Representative bureaucracy: does female police leadership affect gender-based violence arrests?

Representative bureaucracy theory postulates that passive representation leads to active representation of minority groups. This article investigates the passive representation of female police officers at leadership levels and the active representation of women vis-a-vis gender-based violence arrest rates in the UK. Much of the extant research on representative bureaucracy is located at street level, with evidence showing that discretionary power of minority bureaucrats can lead to active representation. This article is focused on leadership levels of a public bureaucracy. The empirical research is based upon a panel dataset of female police officers as an independent variable and gender-based violence arrest rates as a dependent variable. The analysis reveals that there is little evidence of active representation of women by female police leadership

An F-ratio-Based Method for Estimating the Number of Active Sources in MEG

Magnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordings remains a challenging problem due to the low signal-to-noise ratio (SNR), the presence of correlated sources, inaccuracies in head modeling, and variations in individual anatomy. To address these issues, our study introduces a robust method for accurately estimating the number of active sources in the brain based on the F-ratio statistical approach, which allows for a comparison between a full model with a higher number of sources and a reduced model with fewer sources. Using this approach, we developed a formal statistical procedure that sequentially increases the number of sources in the multiple dipole localization problem until all sources are found. Our results revealed that the selection of thresholds plays a critical role in determining the method`s overall performance, and appropriate thresholds needed to be adjusted for the number of sources and SNR levels, while they remained largely invariant to different inter-source correlations, modeling inaccuracies, and different cortical anatomies. By identifying optimal thresholds and validating our F-ratio-based method in simulated, real phantom, and human MEG data, we demonstrated the superiority of our F-ratio-based method over existing state-of-the-art statistical approaches, such as the Akaike Information Criterion (AIC) and Minimum Description Length (MDL). Overall, when tuned for optimal selection of thresholds, our method offers researchers a precise tool to estimate the true number of active brain sources and accurately model brain function

Time-frequency strategies for increasing high frequency oscillation detectability in intracerebral EEG

International audienceBackground: High Frequency Oscillations (HFOs) are considered to be highly representative of brain tissues capable of producing epileptic seizures. The visual review of HFOs on intracerebral electroencephalography is time-consuming and tedious, and it can be improved by time-frequency (TF) analysis. The main issue is that the signal is dominated by lower frequencies that mask the HFOs. Our aim was to flatten (i.e. whiten) the frequency spectrum to enhance the fast oscillations while preserving an optimal Signal to Noise Ratio (SNR). Method: We investigated 8 methods of data whitening based on either prewhitening or TF normalization in order to improve the detectability of HFOs. We detected all local maxima of the TF image above a range of thresholds in the HFO band. Results: We obtained the Precision and Recall curves at different SNR and for different HFO types and illustrate the added value of whitening both in the time-frequency plane and in time domain. Conclusion: The normalization strategies based on a baseline and on our proposed method (the " H0 z-score ") are more precise than the others. Significance: The H0 z-score provides an optimal framework for representing and detecting HFOs, independent of a baseline and a priori frequency bands

Seismic velocities within the sedimentary succession of the Canada Basin and southern Alpha-Mendeleev Ridge, Arctic Ocean : evidence for accelerated porosity reduction?

Author Posting. © Crown Copyright, 2015. This article is posted here by permission of Oxford University Press for personal use, not for redistribution. The definitive version was published in Geophysical Journal International 204 (2016): 1-20, doi:10.1093/gji/ggv416.The Canada Basin and the southern Alpha-Mendeleev ridge complex underlie a significant proportion of the Arctic Ocean, but the geology of this undrilled and mostly ice-covered frontier is poorly known. New information is encoded in seismic wide-angle reflections and refractions recorded with expendable sonobuoys between 2007 and 2011. Velocity–depth samples within the sedimentary succession are extracted from published analyses for 142 of these records obtained at irregularly spaced stations across an area of 1.9E + 06 km2. The samples are modelled at regional, subregional and station-specific scales using an exponential function of inverse velocity versus depth with regionally representative parameters determined through numerical regression. With this approach, smooth, non-oscillatory velocity–depth profiles can be generated for any desired location in the study area, even where the measurement density is low. Practical application is demonstrated with a map of sedimentary thickness, derived from seismic reflection horizons interpreted in the time domain and depth converted using the velocity–depth profiles for each seismic trace. A thickness of 12–13 km is present beneath both the upper Mackenzie fan and the middle slope off of Alaska, but the sedimentary prism thins more gradually outboard of the latter region. Mapping of the observed-to-predicted velocities reveals coherent geospatial trends associated with five subregions: the Mackenzie fan; the continental slopes beyond the Mackenzie fan; the abyssal plain; the southwestern Canada Basin; and, the Alpha-Mendeleev magnetic domain. Comparison of the subregional velocity–depth models with published borehole data, and interpretation of the station-specific best-fitting model parameters, suggests that sandstone is not a predominant lithology in any of the five subregions. However, the bulk sand-to-shale ratio likely increases towards the Mackenzie fan, and the model for this subregion compares favourably with borehole data for Miocene turbidites in the eastern Gulf of Mexico. The station-specific results also indicate that Quaternary sediments coarsen towards the Beaufort-Mackenzie and Banks Island margins in a manner that is consistent with the variable history of Laurentide Ice Sheet advance documented for these margins. Lithological factors do not fully account for the elevated velocity–depth trends that are associated with the southwestern Canada Basin and the Alpha-Mendeleev magnetic domain. Accelerated porosity reduction due to elevated palaeo-heat flow is inferred for these regions, which may be related to the underlying crustal types or possibly volcanic intrusion of the sedimentary succession. Beyond exploring the variation of an important physical property in the Arctic Ocean basin, this study provides comparative reference for global studies of seismic velocity, burial history, sedimentary compaction, seismic inversion and overpressure prediction, particularly in mudrock-dominated successions

An F-ratio-based method for estimating the number of active sources in MEG

IntroductionMagnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordings remains a challenging problem due to the low signal-to-noise ratio (SNR), the presence of correlated sources, inaccuracies in head modeling, and variations in individual anatomy.MethodsTo address these issues, our study introduces a robust method for accurately estimating the number of active sources in the brain based on the F-ratio statistical approach, which allows for a comparison between a full model with a higher number of sources and a reduced model with fewer sources. Using this approach, we developed a formal statistical procedure that sequentially increases the number of sources in the multiple dipole localization problem until all sources are found.ResultsOur results revealed that the selection of thresholds plays a critical role in determining the method's overall performance, and appropriate thresholds needed to be adjusted for the number of sources and SNR levels, while they remained largely invariant to different inter-source correlations, translational modeling inaccuracies, and different cortical anatomies. By identifying optimal thresholds and validating our F-ratio-based method in simulated, real phantom, and human MEG data, we demonstrated the superiority of our F-ratio-based method over existing state-of-the-art statistical approaches, such as the Akaike Information Criterion (AIC) and Minimum Description Length (MDL).DiscussionOverall, when tuned for optimal selection of thresholds, our method offers researchers a precise tool to estimate the true number of active brain sources and accurately model brain function

Microtesla MRI of the human brain combined with MEG

One of the challenges in functional brain imaging is integration of complementary imaging modalities, such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). MEG, which uses highly sensitive superconducting quantum interference devices (SQUIDs) to directly measure magnetic fields of neuronal currents, cannot be combined with conventional high-field MRI in a single instrument. Indirect matching of MEG and MRI data leads to significant co-registration errors. A recently proposed imaging method - SQUID-based microtesla MRI - can be naturally combined with MEG in the same system to directly provide structural maps for MEG-localized sources. It enables easy and accurate integration of MEG and MRI/fMRI, because microtesla MR images can be precisely matched to structural images provided by high-field MRI and other techniques. Here we report the first images of the human brain by microtesla MRI, together with auditory MEG (functional) data, recorded using the same seven-channel SQUID system during the same imaging session. The images were acquired at 46 microtesla measurement field with pre-polarization at 30 mT. We also estimated transverse relaxation times for different tissues at microtesla fields. Our results demonstrate feasibility and potential of human brain imaging by microtesla MRI. They also show that two new types of imaging equipment - low-cost systems for anatomical MRI of the human brain at microtesla fields, and more advanced instruments for combined functional (MEG) and structural (microtesla MRI) brain imaging - are practical.Comment: 8 pages, 5 figures - accepted by JM

Interactions of thrombospondins with α4β1 integrin and CD47 differentially modulate T cell behavior

Thrombospondin (TSP)-1 has been reported to modulate T cell behavior both positively and negatively. We found that these opposing responses arise from interactions of TSP1 with two different T cell receptors. The integrin α4β1 recognizes an LDVP sequence in the NH2-terminal domain of TSP1 and was required for stimulation of T cell adhesion, chemotaxis, and matrix metalloproteinase gene expression by TSP1. Recognition of TSP1 by T cells depended on the activation state of α4β1 integrin, and TSP1 inhibited interaction of activated α4β1 integrin on T cells with its counter receptor vascular cell adhesion molecule-1. The α4β1 integrin recognition site is conserved in TSP2. A recombinant piece of TSP2 containing this sequence replicated the α4β1 integrin–dependent activities of TSP1. The β1 integrin recognition sites in TSP1, however, were neither necessary nor sufficient for inhibition of T cell proliferation and T cell antigen receptor signaling by TSP1. A second TSP1 receptor, CD47, was not required for some stimulatory responses to TSP1 but played a significant role in its T cell antigen receptor antagonist and antiproliferative activities. Modulating the relative expression or function of these two TSP receptors could therefore alter the direction or magnitude of T cell responses to TSPs

Multi-Channel SQUID System for MEG and Ultra-Low-Field MRI

A seven-channel system capable of performing both magnetoencephalography (MEG) and ultra-low-field magnetic resonance imaging (ULF MRI) is described. The system consists of seven second-order SQUID gradiometers with 37 mm diameter and 60 mm baseline, having magnetic field resolution of 1.2-2.8 fT/rtHz. It also includes four sets of coils for 2-D Fourier imaging with pre-polarization. The system's MEG performance was demonstrated by measurements of auditory evoked response. The system was also used to obtain a multi-channel 2-D image of a whole human hand at the measurement field of 46 microtesla with 3 by 3 mm resolution.Comment: To appear in Proceedings of 2006 Applied Superconductivity Conferenc

Comparison of beamformer implementations for MEG source localization

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression. We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes. When applied carefully to MEG data with a typical SNR (3-15 dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.Peer reviewe