Entity

Time filter

Source Type

Pittsburgh, PA, United States

Meriney S.D.,University of Pittsburgh | Dittrich M.,University of Pittsburgh | Dittrich M.,Pittsburgh Supercomputing Center
Journal of Physiology | Year: 2013

The neuromuscular junction is known as a strong and reliable synapse. It is strong because it releases an excess of chemical transmitter, beyond what is required to bring the postsynaptic muscle cell to threshold. Because the synapse can sustain suprathreshold muscle activation during short trains of action potentials, it is also reliable. The presynaptic mechanisms that lead to reliability during short trains of activity have only recently been elucidated. It appears that there are relatively few calcium channels in individual active zones, that channels open with a low probability during action potential stimulation and that even if channels open the resulting calcium flux only rarely triggers vesicle fusion. Thus, each synaptic vesicle may only associate with a small number of calcium channels, forming an unreliable single vesicle release site. Strength and reliability of the neuromuscular junction emerge as a result of its assembly from thousands of these unreliable single vesicle release sites. Hence, these synapses are strong while at the same time only releasing a small subset of available docked vesicles during each action potential, thus conserving transmitter release resources. This prevents significant depression during short trains of action potential activity and confers reliability. © 2013 The Authors. The Journal of Physiology © 2013 The Physiological Society. Source


Morgan J.L.,Harvard University | Berger D.R.,Harvard University | Wetzel A.W.,Pittsburgh Supercomputing Center | Lichtman J.W.,Harvard University
Cell | Year: 2016

Summary In an attempt to chart parallel sensory streams passing through the visual thalamus, we acquired a 100-trillion-voxel electron microscopy (EM) dataset and identified cohorts of retinal ganglion cell axons (RGCs) that innervated each of a diverse group of postsynaptic thalamocortical neurons (TCs). Tracing branches of these axons revealed the set of TCs innervated by each RGC cohort. Instead of finding separate sensory pathways, we found a single large network that could not be easily subdivided because individual RGCs innervated different kinds of TCs and different kinds of RGCs co-innervated individual TCs. We did find conspicuous network subdivisions organized on the basis of dendritic rather than neuronal properties. This work argues that, in the thalamus, neural circuits are not based on a canonical set of connections between intrinsically different neuronal types but, rather, may arise by experience-based mixing of different kinds of inputs onto individual postsynaptic cells. © 2016 Elsevier Inc. Source


News Article
Site: http://www.nature.com/nature/current_issue/

We imaged the primary visual cortex of an awake 9-month-old C57BL/6 male mouse, as described previously10, 13, with a custom-built two-photon microscope12. Using volumetric in vivo two-photon calcium imaging of a genetically encoded calcium indicator (GCaMP3), we measured the time-resolved responses of a population of identified neurons to a wide array of stimuli including drifting gratings (up to 16 directions, 3 spatial, and 2 temporal frequencies). Following 12 days of imaging calcium responses in the same cohort of neurons, we labelled blood vessels with a tail vein injection (rhodamine B-conjugated dextran) and acquired an in vivo fluorescence volume. The animal’s brain was then prepared for large-scale transmission EM as described previously8. 3,700 serial sections (<50 nm thick) were cut and imaged spanning a 450 μm × 450 μm × 150 μm volume at 4 nm × 4 nm × 40 nm per voxel resolution. Sections representing the middle third of the EM volume were aligned and imported into CATMAID16 for distributed, online, manual reconstruction and targeted volumes around identified synapses were exported for volumetric segmentation and PSD analysis. EM reconstructed neurons were identified in the in vivo stack by using the blood vessels as landmarks. Apical dendrites originating from deeper neocortical lamina were similarly identified and corresponded by location and branching geometry of their apical tufts. Permutation tests were used in statistical analyses, unless otherwise noted. All procedures were conducted in accordance with the ethical guidelines of the NIH and approved by the IACUC at Harvard Medical School. For cranial window implant surgery the mouse was anesthetized with isoflurane (1.2–2% in 100% O ). Dexamethasone (3.2 mg per kg body weight, intramuscular) was administered on the day before surgery and atropine (0.2 mg per kg body weight, intraperitoneally) at the beginning of surgery. Using aseptic technique, we secured a headpost in place using cyanoacrylate, dental acrylic, and C&B Metabond (Parkell), and made a 5 mm craniotomy over the left visual cortex (centre: ~2.8 mm lateral, 0.5 mm anterior to lambda) as described previously32. A 5 mm glass cranial window was implanted consisting of an 8 mm coverslip cured to two 5 mm coverslips (Warner #1; total thickness: ~0.5 mm; thickness below skull: ~200 mm) using index-matched adhesive (Norland #71). We secured the window in place using cyanoacrylate and dental acrylic. We habituated the mouse with water scheduling so that water was delivered only during and immediately after head restraint training. We increased the duration of head restraint sessions over the course of 2 weeks, from 3 min to 2 h32. We then performed retinotopic mapping of visual cortical areas using widefield intrinsic autofluorescence imaging, measuring autofluorescence produced by blue excitation (470 nm centre, 40 nm band, Chroma) through a green/red emission filter (longpass, 500 nm cutoff). We collected images using a CCD camera (Sensicam, Cooke, 344 × 260 pixels spanning 4 mm × 3 mm; 2 Hz acquisition rate) through a 5× air objective (0.14 NA, Mitituyo) using ImageJ acquisition software. For retinotopic mapping, stimuli were presented at 2–6 retinotopic positions for 10 s each, with 10 s of mean luminance between trials. GCaMP3 expression was targeted by viral injection. Dexamethasone (3.2 mg per kg body weight, intramuscular) was administered at least 2 h before coverslip removal. The mouse was anesthetized (isoflurane, 1–1.5%) and the cranial window was sterilized with alcohol and the coverslip removed. We then volume injected (50–100 ml min−1, Stoelting) 30–100 nl of a 10:1 mixture of AAV2/1.hSynap.GCaMP3.3.SV4033 (Penn Vector Core) and 1 mM sulforhodamine-101 (Invitrogen) to visualize the injection. Using the blood vessel pattern observed during widefield imaging as a guide, we made an injection in the posterior part of primary visual cortex at a depth of ~250 μm below the pial surface. After injection, a new cranial window was sealed in place and the mouse recovered. A 120 Hz LCD monitor (Samsung 2233RZ, 2200) was calibrated at each temporal frequency using a spectrophotometer (Photoresearch PR-650). We confirmed waveforms were sinusoidal by measuring luminance fluctuations of a full-field sinusoidally modulated stimulus (using a photomultiplier tube, Hamamatsu). The monitor was positioned so that the stimulus patch was 21 cm from the contralateral eye. Local 40° Gabor-like circular patches (sigmoidal 10–90% falloff in 10°) containing either square-wave (for mapping retinotopy with widefield intrinsic autofluorescence and targeting GCaMP3 injections) or sine-wave (for mapping position of receptive fields with two-photon imaging) drifting gratings (80% contrast) were alternated with periods of uniform mean luminance (59 cd m−2). In an effort to increase the population of responsive cells and explore receptive field parameters we presented gratings of varying directions at multiple spatial and temporal frequencies or at different positions in the visual field. We presented either 8 directions at 3 spatial frequencies (0.06, 0.12, and 0.24 cycles per degree (cpd)) and 2 temporal frequencies (2 and 8 Hz), 16 directions at 2 spatial frequencies (0.04 and 0.16 cpd) and 2 temporal frequencies (2 and 8 Hz), 8 directions at 6 positions, or 16 directions at 4 positions (45–115° eccentricity and −5–25° elevation), for a total of 64 stimulus types plus 10% blank trials. Stimuli were centred on the location eliciting maximum calcium responses in the imaged field (monocular cortex), which most effectively drove responses in the population for experiments that did not vary stimulus position. All stimuli in a given protocol were presented in a pseudo-random order (sampling without replacement), and presented 3 times per volume experiment with 2–4 experiments per volume per day. Imaging was performed with a custom-designed two-photon laser-scanning microscope12. Excitation light from a Mai Tai HP DeepSee laser (Spectra-Physics) with dispersion compensation was directed into a modulator (Conoptics) and a beam expander (Edmund Optics). The expanded beam was raster scanned into the brain with a resonant (4 kHz, Electro-Optical Products) and a conventional galvanometer (Galvoline) (240 line frames, bidirectional, 31 Hz) through a 0.8 numerical aperture (NA) 16× objective lens (Nikon). Emitted photons were directed through a green filter (centre: 542 nm; band: 50 nm; Semrock) onto GaAsP photomultipliers (no cooling, 50 μA protection circuit, H7422P-40MOD, Hamamatsu). The photomultiplier signals were amplified (DHPCA-100, Femto), and low-pass filtered (cutoff frequency = ~700 kHz). These and the mirror driver signals were acquired at 3.3 MHz using a multifunction data acquisition board (PCI-6115, National Instruments). Images were reconstructed in MATLAB (MathWorks) and continuously streamed onto a RAID array. Microscope control was also performed in MATLAB using an analogue output board (PCI-6711, National Instruments). The laser’s dispersion compensation was adjusted to maximize collected fluorescence. A piezoelectric objective translator on the microscope enabled imaging multiple 300 × 300 × 100 μm volumes with 8 planes at 4 Hz separated by ~12.5 μm allowing us to capture the response properties of many cells through the depth of L2/3. The imaged field of view was 200–300 μm on a side at resolution of 0.8–1.2 μm per pixel (dwell-time ~2.7 μs). GCaMP3 was excited at 920 nm. Laser power was automatically adjusted as a function of imaging depth at the modulator with power exiting the objective ranging from 30–60 mW. During imaging, the mouse was placed on 6-inch diameter foam ball that could spin noiselessly on an axel (Plasteel). We monitored trackball revolutions using a custom photodetector circuit and recorded eye movements using an IR-CCD camera (Sony xc-ei50; 30 Hz) and infrared illumination (720–2,750 nm bandpass filter, Edmund). Visual stimuli were presented for 4 s with 4 s of mean luminance between trials. Recording sessions were 2–6 h in duration. Use of the genetically encoded calcium indicator GCaMP3, permitted recording from the same neurons over multiple days with the selectivity of calcium signals stable over several days of imaging (Extended Data Fig. 1)32, 34, 35. Within this volume we obtained calcium signals from cell bodies of superficial layer (L2/3) neurons and large calibre apical dendrites that continued beyond the depth of our imaging volume and had branching morphologies consistent with deep layer pyramidal cells. These were likely from L5 neurons because of their large calibre, and because most L6 pyramidal cells do not project their apical dendrites more superficially than L436, 37. The calcium signals from these deep layer apical dendrites stem from either forward-38, 39 or back-propagating action potentials40, are consistent across days (Extended Data Fig. 1) and along the length of the deep layer apical dendritic trunks (Extended Data Fig. 2), and therefore most likely reflect the response properties of the soma. We relocated the cohort of neurons daily by using the vasculature’s negative staining as fiducial landmarks. For the final in vivo imaging session, we injected the tail vein with a fluorescent dye to label blood vessels (rhodamine B isothiocyanate–Dextran (MW ~70k), 5% v/v, Sigma) and acquired a fluorescence stack to correspond the calcium-imaged neurons in vivo with their identities in the EM volume ex vivo8 (see below, and Extended Data Fig. 4). Following in vivo two-photon imaging the animal was perfused transcardially (2% formaldehyde/2.5% glutaraldehyde in 0.1 M cacodylate buffer with 0.04% CaCl ) and the brain was processed for serial-section TEM. 200 μm thick coronal vibratome sections were cut, post-fixed, and en bloc stained with 1% osmium tetroxide/1.5% potassium ferrocyanide followed by 1% uranyl acetate, dehydrated with a graded ethanol series, and embedded in resin (TAAB 812 Epon, Canemco). We located the calcium-imaged region by matching vasculature between in vivo fluorescence and serial thick (1 μm) toluidine blue (EMS) sections cut from an adjacent vibratome sections, then cut ~3,700 serial (<50 nm) sections on an ultramicrotome (Leica UC7) using a 35 degree diamond knife (EMS-Diatome) and manually collected sections on 1 mm × 2 mm dot-notch slot grids (Synaptek) that were coated with a pale gold Pioloform (Ted Pella) support film, carbon coated, and glow-discharged. Following section pickup, we post-stained grids with uranyl acetate and lead citrate. Using the custom-built transmission electron microscope camera array (TEMCA)8 we imaged the ~3,700 section series, targeting a ~450 μm × 450 μm region for each section (Fig. 1c). Acquired at 4 nm per pixel in plane, this amounted to ~100 terabytes of raw data to date comprising 30 million cubic microns of brain and >10 million (4,000 × 2,672 pixel) camera images. Magnification at the scope was 2,000×, accelerating potential was 120 kV, and beam current was ~90 microamperes through a tungsten filament. Images suitable for circuit reconstruction were acquired at a net rate of 5–8 million pixels s−1. Approximately the middle third of the series (sections 2,281–3,154) was aligned using open source software developed at Pittsburgh Supercomputing Center (AlignTK)8 and imported into CATMAID16 for distributed online visualization and segmentation. Within the analysed EM series there were 51 missing sections. Nineteen were single section losses. There were 2 instances each of missing 2, 3, and 4; and 1 instance each of missing 6 or 8 consecutive sections near the series boundaries. Folds, staining artefacts, and sometimes cracks occurred during section processing, but were typically isolated to edges of our large sections and therefore did not usually interfere with manual segmentation. To find the correspondence between the cells imaged in vivo with those in the EM data set, a global 3D affine alignment was used with fiducial landmarks manually specified at successively finer scales of vasculature and then cell bodies to re-locate the calcium-imaged neurons in the EM-imaged volume (Extended Data Fig. 4). Apical dendrites arising from deep layer (putative L5) pyramidal neurons were identified by their characteristic morphology36, 41, 42 (also see below). Their correspondence was facilitated by the unique branching patterns of their apical tufts and those that could not be unambiguously identified were not included in the functional analysis. We first traced the axonal and dendritic arbors of the functionally characterized neurons in the EM data set by manually placing a series of marker points down the midline of each process to generate a skeletonized model of the arbors using CATMAID16 (Figs 1d, 2a, 3a, Extended Data Fig. 6, Supplementary Data 1–3). We identified synapses using classical criteria42. For each synapse on the axon of a functionally characterized cell, dendrites of postsynaptic excitatory neurons were traced either to the boundaries of the aligned volume or centripetally back to the cell body8. We identified deep layer apical dendrites of (putative L5) pyramidal cells by their large calibre, high spine density, and their continuation beyond the bottom border of the EM volume, which spans from the pial surface through L4. For each neuronal target reconstruction included in the analysis, a second independent annotator verified the tracing by working backwards from the most distal end of every process. An additional round of validation was done for each synapse between functionally characterized cells where a third annotator who had not previously traced the pre- or post-synaptic process, independently verified the anatomical connectivity blind to previous tracing work. We began this independent round of validation at each synapse and traced the pre- and postsynaptic processes centripetally. If the initial reconstruction and subsequent verification of the reconstruction diverged, that connection and the segmentation work distal from the point of divergence was excluded from further analysis. EM reconstruction and validation was performed blind to cells’ functional characteristics and targeted cells were initially assigned to individual annotators pseudo-randomly weighted by tracing productivity. We performed targeted volumetric reconstructions of synapses connecting functionally characterized cells by developing tools to interface with CATMAID cutout, locally align, and catalogue volumes of interest based on location (Fig. 4a; for example, 400 pixels × 400 pixels × 41 sections or 3.2 μm × 3.2 μm × 1.64 μm volumes centred on synapses represented by CATMAID connectors). Presynaptic boutons, postsynaptic spines, their parent axons and dendrites, and postsynaptic density (PSD) areas were manually segmented with itk-SNAP (http://www.itksnap.org/). PSD areas were calculated as described previously43 with obliquely cut or en face synapse areas measured using their maximum z-projection. En face or obliquely cut synapses were identified by serial sections that starkly transitioned from a clear presynaptic specialization hosting a vesicle pool, to a distinctly different postsynaptic cell, typically with an intervening section of electron dense area representing the postsynaptic density and/or synaptic cleft (for example, Extended Data Fig. 5). In vivo calcium imaging data was analysed in MATLAB and ImageJ (NIH) as described previously12, 13. To correct for motion along the imaged plane (x–y motion), the stack for each imaging plane was registered to the average field of view using TurboReg44. A 5 pixel border at each edge of the field of view was ignored to eliminate registration artefacts. Masks for analysing fluorescence signal from neurons were manually generated corresponding to cells in the EM volume, registered to the in vivo anatomical fluorescence stack, and to individual physiological imaging planes. Time courses of cells spanning multiple physiological imaging planes were weighted by dwell time in each plane and averaged across planes. Evoked responses for each EM identified cells were measured for each stimulation epoch as the difference in fractional fluorescence (% ΔF/F ) between the 5 s after and the 2.5 s before stimulus onset (pre-stimulus activity), and averaged across stimulus repetitions. We quantified visual responsiveness of each cell by calculating the average Pearson correlation coefficient of the responses to all stimuli across repetitions (average trial-to-trial correlation). We defined the significance of visual responses as the probability (P value) that the observed trial-to-trial correlation is larger than the correlation obtained from a full random permutation of the data for spatial and temporal frequency experiments (P  < 0.05) and experiments where stimulus position was varied (P  < 0.01). In retinotopic experiments designed to increase the number of characterized neurons, we found cells that did not reliably respond to stimuli ± 20° from the centre of the display. These cells that either had receptive fields smaller than our stimuli or stimuli were positioned at the at the edge of their receptive fields. We considered these cells as potentially driven by stimulus edge effects and therefore excluded such experiments from further analysis. To estimate the preferred orientation, direction, and spatiotemporal frequency, we modelled responses with a combination of a multivariate Gaussian with spatial frequency (x and y, deg), temporal frequency (Hz) and position (x and y, deg) as independent dimensions, a constant gain factor, and a static exponent. We fit the model to data using a large-scale nonlinear optimization algorithm (Trust Region Reflective, MATLAB Optimization Toolbox, MathWorks Inc.), generating multiple fits from randomly selected starting points and selected the best fit (least-square criterion). The quality of model fits was inspected visually for all neurons included in the data set. EM connectivity was analysed using custom written software in MATLAB and Python. Connectivity analysis that did not utilize functional information (Figs 1e and 3, Extended Data Fig. 7) started with the entire population of excitatory neuronal targets in the reconstructed network. Network modularity and neuron connectivity motifs (Fig. 1e and Extended Data Fig. 7) were analysed with code modified from the Brain Connectivity Toolbox45. We used an implementation of the Louvain method17 followed by consensus portioning46 for weighted and directed graphs to detect communities, or interconnected pyramidal neuron targets, from our EM reconstructed network purely by anatomical connectivity. For this analysis we included only the 201 traced neurons having multiple synaptic partners (degree ≥ 2). The number of synapses reconstructed between neurons was used as weights for all analyses. Modularity Q was given by the standard equation: where l is the total number of edges, given by where N is the total set of nodes, a is the (i,j) element of the weighted adjacency matrix, δ(m ,m ) is 1 if i and j are in the same community and 0 otherwise, and are the in and out degrees of the jth and ith nodes respectively, calculated by To generate null models of connectivity matrices for hypothesis testing, we shuffled the reconstructed adjacency conditioned on our sample degree, weight and strength distributions (Extended Data Fig. 7)31, 47. Analysis of connectivity with neuronal function restricted our sample population to those cell pairs where both pre- and post-synaptic cells were functionally characterized. For orientation tuning (Figs 1d, f, 2, 4a–c, Extended Data Figs 5, 6, 8, 9), between 50 neurons, there were 29 connected pairs. On average, we detected 1.3 synapses per connected pair where we measured orientation selectivity for both cells. We varied retinotopic position and spatial and temporal frequencies of the grating stimulus with the goal of improved measurement of orientation preference for more cells. The sensory physiology of a subset of cells were simultaneously recorded across multiple stimulus parameters. These 120 cells were used for signal correlation analysis (Extended Data Fig. 10). Potential synapse length (L ) represents the degree to which pairs of neurons’ axonal and dendritic arbors come sufficiently close to make a synapse (Fig. 2a, c–f, 3b, d, Extended Data Figs 9, 10). For excitatory pyramidal cells, we computed this length of potential synaptic connectivity between all pairs by first resampling the dendritic and axonal arbor skeletons to a maximum segment length of 40 nm (the average thickness of the EM sections) and summing the length of all dendrite segments within a maximum spine length distance of the axon (s = 5 μm: Figs 2, 3 and Extended Data Fig. 10; s = 1 μm: Extended Data Fig. 9). We use s = 5 μm based the longest spine connecting functionally connected neurons (~ 5 μm). Analysis of neurons connected by multiple synapses (Fig. 3) was not restricted to cell pairs where both pre and post-synaptic cells were physiologically characterized. This population included 137 neurons connected by 267 synapses in 115 multi-synapse cell pairs whose axonal and dendritic arbors were traced exhaustively in the aligned volume. As a comparison population, we used 25 unique pairs connected by one synapse from the functionally characterized population described above, because they were also reconstructed throughout the aligned volume. To examine whether poly-synaptic connectivity occurs greater than random, we first computed a population average synapse rate (λ ) normalized by potential synapse length, by dividing the total number of synapses reconstructed from the set of 50 functionally characterized neurons by their total pairwise L . We next compared λ for individual neuron pairs each connected by different numbers of synapses (Fig. 3b). This was used to assess whether multiple synapses occurred more often than predicted from a simple Poisson model. We examined the frequency of clustered vs distant synapses by comparing synapse pairs that were separated by >20 μm or <20 μm. For each synapse from each pair of neurons connected by n synapses, we computed the total L within 20 μm or beyond 20 μm from that synapse. We then took the fraction of the overlap beyond 20 μm: as the expected probability that each of the (n − 1) other synapses will occur >20 μm away. The expected number of distant synapse was taken as (n − 1) times the fraction of overlap beyond 20 μm, which was compared with the actual number of distant synapses observed (Fig. 3d). 3D renderings were generated using Blender (http://www.blender.org/) (Figs 1d, 2a, 3a, Extended Data Fig. 6, Supplementary Data 1–3), Imaris (Bitplane) (Extended Data Fig. 4 and Supplementary Video 1), and itk-SNAP (Fig. 4a). Cytoscape (http://www.cytoscape.org/) was used for network graph layouts (Figs 1f). Statistical methods were not used to predetermine sample sizes. Statistical comparisons between sample distributions were done with Permutation tests (that is, Monte Carlo-based Randomization tests) unless otherwise noted. Permutation tests were ideal as we do not assume the underlying distributions are normal, nor need the observations to be independent. For Permutation tests, we computed the incidence of differences between means or Pearson’s linear correlation coefficient of randomly drawn samples from combined sample distributions exceeding the empirical difference (Figs 2b–d, f, 4c and Extended Data Figs 7b, 9a, b, 10c, d). Cochran-Armitage two-sided tests for trend were used on proportional binned data with linear weights (Fig. 2b, f). Standard errors were calculated from bootstrapped sample distributions. For cumulative distributions (Figs 2c, d, and Extended Data Figs 9a, b, 10c, d), we repeatedly resampled by randomly drawing with replacement from the sample distribution the number of observed values 1,000–10,000 times and extracted the standard deviation at each step of the empirical CDF. For binned data (Fig. 2b, f, and Extended Data Fig. 9d), each resampled distribution was binned and the standard deviation was computed from the resampled probabilities or rates within each bin. Custom code is available upon request.


News Article
Site: http://www.scientificcomputing.com/rss-feeds/all/rss.xml/all

The security of the more than $7 billion in research funded by the National Science Foundation (NSF) will be significantly bolstered, thanks to a $5-million grant awarded to Indiana University, the National Center for Supercomputing Applications (NCSA), the Pittsburgh Supercomputing Center (PSC) and the University of Wisconsin-Madison for a collaborative effort to create the NSF Cybersecurity Center of Excellence. This funding will establish the Center for Trustworthy Scientific Cyberinfrastructure (CTSC), a three-year-old collaboration between the aforementioned institutions, as the NSF Cybersecurity Center of Excellence, an entity focused on addressing cybersecurity challenges of NSF scientific research. Ensuring scientific computing remains trustworthy and uncorrupted is essential in protecting the nation’s science. In its role as a Cybersecurity Center of Excellence, the CTSC will provide readily available cybersecurity services tailored to the NSF science community. These resources will include leadership and coordination across organizations, and education and training to expand the pool of available cybersecurity expertise. "NSF-funded cyberinfrastructure presents unique challenges for operational security personnel and impacts other important areas of research affecting society, including ocean sciences, natural hazards, engineering, biology and physics,” said Anita Nikolich, cybersecurity program director within NSF’s advanced cyberinfrastructure division. “Organizations that host cyberinfrastructure must find the right balance of security, privacy and usability while maintaining an environment in which data are openly shared. Many research organizations lack expertise in technical and policy security, and could benefit from an independent, shared security resource pool." The CTSC will collaborate directly with NSF-funded research organizations to address their cybersecurity challenges and provide forums for cybersecurity collaboration across organizations. For example, Jim Basney of the National Center for Supercomputing Applications will lead CTSC support activities on the topic of identity and access management for research organizations. “Cybersecurity is no longer solely a technical matter — it’s a critical part of any organization’s risk management,” said Von Welch, director of Indiana University’s Center for Applied Cybersecurity Research (CACR) and CTSC principal investigator. “Addressing the cybersecurity risks to science requires a comprehensive understanding of research and the threats it faces. Many of these threats are those faced by other organizations on the Internet, but others are unique to the science community with its collaborative nature and use of high-end information technology and cyberinfrastructure.” The CTSC will also convene an annual NSF Cybersecurity Summit, led by PSC Chief Information Security Officer James A. Marsteller, to share experiences, provide training and discuss cybersecurity challenges. “Organized with significant input from the NSF community, the annual Summit provides a key opportunity to share experiences, lessons learned and advances with other NSF projects,” Marsteller said. “The forum provides an opportunity to discuss serious issues around implementing cybersecurity not only of a technical nature, but also cultural, managerial and budgetary and the like.” An example of a safeguard the CTSC will promote is software assurance, as experienced, respected names in that field, such as Barton Miller, professor at University of Wisconsin-Madison, will offer their expertise to reduce the risks of vulnerabilities and breaches for researchers. “Every day, the news continues to document why truly excellent research in highly applied cybersecurity is a national priority,” said Brad Wheeler, IU vice president for information technology and interim dean of the IU School of Informatics and Computing. “This award adds to the many national distinctions that CACR has achieved in its 13 years as part of IU’s formidable cybersecurity capabilities in education, research and operations.” Additionally, the CTSC will collaborate with the U.S. Department of Energy’s Energy Science Network (ESnet) to develop a threat profile for open science. “The Department of Energy and NSF enable scientific discovery in a range of domains critical to our nation’s future,” said Greg Bell, director for ESnet and division director at the Lawrence Berkeley National Laboratory. “Working together to understand cybersecurity threat models shared by these collaborations is an important step forward for the two agencies, and ESnet is delighted to be collaborating on this effort.”


News Article | December 13, 2006
Site: arstechnica.com

Back when the Parallels beta was first announced in April (on the same day that Boot Camp was released), everyone started talking about the possibilities of running Mac and Windows apps side-by-side one day. Parallels made that easier for us, but at the time, we were still limited to this odd, virtual environment that many of us wished to escape. There is a little-known feature built into the newest beta for Parallels, though, that will undoubtedly make everyone's virtualization lives that much better, called Coherence. It allows you to hide the Windows desktop and run Windows apps, through Parallel, side-by-side with your OS X applications as if they were all running together in one, big, happy family. How is this possible? Why would you want to do that? Adam Pash of LifeHacker thought the same thing: He goes through a pretty detailed set of instructions for how to set it all up, and it's really not all that complex. None of his tips are even required in order to run Parallels in Coherence mode, they're just recommended in order to "help keep the line between Windows and your Mac pretty thin." His tips, after installing Parallels and a copy of Windows, include: Then, all you have to do is select "Coherence" from the View menu and you're all set to run programs from the two OSes side-by-side. You can even launch Windows apps from the Mac, which he details in his writeup as well. It makes use of a third-party app from VerySimple Dev since Parallels does not yet support this feature, but what Parallels does support now is "seamless drag and drop" between Windows and Mac. Oh Parallels gods, thank you for bestowing this wonderful feature upon us. If only Apple would stop denying rumors of including such virtualization abilities in Leopard, we could all die happy.

Discover hidden collaborations