Electric fishing survey of the gravel addition sites on the River Wyre, Grizedale Beck and Joshua's Beck.

Although geographically the River Wyre lies between two rivers containing major migrations of adult salmon and sea trout, its rod & line fisheries have for a number of years produced exceptionally low catches. In order to determine the causes of this the Wyre Salmon and Sea trout Restoration Group (WSSRG) was conceived in 1994 as a partnership between the then National Rivers Authority (now Environment Agency), local landowners, angling clubs and interested parties. Two studies of 1994 and 1995 stated that there is a shortage of useable spawning gravels on the river. This is compounded by Abbeystead Reservoir acting as a gravel trap, the siltation of gravels on several side becks and problems with access to available gravels by returning adults. There was also perceived to be a need for accurate fishery data from the river encompassing redd counts, catch data and surveys of fry populations. The 1995 report suggested a number of management proposals which might be adopted in order to improve and create available spawning habitat for migratory salmonids. Funding was made available to create three spawning gravels on each of two side becks (Grizedale Beck and Joshua's Beck) and the addition of gravels to a site oh the main river below Abbeystead Reservoir. Modifications were also made to the fish pass at Abbeystead to allow easier passage of fish. These improvements were made in the autumn of 1995. Salmonid spawning redd counting was undertaken on the whole Wyre catchment in 1995/1996 and specific surveys by electric fishing on the gravel enhancement sites in the summer of 1996. This report details the current state of the improvement works that were undertaken and presents the results of electric fishing surveys in September 1996. A number of lessons have been learnt which will be of great benefit to the Fisheries Function in other parts of the Wyre catchment and the Central Area in general

Disconnection of network hubs and cognitive impairment after traumatic brain injury.

Traumatic brain injury affects brain connectivity by producing traumatic axonal injury. This disrupts the function of large-scale networks that support cognition. The best way to describe this relationship is unclear, but one elegant approach is to view networks as graphs. Brain regions become nodes in the graph, and white matter tracts the connections. The overall effect of an injury can then be estimated by calculating graph metrics of network structure and function. Here we test which graph metrics best predict the presence of traumatic axonal injury, as well as which are most highly associated with cognitive impairment. A comprehensive range of graph metrics was calculated from structural connectivity measures for 52 patients with traumatic brain injury, 21 of whom had microbleed evidence of traumatic axonal injury, and 25 age-matched controls. White matter connections between 165 grey matter brain regions were defined using tractography, and structural connectivity matrices calculated from skeletonized diffusion tensor imaging data. This technique estimates injury at the centre of tract, but is insensitive to damage at tract edges. Graph metrics were calculated from the resulting connectivity matrices and machine-learning techniques used to select the metrics that best predicted the presence of traumatic brain injury. In addition, we used regularization and variable selection via the elastic net to predict patient behaviour on tests of information processing speed, executive function and associative memory. Support vector machines trained with graph metrics of white matter connectivity matrices from the microbleed group were able to identify patients with a history of traumatic brain injury with 93.4% accuracy, a result robust to different ways of sampling the data. Graph metrics were significantly associated with cognitive performance: information processing speed (R(2) = 0.64), executive function (R(2) = 0.56) and associative memory (R(2) = 0.25). These results were then replicated in a separate group of patients without microbleeds. The most influential graph metrics were betweenness centrality and eigenvector centrality, which provide measures of the extent to which a given brain region connects other regions in the network. Reductions in betweenness centrality and eigenvector centrality were particularly evident within hub regions including the cingulate cortex and caudate. Our results demonstrate that betweenness centrality and eigenvector centrality are reduced within network hubs, due to the impact of traumatic axonal injury on network connections. The dominance of betweenness centrality and eigenvector centrality suggests that cognitive impairment after traumatic brain injury results from the disconnection of network hubs by traumatic axonal injury

Novel modeling of task versus rest brain state predictability using a dynamic time warping spectrum: comparisons and contrasts with other standard measures of brain dynamics

Dynamic time warping, or DTW, is a powerful and domain-general sequence alignment method for computing a similarity measure. Such dynamic programming-based techniques like DTW are now the backbone and driver of most bioinformatics methods and discoveries. In neuroscience it has had far less use, though this has begun to change. We wanted to explore new ways of applying DTW, not simply as a measure with which to cluster or compare similarity between features but in a conceptually different way. We have used DTW to provide a more interpretable spectral description of the data, compared to standard approaches such as the Fourier and related transforms. The DTW approach and standard discrete Fourier transform (DFT) are assessed against benchmark measures of neural dynamics. These include EEG microstates, EEG avalanches, and the sum squared error (SSE) from a multilayer perceptron (MLP) prediction of the EEG time series, and simultaneously acquired FMRI BOLD signal. We explored the relationships between these variables of interest in an EEG-FMRI dataset acquired during a standard cognitive task, which allowed us to explore how DTW differentially performs in different task settings. We found that despite strong correlations between DTW and DFT-spectra, DTW was a better predictor for almost every measure of brain dynamics. Using these DTW measures, we show that predictability is almost always higher in task than in rest states, which is consistent to other theoretical and empirical findings, providing additional evidence for the utility of the DTW approach

The control of global brain dynamics: opposing actions of frontoparietal control and default mode networks on attention

Understanding how dynamic changes in brain activity control behavior is a major challenge of cognitive neuroscience. Here, we consider the brain as a complex dynamic system and define two measures of brain dynamics: the synchrony of brain activity, measured by the spatial coherence of the BOLD signal across regions of the brain; and metastability, which we define as the extent to which synchrony varies over time. We investigate the relationship among brain network activity, metastability, and cognitive state in humans, testing the hypothesis that global metastability is “tuned” by network interactions. We study the following two conditions: (1) an attentionally demanding choice reaction time task (CRT); and (2) an unconstrained “rest” state. Functional MRI demonstrated increased synchrony, and decreased metastability was associated with increased activity within the frontoparietal control/dorsal attention network (FPCN/DAN) activity and decreased default mode network (DMN) activity during the CRT compared with rest. Using a computational model of neural dynamics that is constrained by white matter structure to test whether simulated changes in FPCN/DAN and DMN activity produce similar effects, we demonstate that activation of the FPCN/DAN increases global synchrony and decreases metastability. DMN activation had the opposite effects. These results suggest that the balance of activity in the FPCN/DAN and DMN might control global metastability, providing a mechanistic explanation of how attentional state is shifted between an unfocused/exploratory mode characterized by high metastability, and a focused/constrained mode characterized by low metastability

Artifacts at 4.5 and 8.0 um in Short Wavelength Spectra from the Infrared Space Observatory

Spectra from the Short Wavelength Spectrometer (SWS) on ISO exhibit artifacts at 4.5 and 8 um. These artifacts appear in spectra from a recent data release, OLP 10.0, as spurious broad emission features in the spectra of stars earlier than ~F0, such as alpha CMa. Comparison of absolutely calibrated spectra of standard stars to corresponding spectra from the SWS reveals that these artifacts result from an underestimation of the strength of the CO and SiO molecular bands in the spectra of sources used as calibrators by the SWS. Although OLP 10.0 was intended to be the final data release, these findings have led to an additional release addressing this issue, OLP 10.1, which corrects the artifacts.Comment: 14 pages, AASTex, including 5 figures. Accepted by ApJ Letter

Universal error corrections for finite semiconductor resistivity in cross-kelvin resistor test structures

The Cross-Kelvin Resistor test structure is commonly used for the extraction of the specific contact resistance of ohmic contacts. Analysis using this structure are generally based on a two-dimensional model that assumes zero voltage drop in the semiconductor layer in the direction normal to the plane of the contact. This paper uses a three-dimensional (3-D) analysis to show the magnitude of the errors introduced by this assumption, and illustrates the conditions under which a 3-D analysis should be used. This paper presents for the first time 3-D universal error correction curves that account for the vertical voltage drop due to the finite depth of the semiconductor laye

Voltage imaging of waking mouse cortex reveals emergence of critical neuronal dynamics.

Complex cognitive processes require neuronal activity to be coordinated across multiple scales, ranging from local microcircuits to cortex-wide networks. However, multiscale cortical dynamics are not well understood because few experimental approaches have provided sufficient support for hypotheses involving multiscale interactions. To address these limitations, we used, in experiments involving mice, genetically encoded voltage indicator imaging, which measures cortex-wide electrical activity at high spatiotemporal resolution. Here we show that, as mice recovered from anesthesia, scale-invariant spatiotemporal patterns of neuronal activity gradually emerge. We show for the first time that this scale-invariant activity spans four orders of magnitude in awake mice. In contrast, we found that the cortical dynamics of anesthetized mice were not scale invariant. Our results bridge empirical evidence from disparate scales and support theoretical predictions that the awake cortex operates in a dynamical regime known as criticality. The criticality hypothesis predicts that small-scale cortical dynamics are governed by the same principles as those governing larger-scale dynamics. Importantly, these scale-invariant principles also optimize certain aspects of information processing. Our results suggest that during the emergence from anesthesia, criticality arises as information processing demands increase. We expect that, as measurement tools advance toward larger scales and greater resolution, the multiscale framework offered by criticality will continue to provide quantitative predictions and insight on how neurons, microcircuits, and large-scale networks are dynamically coordinated in the brain

Finite element modeling of misalignment in interconnect vias

Electrical resistance and hence heat generation in semiconductor chips are becoming more significant issues particularily as generations of silicon devices continue to have smaller features. The resistance of interconnect vias is a significant source of heat generation because of the increasing number of these on chips and increases in via resistance due to reduced size. Finite element modeling of voltage drops and current flow through interconnect vias gives information to aid in designing geometry and materials used in forming vias. It can also be used for modeling the thermal distribution in a via and hence the contribution by vias to heating a chip. In this paper we examine the effect of misalignment of the via between the two metal layers M1 and M2 with regard to the interconnect via resistance. The effect of the interface specific contact resistance is examined in particular. Significant misalignment can be tolerated without increasing the via resistance. The heat generation due to electrical current flow in the via materials and interfaces is modelled using the samefinite element mesh and software. The output of the electrical analysis is used as the heat generation input for the therml analysis