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Cryo-electron microscopy (cryo-EM) studies using single particle reconstruction are extensively used to reveal structural information on macromolecular complexes. Aiming at the highest achievable resolution, state of the art electron microscopes automatically acquire thousands of high-quality micrographs. Particles are detected on and boxed out from each micrograph using fully- or semi-automated approaches. However, the obtained particles still require laborious manual post-picking classification, which is one major bottleneck for single particle analysis of large datasets. We introduce MAPPOS, a supervised post-picking strategy for the classification of boxed particle images, as additional strategy adding to the already efficient automated particle picking routines. MAPPOS employs machine learning techniques to train a robust classifier from a small number of characteristic image features. In order to accurately quantify the performance of MAPPOS we used simulated particle and non-particle images. In addition, we verified our method by applying it to an experimental cryo-EM dataset and comparing the results to the manual classification of the same dataset. Comparisons between MAPPOS and manual post-picking classification by several human experts demonstrated that merely a few hundred sample images are sufficient for MAPPOS to classify an entire dataset with a human-like performance. MAPPOS was shown to greatly accelerate the throughput of large datasets by reducing the manual workload by orders of magnitude while maintaining a reliable identification of non-particle images.

Cryo-electron microscopy (cryo-EM) studies using single particle reconstruction is extensively used to reveal structural information of macromolecular complexes. Aiming at the highest achievable resolution, state of the art electron microscopes acquire thousands of high-quality images. Having collected these data, each single particle must be detected and windowed out. Several fully- or semi-automated approaches have been developed for the selection of particle images from digitized micrographs. However they still require laborious manual post processing, which will become the major bottleneck for next generation of electron microscopes. Instead of focusing on improvements in automated particle selection from micrographs, we propose a post-picking step for classifying small windowed images, which are output by common picking software. A supervised strategy for the classification of windowed micrograph images into particles and non-particles reduces the manual workload by orders of magnitude. The method builds on new powerful image features, and the proper training of an ensemble classifier. A few hundred training samples are enough to achieve a human-like classification performance., Comment: 14 pages, 5 figures

PPak is a new fiber-based Integral Field Unit (IFU), developed at the Astrophysical Institute Potsdam, implemented as a module into the existing PMAS spectrograph. The purpose of PPak is to provide both an extended field-of-view with a large light collecting power for each spatial element, as well as an adequate spectral resolution. The PPak system consists of a fiber bundle with 331 object, 36 sky and 15 calibration fibers. The object and sky fibers collect the light from the focal plane behind a focal reducer lens. The object fibers of PPak, each 2.7 arcseconds in diameter, provide a contiguous hexagonal field-of-view of 74 times 64 arcseconds on the sky, with a filling factor of 60%. The operational wavelength range is from 400 to 900nm. The PPak-IFU, together with the PMAS spectrograph, are intended for the study of extended, low surface brightness objects, offering an optimization of total light-collecting power and spectral resolution. This paper describes the instrument design, the assembly, integration and tests, the commissioning and operational procedures, and presents the measured performance at the telescope., Comment: 14 pages, 21 figures, accepted at PASP

The quantitative spectroscopy of stellar objects in complex environments is mainly limited by the ability of separating the object from the background. Standard slit spectroscopy, restricting the field of view to one dimension, is obviously not the proper technique in general. The emerging Integral Field (3D) technique with spatially resolved spectra of a two-dimensional field of view provides a great potential for applying advanced subtraction methods. In this paper an image reconstruction algorithm to separate point sources and a smooth background is applied to 3D data. Several performance tests demonstrate the photometric quality of the method. The algorithm is applied to real 3D observations of a sample Planetary Nebula in M31, whose spectrum is contaminated by the bright and complex galaxy background. The ability of separating sources is also studied in a crowded stellar field in M33., Comment: 4 pages, 8 figures, in Proceedings "Euro3D Science Workshop", Cambridge, May 2003, accepted for publication in AN

GrassPlot is a collaborative vegetation-plot database organised by the Eurasian Dry Grassland Group (EDGG)and listed in the Global Index of Vegetation-Plot Databases (GIVD ID EU-00-003). GrassPlot collects plot records (releves) from grasslands and other open habitats of the Palaearctic biogeographic realm. It focuses on precisely delimited plots of eight standard grain sizes (0.0001; 0.001; ... 1,000 m_) and on nested-plot series withat least four different grain sizes. The usage of GrassPlot is regulated through Bylaws that intend to balance the interests of data contributors and data users. The current version (v. 1.00) contains data for approximately 170,000 plots of different sizes and 2,800 nested-plot series. The key components are richness data and metadata.However, most included datasets also encompass compositional data. About 14,000 plots have near-complete records of terricolous bryophytes and lichens in addition to vascular plants. At present, GrassPlot contains data from 36 countries throughout the Palaearctic, spread across elevational gradients and major grassland types. GrassPlot with its multi-scale and multi-taxon focus complements the larger international vegetation plot databases, such as the European Vegetation Archive (EVA) and the global database "sPlot". Its main aim is to facilitate studies on the scale- and taxon-dependency of biodiversity patterns and drivers along macroecological gradients. GrassPlot is a dynamic database and will expand through new data collection coordinated by the elected Governing Board. We invite researchers with suitable data to join GrassPlot. Researchers with project ideas addressable with GrassPlot data are welcome to submit proposals to the Governing Board.

Describes a statewide childhood gun safety program in New Mexico aimed at reducing the availability of loaded guns at home. Focusing on increasing safety awareness of communities, health care professionals, parents, and gun owners, the program will be continued with a new three-year funding grant. (SLD)