Acute Eczema in Adults and Children

Abstract Not Provided

Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization

Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics (``climate'') can be produced by employing a feedback loop, whereby the model is trained to predict forward one time step, then the model output is used as input for multiple time steps. In the absence of mitigating techniques, however, this technique can result in artificially rapid error growth. In this article, we systematically examine the technique of adding noise to the ML model input during training to promote stability and improve prediction accuracy. Furthermore, we introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training. Our case study uses reservoir computing, a machine-learning method using recurrent neural networks, to predict the spatiotemporal chaotic Kuramoto-Sivashinsky equation. We find that reservoir computers trained with noise or with LMNT produce climate predictions that appear to be indefinitely stable and have a climate very similar to the true system, while reservoir computers trained without regularization are unstable. Compared with other regularization techniques that yield stability in some cases, we find that both short-term and climate predictions from reservoir computers trained with noise or with LMNT are substantially more accurate. Finally, we show that the deterministic aspect of our LMNT regularization facilitates fast hyperparameter tuning when compared to training with noise.Comment: 39 pages, 8 figures, 5 table

Developing ecosystem service indicators: experiences and lessons learned from sub-global assessments and other initiatives

People depend upon ecosystems to supply a range of services necessary for their survival and well-being. Ecosystem service indicators are critical for knowing whether or not these essential services are being maintained and used in a sustainable manner, thus enabling policy makers to identify the policies and other interventions needed to better manage them. As a result, ecosystem service indicators are of increasing interest and importance to governmental and inter-governmental processes, including amongst others the Convention on Biological Diversity (CBD) and the Aichi Targets contained within its strategic plan for 2011-2020, as well as the emerging Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES). Despite this growing demand, assessing ecosystem service status and trends and developing robust indicators is o!en hindered by a lack of information and data, resulting in few available indicators. In response, the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), together with a wide range of international partners and supported by the Swedish International Biodiversity Programme (SwedBio)*, undertook a project to take stock of the key lessons that have been learnt in developing and using ecosystem service indicators in a range of assessment contexts. The project examined the methodologies, metrics and data sources employed in delivering ecosystem service indicators, so as to inform future indicator development. This report presents the principal results of this project

The Emerging Scholarly Brain

It is now a commonplace observation that human society is becoming a coherent super-organism, and that the information infrastructure forms its emerging brain. Perhaps, as the underlying technologies are likely to become billions of times more powerful than those we have today, we could say that we are now building the lizard brain for the future organism.Comment: to appear in Future Professional Communication in Astronomy-II (FPCA-II) editors A. Heck and A. Accomazz

Assessing Interaction Networks with Applications to Catastrophe Dynamics and Disaster Management

In this paper we present a versatile method for the investigation of interaction networks and show how to use it to assess effects of indirect interactions and feedback loops. The method allows to evaluate the impact of optimization measures or failures on the system. Here, we will apply it to the investigation of catastrophes, in particular to the temporal development of disasters (catastrophe dynamics). The mathematical methods are related to the master equation, which allows the application of well-known solution methods. We will also indicate connections of disaster management with excitable media and supply networks. This facilitates to study the effects of measures taken by the emergency management or the local operation units. With a fictious, but more or less realistic example of a spreading epidemic disease or a wave of influenza, we illustrate how this method can, in principle, provide decision support to the emergency management during such a disaster. Similar considerations may help to assess measures to fight the SARS epidemics, although immunization is presently not possible

Comparative metagenomics reveals the distinctive adaptive features of the Spongia officinalis endosymbiotic consortium

Current knowledge of sponge microbiome functioning derives mostly from comparative analyses with bacterioplankton communities. We employed a metagenomics-centered approach to unveil the distinct features of the Spongia officinalis endosymbiotic consortium in the context of its two primary environmental vicinities. Microbial metagenomic DNA samples (n = 10) from sponges, seawater, and sediments were subjected to Hiseq Illumina sequencing (c. 15 million 100 bp reads per sample). Totals of 10,272 InterPro (IPR) predicted protein entries and 784 rRNA gene operational taxonomic units (OTUs, 97% cut-off) were uncovered from all metagenomes. Despite the large divergence in microbial community assembly between the surveyed biotopes, the S. officinalis symbiotic community shared slightly greater similarity (p < 0.05), in terms of both taxonomy and function, to sediment than to seawater communities. The vast majority of the dominant S. officinalis symbionts (i.e., OTUs), representing several, so-far uncultivable lineages in diverse bacterial phyla, displayed higher residual abundances in sediments than in seawater. CRISPR-Cas proteins and restriction endonucleases presented much higher frequencies (accompanied by lower viral abundances) in sponges than in the environment. However, several genomic features sharply enriched in the sponge specimens, including eukaryotic-like repeat motifs (ankyrins, tetratricopeptides, WD-40, and leucine-rich repeats), and genes encoding for plasmids, sulfatases, polyketide synthases, type IV secretion proteins, and terpene/terpenoid synthases presented, to varying degrees, higher frequencies in sediments than in seawater. In contrast, much higher abundances of motility and chemotaxis genes were found in sediments and seawater than in sponges. Higher cell and surface densities, sponge cell shedding and particle uptake, and putative chemical signaling processes favoring symbiont persistence in particulate matrices all may act as mechanisms underlying the observed degrees of taxonomic connectivity and functional convergence between sponges and sediments. The reduced frequency of motility and chemotaxis genes in the sponge microbiome reinforces the notion of a prevalent mutualistic mode of living inside the host. This study highlights the S. officinalis "endosymbiome" as a distinct consortium of uncultured prokaryotes displaying a likely "sit-and-wait" strategy to nutrient foraging coupled to sophisticated anti-viral defenses, unique natural product biosynthesis, nutrient utilization and detoxification capacities, and both microbe-microbe and host-microbe gene transfer amenability.Portuguese Foundation for Science and Technology (FCT) [PTDC/BIA-MIC/3865/2012, PTDC/MAR-BIO/1547/2014]; Education, Audiovisual and Culture Executive Agency (European Commission, Erasmus Mundus Programme) [EMA2 lot7/SALA1206422]info:eu-repo/semantics/publishedVersio

EEG-Based Functional Brain Networks: Does the Network Size Matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks – whose nodes can vary from tens to hundreds – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies

Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity

Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity.Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability.The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect effective signaling across brain regions, perhaps through the modulation of signal or the suppression of the propagation of noise