Scanning Tunneling Spectroscopy on the novel superconductor CaC6

We present scanning tunneling microscopy and spectroscopy of the newly discovered superconductor CaC$_6$. The tunneling conductance spectra, measured between 3 K and 15 K, show a clear superconducting gap in the quasiparticle density of states. The gap function extracted from the spectra is in good agreement with the conventional BCS theory with $\Delta(0)$ = 1.6 $\pm$ 0.2 meV. The possibility of gap anisotropy and two-gap superconductivity is also discussed. In a magnetic field, direct imaging of the vortices allows to deduce a coherence length in the ab plane $\xi_{ab}\simeq$ 33 nm

Ancestral genome estimation reveals the history of ecological diversification in Agrobacterium

Horizontal gene transfer (HGT) is considered as a major source of innovation in bacteria, and as such is expected to drive adaptation to new ecological niches. However, among the many genes acquired through HGT along the diversification history of genomes, only a fraction may have actively contributed to sustained ecological adaptation. We used a phylogenetic approach accounting for the transfer of genes (or groups of genes) to estimate the history of genomes in Agrobacterium biovar 1, a diverse group of soil and plant-dwelling bacterial species. We identified clade-specific blocks of cotransferred genes encoding coherent biochemical pathways that may have contributed to the evolutionary success of key Agrobacterium clades. This pattern of gene coevolution rejects a neutral model of transfer, in which neighboring genes would be transferred independently of their function and rather suggests purifying selection on collectively coded acquired pathways. The acquisition of these synapomorphic blocks of cofunctioning genes probably drove the ecological diversification of Agrobacterium and defined features of ancestral ecological niches, which consistently hint at a strong selective role of host plant rhizospheres

IL-6 gene amplification and expression in human glioblastomas

The aggressiveness of human gliomas appears to be correlated with the upregulation of interleukin 6 (IL-6) gene. Using quantitative PCR methods, we detected amplification and expression of the IL-6 gene in 5 of 5 primary glioblastoma samples and in 4 of 5 glioblastoma cell lines. This finding suggests that the amplification of IL-6 gene may be a common feature in glioblastomas and may contribute to the IL-6 over-expression. © 2001 Cancer Research Campaign http://www.bjcancer.co

Interaction-based quantum metrology showing scaling beyond the Heisenberg limit

Quantum metrology studies the use of entanglement and other quantum resources to improve precision measurement. An interferometer using N independent particles to measure a parameter X can achieve at best the "standard quantum limit" (SQL) of sensitivity {\delta}X \propto N^{-1/2}. The same interferometer using N entangled particles can achieve in principle the "Heisenberg limit" {\delta}X \propto N^{-1}, using exotic states. Recent theoretical work argues that interactions among particles may be a valuable resource for quantum metrology, allowing scaling beyond the Heisenberg limit. Specifically, a k-particle interaction will produce sensitivity {\delta}X \propto N^{-k} with appropriate entangled states and {\delta}X \propto N^{-(k-1/2)} even without entanglement. Here we demonstrate this "super-Heisenberg" scaling in a nonlinear, non-destructive measurement of the magnetisation of an atomic ensemble. We use fast optical nonlinearities to generate a pairwise photon-photon interaction (k = 2) while preserving quantum-noise-limited performance, to produce {\delta}X \propto N^{-3/2}. We observe super-Heisenberg scaling over two orders of magnitude in N, limited at large N by higher-order nonlinear effects, in good agreement with theory. For a measurement of limited duration, super-Heisenberg scaling allows the nonlinear measurement to overtake in sensitivity a comparable linear measurement with the same number of photons. In other scenarios, however, higher-order nonlinearities prevent this crossover from occurring, reflecting the subtle relationship of scaling to sensitivity in nonlinear systems. This work shows that inter-particle interactions can improve sensitivity in a quantum-limited measurement, and introduces a fundamentally new resource for quantum metrology

Электронный рост нанообъектов Pb на поверхностях Si

Выполнены исследования особенностей формирования металлических наноостровков Pb на поверхности кремния методом сканирующей туннельной микроскопии. Показано, что рост наноостровков Pb на поверхности Si происходит в рамках модели Странски—Крастанова; вместе с тем формирование островков сопровождается их расслоением с характерным масштабом 2 нм (7 монослоёв Pb). Обнаруженное явление рассматривается в связи с минимизацией энергии в квантовых ямах, образующихся вследствие эффекта квантовой локализации, и объясняется в рамках модели электронного роста.Виконано дослідження особливостей формування металевих наноострівців Pb на поверхні кремнію методою сканівної тунельної мікроскопії. Показано, що ріст наноострівців Pb на поверхні Si відбувається в рамках моделю Странскі—Крастанова; разом з тим формування острівців супроводжується їх розшаруванням з характерним масштабом 2 нм (7 моношарів Pb). Виявлене явище розглядається в зв’язку з мінімізацією енергії в квантових ямах, які утворюються внаслідок ефекту квантової локалізації, та пояснюються в рамках моделю електронного росту.We report on Pb-islands growth on a surface of silicon. Using the scanning tunnelling microscopy, we show that, while in general the growth follows the Stranski—Krastanov scenario, the formation of Pb islands is accompanied by their lamination with a characteristic scale of two nanometers (7 monolayers of Pb). Such an effect manifests the energy minimum in quantum wells due to the quantum confinement, and it can be explained within the scope of the electronic-growth model

Cognitive loading affects motor awareness and movement kinematics but not locomotor trajectories during goal-directed walking in a virtual reality environment.

The primary purpose of this study was to investigate the effects of cognitive loading on movement kinematics and trajectory formation during goal-directed walking in a virtual reality (VR) environment. The secondary objective was to measure how participants corrected their trajectories for perturbed feedback and how participants' awareness of such perturbations changed under cognitive loading. We asked 14 healthy young adults to walk towards four different target locations in a VR environment while their movements were tracked and played back in real-time on a large projection screen. In 75% of all trials we introduced angular deviations of ±5° to ±30° between the veridical walking trajectory and the visual feedback. Participants performed a second experimental block under cognitive load (serial-7 subtraction, counter-balanced across participants). We measured walking kinematics (joint-angles, velocity profiles) and motor performance (end-point-compensation, trajectory-deviations). Motor awareness was determined by asking participants to rate the veracity of the feedback after every trial. In-line with previous findings in natural settings, participants displayed stereotypical walking trajectories in a VR environment. Our results extend these findings as they demonstrate that taxing cognitive resources did not affect trajectory formation and deviations although it interfered with the participants' movement kinematics, in particular walking velocity. Additionally, we report that motor awareness was selectively impaired by the secondary task in trials with high perceptual uncertainty. Compared with data on eye and arm movements our findings lend support to the hypothesis that the central nervous system (CNS) uses common mechanisms to govern goal-directed movements, including locomotion. We discuss our results with respect to the use of VR methods in gait control and rehabilitation

3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI

Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various neurological diseases, including stroke and dementia. Automated quantification of EPVS would greatly help to advance research into its etiology and its potential as a risk indicator of disease. We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI. We first segment the basal ganglia and subsequently apply a 3D convolutional regression network designed for small object detection within this region of interest. The network takes an image as input, and outputs a quantification score of EPVS. The network has significantly more convolution operations than pooling ones and no final activation, allowing it to span the space of real numbers. We validated our approach using a dataset of 2000 brain MRI scans scored visually. Experiments with varying sizes of training and test sets showed that a good performance can be achieved with a training set of only 200 scans. With a training set of 1000 scans, the intraclass correlation coefficient (ICC) between our scoring method and the expert's visual score was 0.74. Our method outperforms by a large margin - more than 0.10 - four more conventional automated approaches based on intensities, scale-invariant feature transform, and random forest. We show that the network learns the structures of interest and investigate the influence of hyper-parameters on the performance. We also evaluate the reproducibility of our network using a set of 60 subjects scanned twice (scan-rescan reproducibility). On this set our network achieves an ICC of 0.93, while the intrarater agreement reaches 0.80. Furthermore, the automated EPVS scoring correlates similarly to age as visual scoring