How much information is needed to infer reticulate evolutionary histories?

Phylogenetic networks are a generalization of evolutionary trees and are an important tool for analyzing reticulate evolutionary histories. Recently, there has been great interest in developing new methods to construct rooted phylogenetic networks, that is, networks whose internal vertices correspond to hypothetical ancestors, whose leaves correspond to sampled taxa, and in which vertices with more than one parent correspond to taxa formed by reticulate evolutionary events such as recombination or hybridization. Several methods for constructing evolutionary trees use the strategy of building up a tree from simpler building blocks (such as triplets or clusters), and so it is natural to look for ways to construct networks from smaller networks. In this article, we shall demonstrate a fundamental issue with this approach. Namely, we show that even if we are given all of the subnetworks induced on all proper subsets of the leaves of some rooted phylogenetic network, we still do not have all of the information required to completely determine that network. This implies that even if all of the building blocks for some reticulate evolutionary history were to be taken as the input for any given network building method, the method might still output an incorrect history. We also discuss some potential consequences of this result for constructing phylogenetic networks

Reconstructing phylogenetic level-1 networks from nondense binet and trinet sets

Binets and trinets are phylogenetic networks with two and three leaves, respectively. Here we consider the problem of deciding if there exists a binary level-1 phylogenetic network displaying a given set T of binary binets or trinets over a taxon set X, and constructing such a network whenever it exists. We show that this is NP-hard for trinets but polynomial-time solvable for binets. Moreover, we show that the problem is still polynomial-time solvable for inputs consisting of binets and trinets as long as the cycles in the trinets have size three. Finally, we present an O(3^{|X|} poly(|X|)) time algorithm for general sets of binets and trinets. The latter two algorithms generalise to instances containing level-1 networks with arbitrarily many leaves, and thus provide some of the first supernetwork algorithms for computing networks from a set of rooted 1 phylogenetic networks

Volumetric breast density affects performance of digital screening mammography

PURPOSE: To determine to what extent automatically measured volumetric mammographic density influences screening performance when using digital mammography (DM). METHODS: We collected a consecutive series of 111,898 DM examinations (2003-2011) from one screening unit of the Dutch biennial screening program (age 50-75 years). Volumetric mammographic density was automatically assessed using Volpara. We determined screening performance measures for four density categories comparable to the American College of Radiology (ACR) breast density categories. RESULTS: Of all the examinations, 21.6% were categorized as density category 1 ('almost entirely fatty') and 41.5, 28.9, and 8.0% as category 2-4 ('extremely dense'), respectively. We identified 667 screen-detected and 234 interval cancers. Interval cancer rates were 0.7, 1.9, 2.9, and 4.4‰ and false positive rates were 11.2, 15.1, 18.2, and 23.8‰ for categories 1-4, respectively (both p-trend < 0.001). The screening sensitivity, calculated as the proportion of screen-detected among the total of screen-detected and interval tumors, was lower in higher density categories: 85.7, 77.6, 69.5, and 61.0% for categories 1-4, respectively (p-trend < 0.001). CONCLUSIONS: Volumetric mammographic density, automatically measured on digital mammograms, impacts screening performance measures along the same patterns as established with ACR breast density categories. Since measuring breast density fully automatically has much higher reproducibility than visual assessment, this automatic method could help with implementing density-based supplemental screening

A Note on Encodings of Phylogenetic Networks of Bounded Level

Driven by the need for better models that allow one to shed light into the question how life's diversity has evolved, phylogenetic networks have now joined phylogenetic trees in the center of phylogenetics research. Like phylogenetic trees, such networks canonically induce collections of phylogenetic trees, clusters, and triplets, respectively. Thus it is not surprising that many network approaches aim to reconstruct a phylogenetic network from such collections. Related to the well-studied perfect phylogeny problem, the following question is of fundamental importance in this context: When does one of the above collections encode (i.e. uniquely describe) the network that induces it? In this note, we present a complete answer to this question for the special case of a level-1 (phylogenetic) network by characterizing those level-1 networks for which an encoding in terms of one (or equivalently all) of the above collections exists. Given that this type of network forms the first layer of the rich hierarchy of level-k networks, k a non-negative integer, it is natural to wonder whether our arguments could be extended to members of that hierarchy for higher values for k. By giving examples, we show that this is not the case

A purine metabolic checkpoint that prevents autoimmunity and autoinflammation.

Still's disease, the paradigm of autoinflammation-cum-autoimmunity, predisposes for a cytokine storm with excessive T lymphocyte activation upon viral infection. Loss of function of the purine nucleoside enzyme FAMIN is the sole known cause for monogenic Still's disease. Here we discovered that a FAMIN-enabled purine metabolon in dendritic cells (DCs) restrains CD4+ and CD8+ T cell priming. DCs with absent FAMIN activity prime for enhanced antigen-specific cytotoxicity, IFNγ secretion, and T cell expansion, resulting in excessive influenza A virus-specific responses. Enhanced priming is already manifest with hypomorphic FAMIN-I254V, for which ∼6% of mankind is homozygous. FAMIN controls membrane trafficking and restrains antigen presentation in an NADH/NAD+-dependent manner by balancing flux through adenine-guanine nucleotide interconversion cycles. FAMIN additionally converts hypoxanthine into inosine, which DCs release to dampen T cell activation. Compromised FAMIN consequently enhances immunosurveillance of syngeneic tumors. FAMIN is a biochemical checkpoint that protects against excessive antiviral T cell responses, autoimmunity, and autoinflammation

Illuminating hydrological processes at the soil-vegetation-atmosphere interface with water stable isotopes

Funded by DFG research project “From Catchments as Organised Systems to Models based on Functional Units” (FOR 1Peer reviewedPublisher PDFPublisher PD

NOD2 regulates hematopoietic cell function during graft-versus-host disease

Nucleotide-binding oligomerization domain 2 (NOD2) polymorphisms are independent risk factors for Crohn's disease and graft-versus-host disease (GVHD). In Crohn's disease, the proinflammatory state resulting from NOD2 mutations have been associated with a loss of antibacterial function of enterocytes such as paneth cells. NOD2 has not been studied in experimental allogeneic bone marrow transplantation (allo-BMT). Using chimeric recipients with NOD2−/− hematopoietic cells, we demonstrate that NOD2 deficiency in host hematopoietic cells exacerbates GVHD. We found that proliferation and activation of donor T cells was enhanced in NOD-deficient allo-BMT recipients, suggesting that NOD2 plays a role in the regulation of host antigen-presenting cells (APCs). Next, we used bone marrow chimeras in an experimental colitis model and observed again that NOD2 deficiency in the hematopoietic cells results in increased intestinal inflammation. We conclude that NOD2 regulates the development of GVHD through its inhibitory effect on host APC function

Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification.

Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification

Testing Laser-Structured Antimicrobial Surfaces Under Space Conditions: The Design of the ISS Experiment BIOFILMS

Maintaining crew health and safety are essential goals for long-term human missions to space. Attaining these goals requires the development of methods and materials for sustaining the crew’s health and safety. Paramount is microbiological monitoring and contamination reduction. Microbial biofilms are of special concern, because they can cause damage to spaceflight equipment and are difficult to eliminate due to their increased resistance to antibiotics and disinfectants. The introduction of antimicrobial surfaces for medical, pharmaceutical and industrial purposes has shown a unique potential for reducing and preventing biofilm formation. This article describes the development process of ESA’s BIOFILMS experiment, that will evaluate biofilm formation on various antimicrobial surfaces under spaceflight conditions. These surfaces will be composed of different metals with and without specified surface texture modifications. Staphylococcus capitis subsp. capitis, Cupriavidus metallidurans and Acinetobacter radioresistens are biofilm forming organisms that have been chosen as model organisms. The BIOFILMS experiment will study the biofilm formation potential of these organisms in microgravity on the International Space Station on inert surfaces (stainless steel AISI 304) as well as antimicrobial active copper (Cu) based metals that have undergone specific surface modification by Ultrashort Pulsed Direct Laser Interference Patterning (USP-DLIP). Data collected in 1 x g has shown that these surface modifications enhance the antimicrobial activity of Cu based metals. In the scope of this, the interaction between the surfaces and bacteria, which is highly determined by topography and surface chemistry, will be investigated. The data generated will be indispensable for the future selection of antimicrobial materials in support of human- and robotic-associated activities in space exploration