Agreement of Air Bike and Treadmill Protocols to Assess Maximal Oxygen Uptake: An Exploratory Study.

Maximal oxygen consumption (V̇O2max) is an important measure of aerobic fitness, with applications in evaluating fitness, designing training programs, and assessing overall health. While treadmill assessments are considered the gold standard, airbikes (ABs) are increasingly popular exercise machines. However, limited research exists on AB-based V̇O2max assessments, particularly regarding agreement with treadmill graded exercise tests. To address this gap, a randomized crossover study was conducted, involving 15 healthy adults (9M, 6F, 7 familiar with AB) aged 30.1 ± 8.6 years. Paired t-tests, intraclass correlation coefficients (ICC), Bland-Altman and Principal component (PC) analyses were used to assess agreement between protocols. The results demonstrated good to excellent agreement in V̇O2max, maximum heart rate (HR), and rating of perceived exertion (ICC range: 0.89-0.92). However, significant differences were observed in several measures, including V̇O2max and maximum HR (p < 0.01). Overall a systematic bias 3.31 mL/kg/min (treadmill > AB, 95%CI[1.67,4.94]) was observed, no proportional bias was present; however, regular AB users (systematic bias: 1.27 (95%CI[0.20,2.34]) mL/kg/min) exhibited higher agreement in V̇O2max measures compared to non-regular users (systematic bias: 5.09 (95%CI[3.69,6.49]) mL/kg/min). There were no significant differences in cardiorespiratory coordination, between the AB and the treadmill. These findings suggest that for individuals familiar with the AB, it can be a suitable alternative for assessing V̇O2max compared to the treadmill. Future research with larger samples should focus on developing prediction equations for field AB tests to predict V̇O2max. Practitioners should consider using the AB to assess V̇O2max in individuals who prefer it over running.

Beyond integrative experiment design: Systematic experimentation guided by causal discovery AI.

Integrative experiment design is a needed improvement over ad hoc experiments, but the specific proposed method has limitations. We urge a further break with tradition through the use of an enormous untapped resource: Decades of causal discovery artificial intelligence (AI) literature on optimizing the design of systematic experimentation.

A new method for estimating the probability of causal relationships from observational data: Application to the study of the short-term effects of air pollution on cardiovascular and respiratory disease.

In this paper we investigate which airborne pollutants have a short-term causal effect on cardiovascular and respiratory disease using the Ancestral Probabilities (AP) procedure, a novel Bayesian approach for deriving the probabilities of causal relationships from observational data. The results are largely consistent with EPA assessments of causality, however, in a few cases AP suggests that some pollutants thought to cause cardiovascular or respiratory disease are associated due purely to confounding. The AP procedure utilizes maximal ancestral graph (MAG) models to represent and assign probabilities to causal relationships while accounting for latent confounding. The algorithm does so locally by marginalizing over models with and without causal features of interest. Before applying AP to real data, we evaluate it in a simulation study and investigate the benefits of providing background knowledge. Overall, the results suggest that AP is an effective tool for causal discovery.

Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search.

We give novel Python and R interfaces for the (Java) Tetrad project for causal modeling, search, and estimation. The Tetrad project is a mainstay in the literature, having been under consistent development for over 30 years. Some of its algorithms are now classics, like PC and FCI; others are recent developments. It is increasingly the case, however, that researchers need to access the underlying Java code from Python or R. Existing methods for doing this are inadequate. We provide new, up-to-date methods using the JPype Python-Java interface and the Reticulate Python-R interface, directly solving these issues. With the addition of some simple tools and the provision of working examples for both Python and R, using JPype and Reticulate to interface Python and R with Tetrad is straightforward and intuitive.

Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees.

Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables-for instance, recovering brain networks from fMRI data. We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm. BOSS greedily searches over permutations of variables, using GSTs to construct and score DAGs from permutations. GSTs efficiently cache scores to eliminate redundant calculations. BOSS achieves state-of-the-art performance in accuracy and execution time, comparing favorably to a variety of combinatorial and gradient-based learning algorithms under a broad range of conditions. To demonstrate its practicality, we apply BOSS to two sets of resting-state fMRI data: simulated data with pseudo-empirical noise distributions derived from randomized empirical fMRI cortical signals and clinical data from 3T fMRI scans processed into cortical parcels. BOSS is available for use within the TETRAD project which includes Python and R wrappers.

Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study.

Designing studies that apply causal discovery requires navigating many researcher degrees of freedom. This complexity is exacerbated when the study involves fMRI data. In this paper we (i) describe nine challenges that occur when applying causal discovery to fMRI data, (ii) discuss the space of decisions that need to be made, (iii) review how a recent case study made those decisions, (iv) and identify existing gaps that could potentially be solved by the development of new methods. Overall, causal discovery is a promising approach for analyzing fMRI data, and multiple successful applications have indicated that it is superior to traditional fMRI functional connectivity methods, but current causal discovery methods for fMRI leave room for improvement.

Balance between promiscuity and specificity in phage λ host range.

As hosts acquire resistance to viruses, viruses must overcome that resistance to re-establish infectivity, or go extinct. Despite the significant hurdles associated with adapting to a resistant host, viruses are evolutionarily successful and maintain stable coevolutionary relationships with their hosts. To investigate the factors underlying how pathogens adapt to their hosts, we performed a deep mutational scan of the region of the λ tail fiber tip protein that mediates contact with the receptor on λ's host, Escherichia coli. Phages harboring amino acid substitutions were subjected to selection for infectivity on wild type E. coli, revealing a highly restrictive fitness landscape, in which most substitutions completely abrogate function. A subset of positions that are tolerant of mutation in this assay, but diverse over evolutionary time, are associated with host range expansion. Imposing selection for phage infectivity on three λ-resistant hosts, each harboring a different missense mutation in the λ receptor, reveals hundreds of adaptive variants in λ. We distinguish λ variants that confer promiscuity, a general ability to overcome host resistance, from those that drive host-specific infectivity. Both processes may be important in driving adaptation to a novel host.

Distinct patterns of mutational sensitivity for λ resistance and maltodextrin transport in Escherichia coli LamB.

Bacteria can evade cohabiting phages through mutations in phage receptors, but these mutations may come at a cost if they disrupt the receptor's native cellular function. To investigate the relationship between these two conflicting activities, we generated sequence-function maps of Escherichia coli LamB with respect to sensitivity to phage λ and transport of maltodextrin. By comparing 413 missense mutations whose effect on both traits could be analysed, we find that these two phenotypes were correlated, implying that most mutations affect these phenotypes through a common mechanism such as loss of protein stability. However, individual mutations could be found that specifically disrupt λ-sensitivity without affecting maltodextrin transport. We identify and individually assay nine such mutations, whose spatial positions implicate loop L6 of LamB in λ binding. Although missense mutations that lead to λ-resistance are rare, they were approximately as likely to be maltodextrin-utilizing (Mal+) as not (Mal-), implying that E. coli can adapt to λ while conserving the receptor's native function. We propose that in order for E. coli and λ to stably cohabitate, selection for λ-resistance and maltose transport must be spatially or temporally separated.

Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types.

In recent years, great strides have been made for causal structure learning in the high-dimensional setting and in the mixed data-type setting when there are both discrete and continuous variables. However, due to the complications involved with modeling continuous-discrete variable interactions, the intersection of these two settings has been relatively understudied. The current paper explores the problem of efficiently extending causal structure learning algorithms to high-dimensional data with mixed data-types. First, we characterize a model over continuous and discrete variables. Second, we derive a degenerate Gaussian (DG) score for mixed data-types and discuss its asymptotic properties. Lastly, we demonstrate the practicality of the DG score on learning causal structures from simulated data sets.

Scoring Bayesian Networks of Mixed Variables.

In this paper we outline two novel scoring methods for learning Bayesian networks in the presence of both continuous and discrete variables, that is, mixed variables. While much work has been done in the domain of automated Bayesian network learning, few studies have investigated this task in the presence of both continuous and discrete variables while focusing on scalability. Our goal is to provide two novel and scalable scoring functions capable of handling mixed variables. The first method, the Conditional Gaussian (CG) score, provides a highly efficient option. The second method, the Mixed Variable Polynomial (MVP) score, allows for a wider range of modeled relationships, including non-linearity, but it is slower than CG. Both methods calculate log likelihood and degrees of freedom terms, which are incorporated into a Bayesian Information Criterion (BIC) score. Additionally, we introduce a structure prior for efficient learning of large networks and a simplification in scoring the discrete case which performs well empirically. While the core of this work focuses on applications in the search and score paradigm, we also show how the introduced scoring functions may be readily adapted as conditional independence tests for constraint-based Bayesian network learning algorithms. Lastly, we describe ways to simulate networks of mixed variable types and evaluate our proposed methods on such simulations.

Oral delivery of wafers made from HBsAg-expressing maize germ induces long-term immunological systemic and mucosal responses.

BACKGROUND: The hepatitis B surface antigen (HBsAg) has been administered over the last 20 years as a parenteral vaccine against the hepatitis B virus (HBV). Despite high seroconversion rates, chronic infection rates are still high worldwide. Orally delivered vaccines provide a practical alternative to injected vaccines, potentially helping poorly responding populations and providing a viable alternative for populations in remote locations. Anamnestic responses are vital to establishing the efficacy of a given vaccine and have been assessed in this study using a plant-based oral delivery platform expressing HBsAg. METHODS: Long-term immunological memory was assessed in mice injected with a primary dose of Recombivax and boosted with orally-delivered HBsAg wafers, control wafers, or parenterally-delivered commercial vaccine (Recombivax). RESULTS: Mice boosted with HBsAg orally-administered wafers displayed sharp increases in mucosal IgA titers in fecal material and steep increases in serum IgA, whereas mice boosted with Recombivax showed no detectable levels of IgA in either fecal or serum samples following four boosting treatments. Long-term memory in the orally-treated mice was evidenced by sustained fecal IgA, and serum IgA, IgG, and mIU/mL over one year, while Recombivax-treated mice displayed sustained serum IgG and mIU/mL. Furthermore, sharp increases in these same antibodies were induced after re-boosting at 47 and 50 weeks post-primary injection. CONCLUSIONS: Orally-delivered vaccines can provide long-term immune responses mucosally and systemically. For sexually-transmitted diseases that can be acquired at mucosal surfaces, such as HBV, an oral delivery platform may provide added protection over a conventional parenterally administered vaccine.

Hipk is an essential protein that promotes Notch signal transduction in the Drosophila eye by inhibition of the global co-repressor Groucho.

Homeodomain interacting protein kinase (Hipk) is a member of a novel family of serine/threonine kinases. Extensive biochemical studies of vertebrate homologs, particularly Hipk2, have identified a growing list of interactors, including proteins involved in transcriptional regulation, chromatin remodeling and essential signaling pathways such as Wnt and TGFbeta. To gain insight into the in vivo functions of the single Drosophila Hipk we characterized loss of function alleles, which revealed an essential requirement for hipk. We find that in the developing eye, hipk promotes the Notch pathway. Notch signaling acts at multiple points in eye development to promote growth, proliferation and patterning. Hipk stimulates the early function of Notch in promotion of global growth of the eye disc. It has been shown in the Drosophila eye that Hipk interferes with the repressive activity of the global co-repressor, Groucho (Gro). Here, we propose that Hipk antagonizes Gro to promote the transmission of the Notch signal, indicating that Hipk plays numerous roles in regulating gene expression through interference with the formation of Gro-containing co-repressor complexes.

Characterization of Dir: a putative potassium inward rectifying channel in Drosophila.

Potassium channels vary in their function and regulation, yet they maintain a number of important features - they are involved in the control of potassium flow, cell volume, cell membrane resting potential, cell excitability and hormone release. The potassium (K(+)) inward rectifier (Kir) superfamily of channels are potassium selective channels, that are sensitive to the concentration of K(+) ions. They are termed inward rectifiers since they allow a much greater K(+) influx than efflux. There are at least seven subfamilies of Kir channels, grouped according to sequence and functional similarities (Curr. Opin. Neurobiol. 5 (1995) 268; Annu. Rev. Physiol. 59 (1997) 171). While numerous Kir channels have been discovered in a variety of organisms, Drosophila inward rectifier (Dir) is the first putative inward rectifier to be studied in Drosophila. In fact, there are only three genes (including Dir) encoding putative inward rectifiers in the Drosophila genome. Though there are other known potassium channels in Drosophila such as ether-a-go-go and shaker, most are voltage-gated channels. As an important first step in characterizing Kir channels in Drosophila, we initiated studies on Dir.