Up-down symmetry of the turbulent transport of toroidal angular momentum in tokamaks

Two symmetries of the local nonlinear delta-f gyrokinetic system of equations in tokamaks in the high flow regime are presented. The turbulent transport of toroidal angular momentum changes sign under an up-down reflection of the tokamak and a sign change of both the rotation and the rotation shear. Thus, the turbulent transport of toroidal angular momentum must vanish for up-down symmetric tokamaks in the absence of both rotation and rotation shear. This has important implications for the modeling of spontaneous rotation.Comment: 15 pages, 2 figure

Hemodynamic and electrophysiological evidence of resting-state network activity in the primate

An expanding body of literature describes the existence of concerted brain activations in the absence of any external stimuli. Resting-state networks have been identified and demonstrated to be modulated during the performance of specific cognitive operations. However, despite mounting evidence the possibility still remains that those correlated signal fluctuations reflect non-neural phenomena. In order to isolate functionally relevant spontaneous coactivations, we utilized a multi-level sampling approach to obtain co-registered brain signals across a range of sampling resolution and sensitivity. Surface and local field potentials, hemodynamic signals (near-infrared spectroscopy, NIRS), and cell spiking were recorded from dorsolateral prefrontal and posterior parietal cortices in four monkeys trained to remain motionless in a primate chair. The use of an optical recording technique (NIRS) allows measurement of a signal that is physiologically equivalent to that obtained using BOLD fMRI, though with millisecond temporal resolution and minimal technical or environmental constraints. The different signal types exhibited correlations between the two regions of interest in both the frequency and time domains. This evidence suggests that the resting-state network activations detected by fMRI do in fact reflect functional coactivations of areas across multiple levels of network communication

Functional differentiation within the monkey cortex as revealed by near-infrared spectroscopy

The role of prefrontal cortex in working memory (WM) is well established. However, questions remain regarding the topography and “domain-specific differentiation” of different types of information processing in the cortex. While it has been theorized that dorsolateral (DPFC) and ventrolateral (VPFC) prefrontal cortex preferentially process spatial and object WM, respectively, both electrophysiological evidence in the monkey and neuroimaging in the human have largely failed to demonstrate such regional differentiation. In this study we use near-infrared spectroscopy (NIRS) to detect functional changes, across relatively large cortical cell populations, simultaneously from prefrontal and posterior parietal cortices. Imaging data were recorded from a Rhesus macaque performing two types of WM tasks: a spatial task in which the animal had to retain the spatial position of a visual stimulus, and a non-spatial task where he had to retain its color (red or green) during a 20s delay. During performance of the spatial WM task, cerebral activation trends were found in which DPFC exhibited stronger activation than did the VPFC, and posterior parietal cortex maintained higher delay activation than did frontal regions. These differences were less pronounced during performance of the non-spatial task. Additionally, incorrect trials generally elicited lower activations during the delay period than did trials ending with a correct response. Furthermore, NIRS data collected during the performance of a haptic WM task also appear to exhibit inter-regional differences in delay activation. The data thus suggest the presence of preferential cognitive processing between and within posterior and frontal cortical regions

MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting

We propose novel statistics which maximise the power of a two-sample test based on the Maximum Mean Discrepancy (MMD), by adapting over the set of kernels used in defining it. For finite sets, this reduces to combining (normalised) MMD values under each of these kernels via a weighted soft maximum. Exponential concentration bounds are proved for our proposed statistics under the null and alternative. We further show how these kernels can be chosen in a data-dependent but permutation-independent way, in a well-calibrated test, avoiding data splitting. This technique applies more broadly to general permutation-based MMD testing, and includes the use of deep kernels with features learnt using unsupervised models such as auto-encoders. We highlight the applicability of our MMD-Fuse tests on both synthetic low-dimensional and real-world high-dimensional data, and compare its performance in terms of power against current state-of-the-art kernel tests

Perceived organisational politics, political behaviour and employee commitment in the Wenchi Municipal Assembly, Ghana

This study assesses the extent to which employee perceptions of organisational politics influence their commitment in the public sector of Ghana. Three standard scales were adopted for generating data for the study namely; perceptions of organisational politics scale, employee commitment questionnaire and the political behaviour scale. Data were processed using the IBM Statistical Product and Service Solution’s Version 19.0. The partial least squares structural equation modeling was used to measure the relationship between organisational politics and employee commitment. The mediating effect of political behaviour on this relationship was also measured using the same partial least squares structural equation modeling. A sample of 120 employees was selected from the Wenchi Municipal Assembly for the study. The results of the study indicate that employee perceptions of organisational politics have a positively significant relationship with their commitment in the public sector in Ghana. It was therefore recommended for management to reconsider the individual, group and organisational circumstances that generate perceived organisational politics in order to curtail any unwanted political behaviour in the Assembly.Keywords: Perceptions, Organisational Politics, Political Behaviour, Employee Commitmen

Population imaging of synaptically released glutamate in mouse hippocampal slices

Glutamatergic neurotransmission is a widespread form of synaptic excitation in the mammalian brain. The development of genetically encoded fluorescent glutamate sensors allows monitoring synaptic signaling in living brain tissue in real time. Here, we describe single-and two-photon imaging of synaptically evoked glutamatergic population signals in acute hippocampal slices express-ing the fluorescent glutamate sensor SF-iGluSnFR.A184S in CA1 or CA3 pyra-midal neurons. The protocol can be readily used to study defective synaptic glutamate signaling in mouse models of neuropsychiatric disorders, such as Alzheimer disease. For complete details on the use and execution of this protocol, please refer to Zott et al. (2019)

MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting

We propose novel statistics which maximise the power of a two-sample test based on the Maximum Mean Discrepancy (MMD), by adapting over the set of kernels used in defining it. For finite sets, this reduces to combining (normalised) MMD values under each of these kernels via a weighted soft maximum. Exponential concentration bounds are proved for our proposed statistics under the null and alternative. We further show how these kernels can be chosen in a data-dependent but permutation-independent way, in a well-calibrated test, avoiding data splitting. This technique applies more broadly to general permutation-based MMD testing, and includes the use of deep kernels with features learnt using unsupervised models such as auto-encoders. We highlight the applicability of our MMD-FUSE test on both synthetic low-dimensional and real-world high-dimensional data, and compare its performance in terms of power against current state-of-the-art kernel tests.Comment: 38 pages,8 figures, 1 tabl

Accurate reduced models for the pH oscillations in the urea-urease reaction confined to giant lipid vesicles

This theoretical study concerns a pH oscillator based on the urea-urease reaction confined to giant lipid vesicles. Under suitable conditions, differential transport of urea and hydrogen ion across the unilamellar vesicle membrane periodically resets the pH clock that switches the system from acid to basic, resulting in self-sustained oscillations. We analyse the structure of the phase flow and of the limit cycle, which controls the dynamics for giant vesicles and dominates the pronouncedly stochastic oscillations in small vesicles of submicrometer size. To this end, we derive reduced models, which are amenable to analytic treatments that are complemented by numerical solutions, and obtain the period and amplitude of the oscillations as well as the parameter domain, where oscillatory behavior persists. We show that the accuracy of these predictions is highly sensitive to the employed reduction scheme. In particular, we suggest an accurate two-variable model and show its equivalence to a three-variable model that admits an interpretation in terms of a chemical reaction network. The faithful modeling of a single pH oscillator appears crucial for rationalizing experiments and understanding communication of vesicles and synchronization of rhythms.Comment: submitted J. Phys. Chem.