Hydraulic anatomy affects genotypic variation in plant water use and shows differential organ specific plasticity to drought in Sorghum bicolor

Genotypic variability and plasticity in hydraulic anatomy are not well-understood in herbaceous monocots. In this study, we used Sorghum bicolor, a monocotyledonous, tropical grass model, to understand whether differential plant water use is associated with xylem anatomy and if whole-plant xylem anatomy responds to water stress, justifying differential genotypic sensitivity to drought. In a greenhouse environment, we studied four sorghum genotypes that are known to genetically differ in growth and exhibit differential sensitivity to drought. Under well-watered scenario, transpiration variability and plant growth traits correlated with xylem anatomical traits at both the leaf and stem level, including xylem area and predicted xylem-specific hydraulic conductivity. High water use genotypes had inherently higher hydraulic capacity, but under drought, their transpiration declined at higher fractions of transpirable soil water (FTSW) and they showed greater plasticity in hydraulic anatomy. However, lower FTSW thresholds and modest anatomical changes were identified in the low water use genotypes with inherently lower hydraulic conductivity. Drought, induced modular phenotypic plasticity in hydraulic anatomy, whereby plasticity in leaf xylem traits was remarkably higher than stem xylem, while root xylem showed a reverse nature of vascular modification. Xylem traits were in agreement with phloem anatomy, irrespective of water regime. Our study indicates that hydraulic anatomy can be critical for herbaceous monocots in determining limits to plant water use and genotypic response to drought with implications on whole-plant functions and habitat ecology

О нижней оценке для одной квадратичной задачи намногообразии Штифеля

Despite technological advances in metabolomics, large parts of the human metabolome are still unexplored. In an untargeted metabolomics screen aiming to identify substrates of the orphan transporter ATP-binding cassette subfamily C member 5 (ABCC5), we identified a class of mammalian metabolites, N-lactoyl-amino acids. Using parallel protein fractionation in conjunction with shotgun proteomics on fractions containing N-lactoyl-Phe-forming activity, we unexpectedly found that a protease, cytosolic nonspecific dipeptidase 2 (CNDP2), catalyzes their formation. N-lactoyl-amino acids are ubiquitous pseudodipeptides of lactic acid and amino acids that are rapidly formed by reverse proteolysis, a process previously considered to be negligible in vivo. The plasma levels of these metabolites strongly correlate with plasma levels of lactate and amino acid, as shown by increased levels after physical exercise and in patients with phenylketonuria who suffer from elevated Phe levels. Our approach to identify unknown metabolites and their biosynthesis has general applicability in the further exploration of the human metabolome

International evaluation of an AI system for breast cancer screening.

Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful1. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives2. Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.Professor Fiona Gilbert receives funding from the National Institute for Health Research (Senior Investigator award)

Statistical physics of vaccination

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination–one of the most important preventive measures of modern times–is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research

On the generality of stability–complexity relationships in Lotka–Volterra ecosystems

Understanding how complexity persists in nature is a long-standing goal of ecologists. In theoretical ecology, local stability is a widely used measure of ecosystem persistence and has made a major contribution to the ecosystem stability-complexity debate over the last few decades. However, permanence is coming to be regarded as a more satisfactory definition of ecosystem persistence and has relatively recently become available as a tool for assessing the global stability of Lotka-Volterra communities. Here we document positive relationships between permanence and Lotka-Volterra food web complexity and report a positive correlation between the probability of local stability and permanence. We investigate further the frequency of discrepancy (attributed to fragile systems that are locally stable but not permanent or locally unstable systems that are permanent and have cyclic or chaotic dynamics), associate non-permanence with the local stability or instability of equilibria on the boundary of the state-space, and investigate how these vary with aspects of ecosystem complexity. We find that locally stable interior equilibria tend to have all locally unstable boundary equilibria. Since a locally stable boundary is inconsistent with permanent dynamics, this can explain the observed positive correlation between local interior stability and permanence. Our key finding is that, at least in Lotka-Volterra model ecosystems, local stability may be a better measure of persistence than previously though

ABCC6-mediated ATP secretion by the liver is the main source of the mineralization inhibitor inorganic pyrophosphate in the systemic circulation-brief report

Mutations in ABCC6 underlie the ectopic mineralization disorder pseudoxanthoma elasticum (PXE) and some forms of generalized arterial calcification of infancy, both of which affect the cardiovascular system. Using cultured cells, we recently showed that ATP-binding cassette subfamily C member 6 (ABCC6) mediates the cellular release of ATP, which is extracellularly rapidly converted into AMP and the mineralization inhibitor inorganic pyrophosphate (PPi). The current study was performed to determine which tissues release ATP in an ABCC6-dependent manner in vivo, where released ATP is converted into AMP and PPi, and whether human PXE ptients have low plasma PPi concentrations. Using cultured primary hepatocytes and in vivo liver perfusion experiments, we found that ABCC6 mediates the direct, sinusoidal, release of ATP from the liver. Outside hepatocytes, but still within the liver vasculature, released ATP is converted into AMP and PPi. The absence of functional ABCC6 in patients with PXE leads to strongly reduced plasma PPi concentrations. Hepatic ABCC6-mediated ATP release is the main source of circulating PPi, revealing an unanticipated role of the liver in systemic PPi homeostasis. Patients with PXE have a strongly reduced plasma PPi level, explaining their mineralization disorder. Our results indicate that systemic PPi is relatively stable and that PXE, generalized arterial calcification of infancy, and other ectopic mineralization disorders could be treated with PPi supplementation therap