Astrophysical Fluid Dynamics via Direct Statistical Simulation

In this paper we introduce the concept of Direct Statistical Simulation (DSS) for astrophysical flows. This technique may be appropriate for problems in astrophysical fluids where the instantaneous dynamics of the flows are of secondary importance to their statistical properties. We give examples of such problems including mixing and transport in planets, stars and disks. The method is described for a general set of evolution equations, before we consider the specific case of a spectral method optimised for problems on a spherical surface. The method is illustrated for the simplest non-trivial example of hydrodynamics and MHD on a rotating spherical surface. We then discuss possible extensions of the method both in terms of computational methods and the range of astrophysical problems that are of interest.Comment: 26 pages, 11 figures, added clarifying remarks and references, and corrected typos. This version is accepted for publication in The Astrophysical Journa

What's hot in conservation biogeography in a changing climate? Going beyond species range dynamics

International audienceIn recent decades Earth's rapidly changing climate, driven by anthropogenic greenhouse gas emissions, has affected species distributions and phenology, ecological communities and ecosystem processes, effects that are increasingly being observed globally (Allen et al., 2010; Doney et al., 2012; Franklin, Serra‐Diaz, Syphard, & Regan, 2016; Parmesan, 2006; Walther et al., 2002). Pleistocene shifts in species ranges during glacial–interglacial transitions reveal large‐scale biome shifts and no‐analog species assemblages (MacDonald et al., 2008; Nolan et al., 2018; Williams & Jackson, 2007); the pace of current anthropogenic warming outstrips past changes in the Earth system and climate, however, leading to new climate novelties and ecological communities (Ordonez, Williams, & Svenning, 2016). Global scientific consensus now emphasizes that global warming should be kept to 1.5°C to avoid catastrophic changes in ecosystems and the services they provide to people (IPCC, 2018), and climate change threats to biodiversity are being prioritized in international policy response (Ferrier et al., 2016)

DDoS defense by offense

This article presents the design, implementation, analysis, and experimental evaluation of speak-up, a defense against application-level distributed denial-of-service (DDoS), in which attackers cripple a server by sending legitimate-looking requests that consume computational resources (e.g., CPU cycles, disk). With speak-up, a victimized server encourages all clients, resources permitting, to automatically send higher volumes of traffic. We suppose that attackers are already using most of their upload bandwidth so cannot react to the encouragement. Good clients, however, have spare upload bandwidth so can react to the encouragement with drastically higher volumes of traffic. The intended outcome of this traffic inflation is that the good clients crowd out the bad ones, thereby capturing a much larger fraction of the server's resources than before. We experiment under various conditions and find that speak-up causes the server to spend resources on a group of clients in rough proportion to their aggregate upload bandwidths, which is the intended result.National Science Foundation (U.S.) (NSF grant CNS-0225660)National Science Foundation (U.S.) (NSF grant CNS-0520241)United States. Dept. of Defense (National Security Science and Engineering Faculty Fellowship

Metformin mitigates the impaired development of skeletal muscle in the offspring of obese mice

Background: Maternal obesity is linked with offspring obesity and type 2 diabetes. Skeletal muscle (SM) insulin resistance is central to the development of diabetes. Adenosine monophosphate (AMP)-activated protein kinase (AMPK) is inhibited in SM of fetuses born to obese mothers

The Chemotherapeutic Drug 5-Fluorouracil Promotes PKR-Mediated Apoptosis in a p53- Independent Manner in Colon and Breast Cancer Cells

The chemotherapeutic drug 5-FU is widely used in the treatment of a range of cancers, but resistance to the drug remains a major clinical problem. Since defects in the mediators of apoptosis may account for chemo-resistance, the identification of new targets involved in 5-FU-induced apoptosis is of main clinical interest. We have identified the ds-RNA-dependent protein kinase (PKR) as a key molecular target of 5-FU involved in apoptosis induction in human colon and breast cancer cell lines. PKR distribution and activation, apoptosis induction and cytotoxic effects were analyzed during 5-FU and 5-FU/IFNα treatment in several colon and breast cancer cell lines with different p53 status. PKR protein was activated by 5-FU treatment in a p53-independent manner, inducing phosphorylation of the protein synthesis translation initiation factor eIF-2α and cell death by apoptosis. Furthermore, PKR interference promoted a decreased response to 5-FU treatment and those cells were not affected by the synergistic antitumor activity of 5-FU/IFNα combination. These results, taken together, provide evidence that PKR is a key molecular target of 5-FU with potential relevance in the clinical use of this drug

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections

The Social Aspect Of Open Space In Rehabilitation Gardens And Parks

In the research process the landscape space of Latvian rehabilitation centers was inspected and analyzed within the social context. The centers were singled out not only by their aesthetical quality, but also by their functional landscape values contained. Rehabilitation gardens and parks are spaces, where people do more than receive medical treatment, they can relax surrounded by nature, engage in physical activities and rest without taking into account ones social status, age, gender, nationality, political views and religion. The goal is to summarize how Latvian rehabilitation gardens and parks promote patients’ physical activities in open space and analyze the functional quality of landscape of Latvian rehabilitation gardens and parks. Considering the quality of rehabilitation center environment, it is important to evaluated their accessibility and usability by possibly greater user spectrum that is characterized as a universal design. In Latvia a conceptual direction of design like this is relatively new, but already positively accepted and applied by specialists. Open space designs of rehabilitation center landscaping directly influence how a person feels and lives in the landscape. They sculpt not only the material quality of the environment, but also improve people’s communication facility and attitude towards one another. It is important for the environment of Latvian rehabilitation institutions to be friendly, because they are created for the use by all social groups and individuals by applying universal standard principles for open space improvement.</p