Reaching the hydrodynamic regime in a Bose-Einstein condensate by suppression of avalanche

We report the realization of a Bose-Einstein condensate (BEC) in the hydrodynamic regime. The hydrodynamic regime is reached by evaporative cooling at a relative low density suppressing the effect of avalanches. With the suppression of avalanches a BEC containing 120.10^6 atoms is produced. The collisional opacity can be tuned from the collisionless regime to a collisional opacity of more than 3 by compressing the trap after condensation. In the collisional opaque regime a significant heating of the cloud at time scales shorter than half of the radial trap period is measured. This is direct proof that the BEC is hydrodynamic.Comment: Article submitted for Phys. Rev. Letters, 6 figure

Large atom number Bose-Einstein condensate of sodium

We describe the setup to create a large Bose-Einstein condensate containing more than 120x10^6 atoms. In the experiment a thermal beam is slowed by a Zeeman slower and captured in a dark-spot magneto-optical trap (MOT). A typical dark-spot MOT in our experiments contains 2.0x10^10 atoms with a temperature of 320 microK and a density of about 1.0x10^11 atoms/cm^3. The sample is spin polarized in a high magnetic field, before the atoms are loaded in the magnetic trap. Spin polarizing in a high magnetic field results in an increase in the transfer efficiency by a factor of 2 compared to experiments without spin polarizing. In the magnetic trap the cloud is cooled to degeneracy in 50 s by evaporative cooling. To suppress the 3-body losses at the end of the evaporation the magnetic trap is decompressed in the axial direction.Comment: 11 pages, 12 figures, submitted to Review Of Scientific Instrument

Licorice consumption as a cause of posterior reversible encephalopathy syndrome: a case report

INTRODUCTION: A 49-year-old woman was admitted to our hospital because of thunderclap headache and blurred vision. At the time of presentation, her blood pressure was 219/100 mmHg, her arterial pH was 7.64 and her potassium level was 2.7 mM/l. METHODS: The combination of sequential computed tomography (CT) and the triad of hypertension, hypokalemia and metabolic alkalosis in this patient suggested the diagnosis. Supplementary anamnesis and long-term follow-up confirmed it. RESULTS: Brain computed tomography imaging showed minor bleeding in the left Sylvian fissure and bilateral occipital edema, suggestive of posterior reversible encephalopathy syndrome (PRES). Repeated brain CT after 10 days showed a complete resolution of radiological signs. The patient informed us that she had quit smoking 2 weeks ago and had started consuming large amounts of licorice instead of smoking. After she abandoned licorice consumption, her blood pressure normalized. Her latest blood pressure reading was 106/60 mmHg without the use of any antihypertensive drugs. CONCLUSIONS: To the best of our knowledge, this is the first case report describing licorice consumption as a cause of PRES. Glycyrrhizic acid, a component of licorice, inhibits 11β-hydroxysteroid dehydrogenase and subsequently causes mineralocorticoid excess. Mineralocorticoid excess in turn causes high blood pressure and ultimately gives rise to malignant hypertension. Physicians should remember that licorice use is a very easy-to-treat cause of hypertension, hypertensive encephalopathy and PRES

Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory

Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The explicit form of that influence depends on the type of network topology, which is usually unknown for experimental data. Direct comparisons of graph measures between empirical networks with different N and/or k can therefore yield spurious results. We list benefits and pitfalls of various approaches that intend to overcome these difficulties. We discuss the initial graph definition of unweighted graphs via fixed thresholds, average degrees or edge densities, and the use of weighted graphs. For instance, choosing a threshold to fix N and k does eliminate size and density effects but may lead to modifications of the network by enforcing (ignoring) non-significant (significant) connections. Opposed to fixing N and k, graph measures are often normalized via random surrogates but, in fact, this may even increase the sensitivity to differences in N and k for the commonly used clustering coefficient and small-world index. To avoid such a bias we tried to estimate the N,k-dependence for empirical networks, which can serve to correct for size effects, if successful. We also add a number of methods used in social sciences that build on statistics of local network structures including exponential random graph models and motif counting. We show that none of the here-investigated methods allows for a reliable and fully unbiased comparison, but some perform better than others

Emergence of Modular Structure in a Large-Scale Brain Network with Interactions between Dynamics and Connectivity

A network of 32 or 64 connected neural masses, each representing a large population of interacting excitatory and inhibitory neurons and generating an electroencephalography/magnetoencephalography like output signal, was used to demonstrate how an interaction between dynamics and connectivity might explain the emergence of complex network features, in particular modularity. Network evolution was modeled by two processes: (i) synchronization dependent plasticity (SDP) and (ii) growth dependent plasticity (GDP). In the case of SDP, connections between neural masses were strengthened when they were strongly synchronized, and were weakened when they were not. GDP was modeled as a homeostatic process with random, distance dependent outgrowth of new connections between neural masses. GDP alone resulted in stable networks with distance dependent connection strengths, typical small-world features, but no degree correlations and only weak modularity. SDP applied to random networks induced clustering, but no clear modules. Stronger modularity evolved only through an interaction of SDP and GDP, with the number and size of the modules depending on the relative strength of both processes, as well as on the size of the network. Lesioning part of the network, after a stable state was achieved, resulted in a temporary disruption of the network structure. The model gives a possible scenario to explain how modularity can arise in developing brain networks, and makes predictions about the time course of network changes during development and following acute lesions

Business Ownership and Unemployment in Japan

The influence of industrial structure, more specifically of business ownership, is investigated on the level of unemployment in Japan. The question is to what extent business ownership, i.e., entrepreneurship, can reduce the level of unemployment. It will be concluded that Japan is hardly an outlier when using a simple model of the relationship between unemployment and the rate of business ownership. The model is calibrated using recent data of 23 OECD countries. It shows a minor underestimation of the rise in unemployment in Japan in the period 1984-2002. Arguments are brought forward why this might be the case. We argue that small firms in Japan have benefitted in the past from the protective environment of the keiretsu structure. In the current process of industrial restructuring, keiretsu support is dissipating, but has not yet been adequatly replaced with a market environment conducive to the establishment and growth of entrepreneurial firms. The underestimation of the rise in unemployment is a reflection of the limited access of small firms to the market in Japan

Dynamic cerebral autoregulation in acute lacunar and middle cerebral artery territory ischemic stroke

Background and Purpose - We addressed whether dynamic cerebral autoregulation (dCA) is affected in middle cerebral artery (MCA) territory (MCAS) and lacunar ischemic stroke (LS). Methods - Blood pressure (MAP) and MCA velocity (V) were measured in 10 patients with large MCAS (National Institutes of Health Stroke score, 17 +/- 2; mean +/- SEM), in 10 with LS (score, 9 +/- 1), and in 10 reference subjects. dCA was evaluated in time (delay of the MCA V-mean counter-regulation during changes in MAP) and frequency domains (cross-spectral MCA V-mean-to-MAP phase lead). Results - In reference subjects, latencies for MAP increments (5.3 +/- 0.5 seconds) and decrements (5.6 +/- 0.5 seconds) were comparable, and low frequency MCA V-mean-to-MAP phase lead was 56 +/- 5 and 59 +/- 5 degrees (left and right hemisphere). In MCAS, these latencies were 4.6 +/- 0.7 and 5.6 +/- 0.5 seconds in the nonischemic hemisphere and not detectable in the ischemic hemisphere. In the unaffected hemisphere, phase lead was 61 +/- 6 degrees versus 26 +/- 6 degrees on the ischemic side (P <0.05). In LS, no latency and smaller phase lead bilaterally (32 +/- 6 and 33 +/- 5 degrees) conformed to globally impaired dCA. Conclusions - In large MCAS infarcts, dynamic cerebral autoregulation was impaired in the affected hemisphere. In LS, dynamic cerebral autoregulation was impaired bilaterally, a finding consistent with the hypothesis of bilateral small vessel disease in patients with lacunar infarct