Biological Activities of Aqueous and Organic Extracts from Tropical Marine Sponges

We report on screening tests of 66 extracts obtained from 35 marine sponge species from the Caribbean Sea (Curaçao) and from eight species from the Great Barrier Reef (Lizard Island). Extracts were prepared in aqueous and organic solvents and were tested for hemolytic, hemagglutinating, antibacterial and anti-acetylcholinesterase (AChE) activities, as well as their ability to inhibit or activate cell protein phosphatase 1 (PP1). The most interesting activities were obtained from extracts of Ircinia felix, Pandaros acanthifolium, Topsentia ophiraphidites, Verongula rigida and Neofibularia nolitangere. Aqueous and organic extracts of I. felix and V. rigida showed strong antibacterial activity. Topsentia aqueous and some organic extracts were strongly hemolytic, as were all organic extracts from I. felix. The strongest hemolytic activity was observed in aqueous extracts from P. acanthifolium. Organic extracts of N. nolitangere and I. felix inhibited PP1. The aqueous extract from Myrmekioderma styx possessed the strongest hemagglutinating activity, whilst AChE inhibiting activity was found only in a few sponges and was generally weak, except in the methanolic extract of T. ophiraphidites

Biological activities of ethanolic extracts from deep-sea antarctic marine sponges

We report on the screening of ethanolic extracts from 33 deep-sea Antarctic marine sponges for different biological activities. We monitored hemolysis, inhibition of acetylcholinesterase, cytotoxicity towards normal and transformed cells and growth inhibition of laboratory, commensal and clinically and ecologically relevant bacteria. The most prominent activities were associated with the extracts from sponges belonging to the genus Latrunculia, which show all of these activities. While most of these activities are associated to already known secondary metabolites, the extremely strong acetylcholinesterase inhibitory potential appears to be related to a compound unknown to date. Extracts from Tetilla leptoderma, Bathydorus cf. spinosus, Xestospongia sp., Rossella sp., Rossella cf. racovitzae and Halichondria osculum were hemolytic, with the last two also showing moderate cytotoxic potential. The antibacterial tests showed significantly greater activities of the extracts of these Antarctic sponges towards ecologically relevant bacteria from sea water and from Arctic ice. This indicates their ecological relevance for inhibition of bacterial microfouling

An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning

Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data representations/parametrizations; these are, in turn, useful in nonlinear model identification tasks. We focus here on the case of time series data that can ultimately be modelled as a spatially distributed system (e.g. a partial differential equation, PDE), but where we do not know the space in which this PDE should be formulated. Hence, even the spatial coordinates for the distributed system themselves need to be identified - to emerge from - the data mining process. We will first validate this emergent space reconstruction for time series sampled without space labels in known PDEs; this brings up the issue of observability of physical space from temporal observation data, and the transition from spatially resolved to lumped (order-parameter-based) representations by tuning the scale of the data mining kernels. We will then present actual emergent space discovery illustrations. Our illustrative examples include chimera states (states of coexisting coherent and incoherent dynamics), and chaotic as well as quasiperiodic spatiotemporal dynamics, arising in partial differential equations and/or in heterogeneous networks. We also discuss how data-driven spatial coordinates can be extracted in ways invariant to the nature of the measuring instrument. Such gauge-invariant data mining can go beyond the fusion of heterogeneous observations of the same system, to the possible matching of apparently different systems

Radially polarized passively mode-locked thin-disk laser oscillator emitting sub-picosecond pulses with an average output power exceeding the 100 W level

We report on a high-power passively mode-locked radially polarized Yb:YAG thin-disk oscillator providing 125 W of average output power. To the best of our knowledge, this is the highest average power ever reported from a mode-locked radially polarized oscillator without subsequent amplification stages. Mode-locking was achieved by implementing a SESAM as the cavity end mirror and the radial polarization of the LG*01 mode was obtained by means of a circular Grating Waveguide Output Coupler. The repetition rate was 78 MHz. A pulse duration of 0.97 ps and a spectral bandwidth of 1.4 nm (FWHM) were measured at the maximum output power. This corresponds to a pulse energy of 1.6 μJ and a pulse peak power of 1.45 MW. A high degree of radial polarization of 97.3 ± 1% and an M2-value of 2.16 which is close to the theoretical value for the LG*01 doughnut mode were measured

Learning effective stochastic differential equations from microscopic simulations: combining stochastic numerics and deep learning

We identify effective stochastic differential equations (SDE) for coarse observables of fine-grained particle- or agent-based simulations; these SDE then provide coarse surrogate models of the fine scale dynamics. We approximate the drift and diffusivity functions in these effective SDE through neural networks, which can be thought of as effective stochastic ResNets. The loss function is inspired by, and embodies, the structure of established stochastic numerical integrators (here, Euler-Maruyama and Milstein); our approximations can thus benefit from error analysis of these underlying numerical schemes. They also lend themselves naturally to "physics-informed" gray-box identification when approximate coarse models, such as mean field equations, are available. Our approach does not require long trajectories, works on scattered snapshot data, and is designed to naturally handle different time steps per snapshot. We consider both the case where the coarse collective observables are known in advance, as well as the case where they must be found in a data-driven manner.Comment: 19 pages, includes supplemental materia

A filament of dark matter between two clusters of galaxies

It is a firm prediction of the concordance Cold Dark Matter (CDM) cosmological model that galaxy clusters live at the intersection of large-scale structure filaments. The thread-like structure of this "cosmic web" has been traced by galaxy redshift surveys for decades. More recently the Warm-Hot Intergalactic Medium (WHIM) residing in low redshift filaments has been observed in emission and absorption. However, a reliable direct detection of the underlying Dark Matter skeleton, which should contain more than half of all matter, remained elusive, as earlier candidates for such detections were either falsified or suffered from low signal-to-noise ratios and unphysical misalignements of dark and luminous matter. Here we report the detection of a dark matter filament connecting the two main components of the Abell 222/223 supercluster system from its weak gravitational lensing signal, both in a non-parametric mass reconstruction and in parametric model fits. This filament is coincident with an overdensity of galaxies and diffuse, soft X-ray emission and contributes mass comparable to that of an additional galaxy cluster to the total mass of the supercluster. Combined with X-ray observations, we place an upper limit of 0.09 on the hot gas fraction, the mass of X-ray emitting gas divided by the total mass, in the filament.Comment: Nature, in pres