The Telecommunication Markets in Selected OECD Countries: Market Characteristics and Regulatory Institutions

Telekommunikation, Regulierung, Markt, OECD-Staaten, Vergleich, Telecommunications, Regulation, Market, OECD countries, Comparison

WormBase: A modern Model Organism Information Resource

WormBase (https://wormbase.org/) is a mature Model Organism Information Resource supporting researchers using the nematode Caenorhabditis elegans as a model system for studies across a broad range of basic biological processes. Toward this mission, WormBase efforts are arranged in three primary facets: curation, user interface and architecture. In this update, we describe progress in each of these three areas. In particular, we discuss the status of literature curation and recently added data, detail new features of the web interface and options for users wishing to conduct data mining workflows, and discuss our efforts to build a robust and scalable architecture by leveraging commercial cloud offerings. We conclude with a description of WormBase\u27s role as a founding member of the nascent Alliance of Genome Resources

Caenorhabditis nomenclature

Genetic nomenclature allows the genetic features of an organism to be structured and described in a uniform and systematicway. Genetic features, including genes, variations (both natural and induced), and gene products, are assigned descriptorsthat inform on the nature of the feature. These nomenclature designations facilitate communication among researchers (in publications,presentations, and databases) to advance understanding of the biology of the genetic feature and the experimental utilizationof organisms that contain the genetic feature. The nomenclature system that is used for C. elegans was first employed by Sydney Brenner (1974) in his landmark description of the genetics of this model organism, and then substantially extended and modified in Horvitz et al., 1979. The gene, allele, and chromosome rearrangement nomenclature, described below, is an amalgamation of that from bacteria andyeast, with the rearrangement types from Drosophila. The nomenclature avoids standard words, subscripts, superscripts, and Greek letters and includes a hyphen (-) to separatethe gene name from gene number (distinct genes with similar phenotypes or molecular properties). As described by Jonathan Hodgkin, ‘the hyphen is about 1 mm in length in printed text and therefore symbolizes the 1 mm long worm’. These nomenclature propertiesmake C. elegans publications highly suitable for informatic text mining, as there is minimal ambiguity. From the founding of the CaenorhabditisGenetics Center (CGC) in 1979 until 1992, Don Riddle and Mark Edgley acted as the central repository for genetic nomenclature. Jonathan Hodgkin was nomenclature czar from 1992 through 2013; this was a pivotal period with the elucidation of the genome sequence of C. elegans, and later that of related nematodes, and the inception of WormBase. Thus, under the guidance of Hodgkin, the nomenclature system became a central feature of WormBase and the number and types of genetic features significantly expanded. The nomenclature system remains dynamic, with recentadditions including guidelines related to genome engineering, and continued reliance on the community for input. WormBase assigns specific identifying codes to each laboratory engaged in dedicated long-term genetic research on C. elegans. Each laboratory is assigned a laboratory/strain code for naming strains, and an allele code for naming genetic variation(e.g., mutations) and transgenes. These designations are assigned to the laboratory head/PI who is charged with supervisingtheir organization in laboratory databases and their associated biological reagents that are described on WormBase, in publications, and distributed to the scientific community on request. The laboratory/strain code is used: a) to identifythe originator of community-supplied information on WormBase, which, in addition to attribution, facilitates communicationbetween the community/curators and the originator if an issue related to the information should arise at a later date; andb) to provide a tracking code for activities at the CGC. The laboratory/strain designation consists of 2-3 uppercase letters while the allele designation has 1-3 lowercase letters.The final letter of a laboratory code should not be an “O” or an “I” so as not to be mistaken for the numbers “0” or “1” respectively.Additionally, allele designations should also not end with the letter “l” which could also be mistaken for the number “1.” These codes are listed at the CGC and in WormBase. Investigators generating strains, alleles, transgenes, and/or defining genes require these designations and should applyfor them at [email protected]. Information for several other nematode species, in addition to C. elegans, is curated at WormBase. All species are referred to by their Linnean binomial names (e.g,. Caenorhabditis elegans or C. elegans). Details of all the genomes available at WormBase and the degree of their curation can be found at www.wormbase.org/species/al

Korpuslinguistische Zugänge zu Agonalen Zentren

In der Arbeit wird die Analyse agonaler Zentren, die Felder (2012) vorgelegt hat, überprüft und um korpuslinguistische Herangehensweisen erweitert. Es wird überprüft, inwiefern bestimmte Wortarten in der Lage sind, die Analyse agonaler Zentren unabhängig vom Thema des Diskurses zu unterstützen. Dazu wird die computergestützte Korpusanalyse mit Hilfe von Konnektoren, Präpositionen, Partikeln, Substantiven, Adjektiven und Verben zunächst an einem bereits von Felder (2012) analysierten Korpus getestet und dann an einem weiteren, im Hinblick auf Thema und Textsorten völlig anderen Korpus überprüft. Insbesondere die Konnektoren stellen sich dabei als für die themenunabhängige, computergestützte Korpusanalyse als leistungsstark heraus

On Advanced Mobility Concepts for Intelligent Planetary Surface Exploration

Surface exploration by wheeled rovers on Earth's Moon (the two Lunokhods) and Mars (Nasa's Sojourner and the two MERs) have been followed since many years already very suc-cessfully, specifically concerning operations over long time. However, despite of this success, the explored surface area was very small, having in mind a total driving distance of about 8 km (Spirit) and 21 km (Opportunity) over 6 years of operation. Moreover, ESA will send its ExoMars rover in 2018 to Mars, and NASA its MSL rover probably this year. However, all these rovers are lacking sufficient on-board intelligence in order to overcome longer dis-tances, driving much faster and deciding autonomously on path planning for the best trajec-tory to follow. In order to increase the scientific output of a rover mission it seems very nec-essary to explore much larger surface areas reliably in much less time. This is the main driver for a robotics institute to combine mechatronics functionalities to develop an intelligent mo-bile wheeled rover with four or six wheels, and having specific kinematics and locomotion suspension depending on the operational terrain of the rover to operate. DLR's Robotics and Mechatronics Center has a long tradition in developing advanced components in the field of light-weight motion actuation, intelligent and soft manipulation and skilled hands and tools, perception and cognition, and in increasing the autonomy of any kind of mechatronic systems. The whole design is supported and is based upon detailed modeling, optimization, and simula-tion tasks. We have developed efficient software tools to simulate the rover driveability per-formance on various terrain characteristics such as soft sandy and hard rocky terrains as well as on inclined planes, where wheel and grouser geometry plays a dominant role. Moreover, rover optimization is performed to support the best engineering intuitions, that will optimize structural and geometric parameters, compare various kinematics suspension concepts, and make use of realistic cost functions like mass and consumed energy minimization, static sta-bility, and more. For self-localization and safe navigation through unknown terrain we make use of fast 3D stereo algorithms that were successfully used e.g. in unmanned air vehicle ap-plications and on terrestrial mobile systems. The advanced rover design approach is applica-ble for lunar as well as Martian surface exploration purposes. A first mobility concept ap-proach for a lunar vehicle will be presented

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field