High iron and iron household protein contents in perineuronal net-ensheathed neurons ensure energy metabolism with safe iron handling

A subpopulation of neurons is less vulnerable against iron-induced oxidative stress and neurodegeneration. A key feature of these neurons is a special extracellular matrix composition that forms a perineuronal net (PN). The PN has a high affinity to iron, which suggests an adapted iron sequestration and metabolism of the ensheathed neurons. Highly active, fast-firing neurons—which are often ensheathed by a PN—have a particular high metabolic demand, and therefore may have a higher need in iron. We hypothesize that PN-ensheathed neurons have a higher intracellular iron concentration and increased levels of iron proteins. Thus, analyses of cellular and regional iron and the iron proteins transferrin (Tf), Tf receptor 1 (TfR), ferritin H/L (FtH/FtL), metal transport protein 1 (MTP1 aka ferroportin), and divalent metal transporter 1 (DMT1) were performed on Wistar rats in the parietal cortex (PC), subiculum (SUB), red nucleus (RN), and substantia nigra (SNpr/SNpc). Neurons with a PN (PN+) have higher iron concentrations than neurons without a PN: PC 0.69 mM vs. 0.51 mM, SUB 0.84 mM vs. 0.69 mM, SN 0.71 mM vs. 0.63 mM (SNpr)/0.45 mM (SNpc). Intracellular Tf, TfR and MTP1 contents of PN+ neurons were consistently increased. The iron concentration of the PN itself is not increased. We also determined the percentage of PN+ neurons: PC 4%, SUB 5%, SNpr 45%, RN 86%. We conclude that PN+ neurons constitute a subpopulation of resilient pacemaker neurons characterized by a bustling iron metabolism and outstanding iron handling capabilities. These properties could contribute to the low vulnerability of PN+ neurons against iron-induced oxidative stress and degeneration

Iron concentrations in neurons and glial cells with estimates on ferritin concentrations

BACKGROUND: Brain iron is an essential as well as a toxic redox active element. Physiological levels are not uniform among the different cell types. Besides the availability of quantitative methods, the knowledge about the brain iron lags behind. Thereby, disclosing the mechanisms of brain iron homeostasis helps to understand pathological iron-accumulations in diseased and aged brains. With our study we want to contribute closing the gap by providing quantitative data on the concentration and distribution of iron in neurons and glial cells in situ. Using a nuclear microprobe and scanning proton induced X-ray emission spectrometry we performed quantitative elemental imaging on rat brain sections to analyze the iron concentrations of neurons and glial cells. RESULTS: Neurons were analyzed in the neocortex, subiculum, substantia nigra and deep cerebellar nuclei revealing an iron level between [Formula: see text] and [Formula: see text]. The iron concentration of neocortical oligodendrocytes is fivefold higher, of microglia threefold higher and of astrocytes twofold higher compared to neurons. We also analyzed the distribution of subcellular iron concentrations in the cytoplasm, nucleus and nucleolus of neurons. The cytoplasm contains on average 73 of the total iron, the nucleolus-although a hot spot for iron-due to its small volume only 6 of total iron. Additionally, the iron level in subcellular fractions were measured revealing that the microsome fraction, which usually contains holo-ferritin, has the highest iron content. We also present an estimate of the cellular ferritin concentration calculating [Formula: see text] ferritin molecules per [Formula: see text] in rat neurons. CONCLUSION: Glial cells are the most iron-rich cells in the brain. Imbalances in iron homeostasis that lead to neurodegeneration may not only be originate from neurons but also from glial cells. It is feasible to estimate the ferritin concentration based on measured iron concentrations and a reasonable assumptions on iron load in the brain

In-Q-Tel: The Central Intelligence Agency as Venture Capitalist

The Central Intelligence Agency (CIA), the United States’ principal foreign intelligence and spy organization, chartered the first government-sponsored venture capital firm, dubbed In-Q-Tel, in February 1999. In-Q-Tel represents the twenty-first century fusion of U.S. spy efforts with the venture capital industry. Envisioned as a platform to expand the research and development (R&D) efforts of the CIA into the private sector, In-Q-Tel uses CIA-supplied funds to make strategic investments in startup companies developing commercially focused technologies that are of interest to the CIA and greater intelligence community. This Comment contends that, although R&D collaboration between the public and private sectors is vital and should be encouraged, such collaboration should not be in the form of a venture capital firm chartered and sponsored by the CIA. The CIA is not equipped to succeed in the notoriously perilous business of venture capital, and heightened ethical concerns surround the making of government-sponsored equity investments in private companies. Indeed, In-Q-Tel often invests in companies with international operations, vicariously and unnecessarily exposing the CIA and larger U.S. government to foreign entanglements. This Comment begins by tracing relevant developments in the funding of U.S. spy efforts in Part II. Next, Part III explores the venture capital industry, paying particular attention to the interplay between venture capital and R&D. Part IV then analyzes the relationship between the CIA and In-Q-Tel. Finally, Part V: (1) contends the risks of In-Q-Tel currently outweigh its benefits; (2) suggests the current In-Q-Tel model inappropriately exposes the CIA and larger U.S. government to disputes arising from private international law; and (3) proposes alternative courses of action by which the CIA may tap into the R&D efforts of the private sector. Part VI concludes this Comment

Efficient method for estimating the number of communities in a network

While there exist a wide range of effective methods for community detection in networks, most of them require one to know in advance how many communities one is looking for. Here we present a method for estimating the number of communities in a network using a combination of Bayesian inference with a novel prior and an efficient Monte Carlo sampling scheme. We test the method extensively on both real and computer-generated networks, showing that it performs accurately and consistently, even in cases where groups are widely varying in size or structure.Comment: 13 pages, 4 figure

Development of General Guidelines for the Planning of Stormwater Management Facilities: Application to Urban Watersheds in Kentucky

This report provides a planning methodology and a design tool to help determine the appropriate location and volume of detention basins required to control critical storm events. The technique involves using watershed characteristics including the SCS curve number, time of concentration, peak outflow rate, watershed area and the storage recurrence interval to help predict these detention volumes. Historical rainfall records are used in a revised continuous simulation program (SYNOP, Hydroscience, Inc,) to determine the rainfall excess from which runoff hydrographs are produced. Various combinations of the watershed characteristics were input and computer analyses done to obtain the required data base. A statistical analysis is performed in each computer analysis to obtain the statistics on the required volume. Graphs were drawn from these statistical results as functions of the watershed characteristics and the release rate. Entering the graphs with the governing watershed characteristics, the designer can obtain.a good estimate of the detention basin volume required

High-Throughput Automated Olfactory Phenotyping of Group-Housed Mice

Behavioral phenotyping of mice is often compromised by manual interventions of the experimenter and limited throughput. Here, we describe a fully automated behavior setup that allows for quantitative analysis of mouse olfaction with minimized experimenter involvement. Mice are group-housed and tagged with unique RFID chips. They can freely initiate trials and are automatically trained on a go/no-go task, learning to distinguish a rewarded from an unrewarded odor. Further, odor discrimination tasks and detailed training aspects can be set for each animal individually for automated execution without direct experimenter intervention. The procedure described here, from initial RFID implantation to discrimination of complex odor mixtures at high accuracy, can be completed within <2 months with cohorts of up to 25 male mice. Apart from the presentation of monomolecular odors, the setup can generate arbitrary mixtures and dilutions from any set of odors to create complex stimuli, enabling demanding behavioral analyses at high-throughput

Momentum-resolved evolution of the Kondo lattice into 'hidden-order' in URu2Si2

We study, using high-resolution angle-resolved photoemission spectroscopy, the evolution of the electronic structure in URu2Si2 at the Gamma, Z and X high-symmetry points from the high-temperature Kondo-screened regime to the low-temperature `hidden-order' (HO) state. At all temperatures and symmetry points, we find structures resulting from the interaction between heavy and light bands, related to the Kondo lattice formation. At the X point, we directly measure a hybridization gap of 11 meV already open at temperatures above the ordered phase. Strikingly, we find that while the HO induces pronounced changes at Gamma and Z, the hybridization gap at X does not change, indicating that the hidden-order parameter is anisotropic. Furthermore, at the Gamma and Z points, we observe the opening of a gap in momentum in the HO state, and show that the associated electronic structure results from the hybridization of a light electron band with the Kondo-lattice bands characterizing the paramagnetic state.Comment: Updated published version. Mansucript + Supplemental Material (8 pages, 9 figures). Submitted 16 September 201

Iron concentrations in neurons and glial cells with estimates on ferritin concentrations

BACKGROUND: Brain iron is an essential as well as a toxic redox active element. Physiological levels are not uniform among the different cell types. Besides the availability of quantitative methods, the knowledge about the brain iron lags behind. Thereby, disclosing the mechanisms of brain iron homeostasis helps to understand pathological iron-accumulations in diseased and aged brains. With our study we want to contribute closing the gap by providing quantitative data on the concentration and distribution of iron in neurons and glial cells in situ. Using a nuclear microprobe and scanning proton induced X-ray emission spectrometry we performed quantitative elemental imaging on rat brain sections to analyze the iron concentrations of neurons and glial cells. RESULTS: Neurons were analyzed in the neocortex, subiculum, substantia nigra and deep cerebellar nuclei revealing an iron level between [Formula: see text] and [Formula: see text]. The iron concentration of neocortical oligodendrocytes is fivefold higher, of microglia threefold higher and of astrocytes twofold higher compared to neurons. We also analyzed the distribution of subcellular iron concentrations in the cytoplasm, nucleus and nucleolus of neurons. The cytoplasm contains on average 73 of the total iron, the nucleolus-although a hot spot for iron-due to its small volume only 6 of total iron. Additionally, the iron level in subcellular fractions were measured revealing that the microsome fraction, which usually contains holo-ferritin, has the highest iron content. We also present an estimate of the cellular ferritin concentration calculating [Formula: see text] ferritin molecules per [Formula: see text] in rat neurons. CONCLUSION: Glial cells are the most iron-rich cells in the brain. Imbalances in iron homeostasis that lead to neurodegeneration may not only be originate from neurons but also from glial cells. It is feasible to estimate the ferritin concentration based on measured iron concentrations and a reasonable assumptions on iron load in the brain