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Abstract A constrained multivariate linear model is a multivariate linear model with the columns of its coefficient matrix constrained to lie in a known subspace. This class of models includes those typically used to study growth curves and longitudinal data. Envelope methods have been proposed to improve the estimation efficiency in unconstrained multivariate linear models, but have not yet been developed for constrained models. We pursue that development in this article. We first compare the standard envelope estimator with the standard estimator arising from a constrained multivariate model in terms of bias and efficiency. To further improve efficiency, we propose a novel envelope estimator based on a constrained multivariate model. We show the advantage of our proposals by simulations and by studying the probiotic capacity to reduced Salmonella infection. 

Developers use logs to diagnose performance problems in distributed applications. But, it is difficult to know a priori where logs are needed and what information in them is needed to help diagnose problems that may occur in the future. We summarize our work on the Variance-driven Automated Instrumentation Framework (VAIF), which runs alongside distributed applications. In response to newly-observed performance problems, VAIF automatically searches the space of possible instrumentation choices to enable the logs needed to help diagnose them. To work, VAIF combines distributed tracing (an enhanced form of logging) with insights about how response-time variance can be decomposed on the criticalpath portions of requests' traces. 

Developers use logs to diagnose performance problems in distributed applications. However, it is difficult to know a priori where logs are needed and what information in them is needed to help diagnose problems that may occur in the future. We present the Variance-driven Automated Instrumentation Framework (VAIF), which runs alongside distributed applica- tions. In response to newly-observed performance problems, VAIF automatically searches the space of possible instrumen- tation choices to enable the logs needed to help diagnose them. To work, VAIF combines distributed tracing (an enhanced form of logging) with insights about how response-time variance can be decomposed on the critical-path portions of requests’ traces. We evaluate VAIF by using it to localize performance problems in OpenStack and HDFS. We show that VAIF can localize problems related to slow code paths, resource contention, and problematic third-party code while enabling only 3-34% of the total tracing instrumentation. 

Abstract Candidate bacterial phylum Omnitrophota has not been isolated and is poorly understood. We analysed 72 newly sequenced and 349 existing Omnitrophota genomes representing 6 classes and 276 species, along with Earth Microbiome Project data to evaluate habitat, metabolic traits and lifestyles. We applied fluorescence-activated cell sorting and differential size filtration, and showed that most Omnitrophota are ultra-small (~0.2 μm) cells that are found in water, sediments and soils. Omnitrophota genomes in 6 classes are reduced, but maintain major biosynthetic and energy conservation pathways, including acetogenesis (with or without the Wood-Ljungdahl pathway) and diverse respirations. At least 64% of Omnitrophota genomes encode gene clusters typical of bacterial symbionts, suggesting host-associated lifestyles. We repurposed quantitative stable-isotope probing data from soils dominated by andesite, basalt or granite weathering and identified 3 families with high isotope uptake consistent with obligate bacterial predators. We propose that most Omnitrophota inhabit various ecosystems as predators or parasites. 

Abstract Although the phylum Chloroflexota is ubiquitous, its biology and evolution are poorly understood due to limited cultivability. Here, we isolated two motile, thermophilic bacteria from hot spring sediments belonging to the genus Tepidiforma and class Dehalococcoidia within the phylum Chloroflexota. A combination of cryo-electron tomography, exometabolomics, and cultivation experiments using stable isotopes of carbon revealed three unusual traits: flagellar motility, a peptidoglycan-containing cell envelope, and heterotrophic activity on aromatics and plant-associated compounds. Outside of this genus, flagellar motility has not been observed in Chloroflexota, and peptidoglycan-containing cell envelopes have not been described in Dehalococcoidia. Although these traits are unusual among cultivated Chloroflexota and Dehalococcoidia, ancestral character state reconstructions showed flagellar motility and peptidoglycan-containing cell envelopes were ancestral within the Dehalococcoidia, and subsequently lost prior to a major adaptive radiation of Dehalococcoidia into marine environments. However, despite the predominantly vertical evolutionary histories of flagellar motility and peptidoglycan biosynthesis, the evolution of enzymes for degradation of aromatics and plant-associated compounds was predominantly horizontal and complex. Together, the presence of these unusual traits in Dehalococcoidia and their evolutionary histories raise new questions about the timing and selective forces driving their successful niche expansion into global oceans. 

When multiple measures are collected repeatedly over time, redundancy typically exists among responses. The envelope method was recently proposed to reduce the dimension of responses without loss of information in regression with multivariate responses. It can gain substantial efficiency over the standard least squares estimator. In this paper, we generalize the envelope method to mixed effects models for longitudinal data with possibly unbalanced design and time‐varying predictors. We show that our model provides more efficient estimators than the standard estimators in mixed effects models. Improved accuracy and efficiency of the proposed method over the standard mixed effects model estimator are observed in both the simulations and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study.