New early devonian charophyta from Gondwana

Early Devonian charophytes are reported from Australia (Buchan, Victoria) and Europe (Landeyran, southern France): Moellerina australica n. sp. Feist and Pinnoputamen occitanicum n. sp. Feist. Sedimentological data and associated faunas from these localities accord with both species having inhabited lacustrine or estuarine environments. A critical review of Devonian biotopes confirms that, as with present day species, Mid Palaeozoic charophytes could not have lived in open marine habitats. Originating in Baltica during the Silurian, charophytes appeared in Gondwana in the earliest Devonian.<br /

Erosion Rates of Wood During Natural Weathering. Part II. Earlywood and Latewood Erosion Rates

This is the second in a series of reports on the erosion rates of wood exposed outdoors near Madison, Wisconsin. In the work reported here, the erosion rates of earlywood and latewood were determined for smooth-planed vertical-grained lumber for an exposure period of 14 years. The specimens were oriented vertically, facing south; erosion was measured annually for the first 6 years and biannually the remainder of the exposure. Wood species were ponderosa pine, lodgepole pine, Engelmann spruce, western hemlock, and red alder. Large differences were observed between earlywood and latewood erosion rates during weathering. Erosion rates varied from 33 μm/year for lodgepole pine latewood to 58 μm/year for western hemlock and red alder earlywood. In general, no practical differences in erosion were observed for different orientations of the specimens on the test fence (vertical or horizontal longitudinal axis). Some specimens showed considerable decay after 10 years of exposure

Targeted Greybox Fuzzing with Static Lookahead Analysis

Automatic test generation typically aims to generate inputs that explore new paths in the program under test in order to find bugs. Existing work has, therefore, focused on guiding the exploration toward program parts that are more likely to contain bugs by using an offline static analysis. In this paper, we introduce a novel technique for targeted greybox fuzzing using an online static analysis that guides the fuzzer toward a set of target locations, for instance, located in recently modified parts of the program. This is achieved by first semantically analyzing each program path that is explored by an input in the fuzzer's test suite. The results of this analysis are then used to control the fuzzer's specialized power schedule, which determines how often to fuzz inputs from the test suite. We implemented our technique by extending a state-of-the-art, industrial fuzzer for Ethereum smart contracts and evaluate its effectiveness on 27 real-world benchmarks. Using an online analysis is particularly suitable for the domain of smart contracts since it does not require any code instrumentation---instrumentation to contracts changes their semantics. Our experiments show that targeted fuzzing significantly outperforms standard greybox fuzzing for reaching 83% of the challenging target locations (up to 14x of median speed-up)

The impact of regulations on overheating risk in dwellings

Many new and emerging regulations and standards for buildings focus on climate change mitigation through energy and carbon reduction. In cool climates, such reductions are achieved by optimising the building for heat retention. It is increasingly recognised however that some degree of climate change is now inevitable and new and existing buildings need to consider this to ensure resilience and an ability to adapt over time. In this context the current approach to regulation which largely remains focused on the ‘point of handover’ may not be fit for purpose. This paper focuses on a ‘typical’ dwelling designed to a range of standards, representing current or emerging approaches to minimising energy use, using a range of construction methods, where a number of adaptations are available to occupants. It considers, through the use of building performance simulation, how each configuration is likely to perform thermally over time given current climate change predictions. The paper demonstrates that the current approach to assessing overheating risk in dwellings, coupled with the regulatory focus on reducing energy consumption, could result in significant levels of overheating. This overheating could, in the near future, present a risk to health and result in the need for significant interventions

Harvey: A Greybox Fuzzer for Smart Contracts

We present Harvey, an industrial greybox fuzzer for smart contracts, which are programs managing accounts on a blockchain. Greybox fuzzing is a lightweight test-generation approach that effectively detects bugs and security vulnerabilities. However, greybox fuzzers randomly mutate program inputs to exercise new paths; this makes it challenging to cover code that is guarded by narrow checks, which are satisfied by no more than a few input values. Moreover, most real-world smart contracts transition through many different states during their lifetime, e.g., for every bid in an auction. To explore these states and thereby detect deep vulnerabilities, a greybox fuzzer would need to generate sequences of contract transactions, e.g., by creating bids from multiple users, while at the same time keeping the search space and test suite tractable. In this experience paper, we explain how Harvey alleviates both challenges with two key fuzzing techniques and distill the main lessons learned. First, Harvey extends standard greybox fuzzing with a method for predicting new inputs that are more likely to cover new paths or reveal vulnerabilities in smart contracts. Second, it fuzzes transaction sequences in a targeted and demand-driven way. We have evaluated our approach on 27 real-world contracts. Our experiments show that the underlying techniques significantly increase Harvey's effectiveness in achieving high coverage and detecting vulnerabilities, in most cases orders-of-magnitude faster; they also reveal new insights about contract code.Comment: arXiv admin note: substantial text overlap with arXiv:1807.0787

Potentiating antibacterial activity by predictably enhancing endogenous microbial ROS production

The ever-increasing incidence of antibiotic-resistant infections combined with a weak pipeline of new antibiotics has created a global public health crisis1. Accordingly, novel strategies for enhancing our antibiotic arsenal are needed. As antibiotics kill bacteria in part by inducing reactive oxygen species (ROS)2–4, we reasoned that targeting microbial ROS production might potentiate antibiotic activity. Here we show that ROS production can be predictably enhanced in Escherichia coli, increasing the bacteria’s susceptibility to oxidative attack. We developed an ensemble, genome-scale metabolic modeling approach capable of predicting ROS production in E. coli. The metabolic network was systematically perturbed and its flux distribution analyzed to identify targets predicted to increase ROS production. In silico–predicted targets were experimentally validated and shown to confer increased susceptibility to oxidants. Validated targets also increased susceptibility to killing by antibiotics. This work establishes a systems-based method to tune ROS production in bacteria and demonstrates that increased microbial ROS production can potentiate killing by oxidants and antibiotics

Randomizing genome-scale metabolic networks

Networks coming from protein-protein interactions, transcriptional regulation, signaling, or metabolism may appear to have "unusual" properties. To quantify this, it is appropriate to randomize the network and test the hypothesis that the network is not statistically different from expected in a motivated ensemble. However, when dealing with metabolic networks, the randomization of the network using edge exchange generates fictitious reactions that are biochemically meaningless. Here we provide several natural ensembles of randomized metabolic networks. A first constraint is to use valid biochemical reactions. Further constraints correspond to imposing appropriate functional constraints. We explain how to perform these randomizations with the help of Markov Chain Monte Carlo (MCMC) and show that they allow one to approach the properties of biological metabolic networks. The implication of the present work is that the observed global structural properties of real metabolic networks are likely to be the consequence of simple biochemical and functional constraints.Comment: 30 Pages, 6 Main Figures, 6 Supplementary Figures, 1 Supplementary Tabl