Middleware’s message : the financial technics of codata

In this paper, I will argue for the relevance of certain distinctive features of messaging systems, namely those in which data (a) can be sent and received asynchronously, (b) can be sent to multiple simultaneous recipients and (c) is received as a “potentially infinite” flow of unpredictable events. I will describe the social technology of the stock ticker, a telegraphic device introduced at the New York Stock Exchange in the 1860s, with reference to early twentieth century philosophers of synchronous experience (Bergson), simultaneous sign interpretations (Mead and Peirce), and flows of discrete events (Bachelard). Then, I will show how the ticker’s data flows developed into the 1990s-era technologies of message queues and message brokers, which distinguished themselves through their asynchronous implementation of ticker-like message feeds sent between otherwise incompatible computers and terminals. These latter systems’ characteristic “publish/subscribe” communication pattern was one in which conceptually centralized (if logically distributed) flows of messages would be “published,” and for which “subscribers” would be spontaneously notified when events of interest occurred. This paradigm—common to the so-called “message-oriented middleware” systems of the late 1990s—would re-emerge in different asynchronous distributed system contexts over the following decades, from “push media” to Twitter to the Internet of Things

Genome resolved analysis of a premature infant gut microbial community reveals a Varibaculum cambriense genome and a shift towards fermentation-based metabolism during the third week of life.

BACKGROUND: The premature infant gut has low individual but high inter-individual microbial diversity compared with adults. Based on prior 16S rRNA gene surveys, many species from this environment are expected to be similar to those previously detected in the human microbiota. However, the level of genomic novelty and metabolic variation of strains found in the infant gut remains relatively unexplored. RESULTS: To study the stability and function of early microbial colonizers of the premature infant gut, nine stool samples were taken during the third week of life of a premature male infant delivered via Caesarean section. Metagenomic sequences were assembled and binned into near-complete and partial genomes, enabling strain-level genomic analysis of the microbial community.We reconstructed eleven near-complete and six partial bacterial genomes representative of the key members of the microbial community. Twelve of these genomes share >90% putative ortholog amino acid identity with reference genomes. Manual curation of the assembly of one particularly novel genome resulted in the first essentially complete genome sequence (in three pieces, the order of which could not be determined due to a repeat) for Varibaculum cambriense (strain Dora), a medically relevant species that has been implicated in abscess formation.During the period studied, the microbial community undergoes a compositional shift, in which obligate anaerobes (fermenters) overtake Escherichia coli as the most abundant species. Other species remain stable, probably due to their ability to either respire anaerobically or grow by fermentation, and their capacity to tolerate fluctuating levels of oxygen. Metabolic predictions for V. cambriense suggest that, like other members of the microbial community, this organism is able to process various sugar substrates and make use of multiple different electron acceptors during anaerobic respiration. Genome comparisons within the family Actinomycetaceae reveal important differences related to respiratory metabolism and motility. CONCLUSIONS: Genome-based analysis provided direct insight into strain-specific potential for anaerobic respiration and yielded the first genome for the genus Varibaculum. Importantly, comparison of these de novo assembled genomes with closely related isolate genomes supported the accuracy of the metagenomic methodology. Over a one-week period, the early gut microbial community transitioned to a community with a higher representation of obligate anaerobes, emphasizing both taxonomic and metabolic instability during colonization

Clades of huge phages from across Earth's ecosystems.

Bacteriophages typically have small genomes1 and depend on their bacterial hosts for replication2. Here we sequenced DNA from diverse ecosystems and found hundreds of phage genomes with lengths of more than 200 kilobases (kb), including a genome of 735 kb, which is-to our knowledge-the largest phage genome to be described to date. Thirty-five genomes were manually curated to completion (circular and no gaps). Expanded genetic repertoires include diverse and previously undescribed CRISPR-Cas systems, transfer RNAs (tRNAs), tRNA synthetases, tRNA-modification enzymes, translation-initiation and elongation factors, and ribosomal proteins. The CRISPR-Cas systems of phages have the capacity to silence host transcription factors and translational genes, potentially as part of a larger interaction network that intercepts translation to redirect biosynthesis to phage-encoded functions. In addition, some phages may repurpose bacterial CRISPR-Cas systems to eliminate competing phages. We phylogenetically define the major clades of huge phages from human and other animal microbiomes, as well as from oceans, lakes, sediments, soils and the built environment. We conclude that the large gene inventories of huge phages reflect a conserved biological strategy, and that the phages are distributed across a broad bacterial host range and across Earth's ecosystems

Langevin diffusions on the torus: estimation and applications

We introduce stochastic models for continuous-time evolution of angles and develop their estimation. We focus on studying Langevin diffusions with stationary distributions equal to well-known distributions from directional statistics, since such diffusions can be regarded as toroidal analogues of the Ornstein–Uhlenbeck process. Their likelihood function is a product of transition densities with no analytical expression, but that can be calculated by solving the Fokker–Planck equation numerically through adequate schemes. We propose three approximate likelihoods that are computationally tractable: (i) a likelihood based on the stationary distribution; (ii) toroidal adaptations of the Euler and Shoji–Ozaki pseudo-likelihoods; (iii) a likelihood based on a specific approximation to the transition density of the wrapped normal process. A simulation study compares, in dimensions one and two, the approximate transition densities to the exact ones, and investigates the empirical performance of the approximate likelihoods. Finally, two diffusions are used to model the evolution of the backbone angles of the protein G (PDB identifier 1GB1) during a molecular dynamics simulation. The software package sdetorus implements the estimation methods and applications presented in the paper

Empirical estimation of nearshore waves from a global deep-water wave model

Global wind-wave models such as the National Oceanic and Atmospheric Administration Wave Watch 3 (NWW3) play an important role in monitoring the world’s oceans. However, untransformed data at grid points in deep water provide a poor estimate of swell characteristics at nearshore locations, which are often of significant scientific, engineering, and public interest. Explicit wave modeling, such as the Simulating Waves Nearshore (SWAN), is one method for resolving the complex wave transformations affected by bathymetry, winds, and other local factors. However, obtaining accurate bathymetry and determining parameters for such models is often difficult. When target data is available (i.e., from in situ buoys or human observers, empirical alternatives such artificial neural networks (ANNs) and linear regression may be considered for inferring nearshore conditions from offshore model output. Using a sixfold cross-validation scheme, significant wave height Hs and period were estimated at one onshore and two nearshore locations. In estimating Hs at the shoreline, the validation performance of the best ANN was r = 0.91, as compared to those of linear regression (0.82), SWAN (0.78), and the NWW3 Hs baseline (0.54)

Near-shore swell estimation from a global wind-wave model spectral process, linear, and artificial neural network models /

Estimation of swell conditions in coastal regions is important for a variety of public, government, and research applications. Driving a model of the near-shore wave transformation from an offshore global swell model such as NOAAWaveWatch3 is an economical means to arrive at swell size estimates at particular locations of interest. Recently, some work (e.g. Browne et al. [Browne, M., Strauss, D., Castelle, B., Blumenstein, M., Tomlinson, R., 2006. Local swell estimation and prediction from a global wind-wave model. IEEE Geoscience and Remote Sensing Letters 3 (4), 462–466.]) has examined an artificial neural network (ANN) based, empirical approach to wave estimation. Here, we provide a comprehensive evaluation of two data driven approaches to estimating waves near-shore (linear and ANN), and also contrast these with a more traditional spectral wave simulation model (SWAN). Performance was assessed on data gathered from a total of 17 near-shore locations, with heterogenous geography and bathymetry, around the continent of Australia over a 7 month period. It was found that the ANNs out-performed SWAN and the non-linear architecture consistently out-performed the linear method. Variability in performance and differential performance with regard to geographical location could largely be explained in terms of the underlying complexity of the local wave transformation