Latin America region in search of common traits. We evaluate 11 privately held companies backed by venture capital firms with valuations of$1B or more, from the Latin America region. By creating an overview of the market, the company history, founders‘ history, business model, and financing, between other aspects, we compared the findings and establish common traits found in our sample group. We have found similarities in founders‘ profiles, market conditions, and financing tendencies. From an entrepreneurial perspective, this study has created a better description of the main characteristics of high-growth companies in Latin America to provide entrepreneurs a better understanding of each company‘s conditions, that in order might represent the entrepreneurial environment in Latin America