Carbon Nanotubes as Suitable Interface for Improving Neural Recordings

In the last decades, system neuroscientists around the world have dedicated their research to understand how neuronal networks work and how they malfunction in various diseases. Furthermore in the last years we have seen a progressively increased interaction of brain networks with external devices either for the use of brain computer interfaces or through the currently extended brain stimulation (e.g. transcranial magnetic stimulation) for therapy. Both techniques have evidenced even more the need for a better understanding of neuronal networks. These studies have resulted in the development of different strategies to understand the ongoing neuronal activity, such as fluorescence microscopy for genetic labelling and optogenetic techniques, imaging techniques, or the recording/stimulation with increasingly large numbers of electrodes in the whole brain or in both cell cultured neurons and slice preparations. It is in these last two areas where the technology developed on microelectrode arrays, commonly called multi-electrode arrays (MEAs), has become important over other technologie

Quantification of Signal-to-Noise Ratio in Cerebral Cortex Recordings Using Flexible MEAs With Co-localized Platinum Black, Carbon Nanotubes, and Gold Electrodes

Developing new standardized tools to characterize brain recording devices is critical to evaluate neural probes and for translation to clinical use. The signal-to-noise ratio (SNR) measurement is the gold standard for quantifying the performance of brain recording devices. Given the drawbacks with the SNR measure, our first objective was to devise a new method to calculate the SNR of neural signals to distinguish signal from noise. Our second objective was to apply this new SNR method to evaluate electrodes of three different materials (platinum black, Pt; carbon nanotubes, CNTs; and gold, Au) co-localized in tritrodes to record from the same cortical area using specifically designed multielectrode arrays. Hence, we devised an approach to calculate SNR at different frequencies based on the features of cortical slow oscillations (SO). Since SO consist in the alternation of silent periods (Down states) and active periods (Up states) of neuronal activity, we used these as noise and signal, respectively. The spectral SNR was computed as the power spectral density (PSD) of Up states (signal) divided by the PSD of Down states (noise). We found that Pt and CNTs electrodes have better recording performance than Au electrodes for the explored frequency range (5–1500 Hz). Together with two proposed SNR estimators for the lower and upper frequency limits, these results substantiate our SNR calculation at different frequency bands. Our results provide a new validated SNR measure that provides rich information of the performance of recording devices at different brain activity frequency bands (<1500 Hz)

Colecciones ex situ de planta viva para la conservación de la planta amenazada Silene hifacensis, Rouy ex Willk (Caryophyllaceae)

The establishment of seed orchards has allowed obtaining a great deal of germoplasm of Silene hifacensis, an endangered endemic Ibero-Balearic species. In four years, 3.958.531 seeds have been collected in our four seed orchards from a total of 570 plants/year per average, including all genetic variability from natural populations of this species in Alicante province (Illot of Mona, the Pessebret, Cova de les Cendres, Morro de Toix)

Jet energy measurement and its systematic uncertainty in proton–proton collisions at √s=7 TeV with the ATLAS detector

The jet energy scale (JES) and its systematic uncertainty are determined for jets measured with the ATLAS detector using proton–proton collision data with a centre-of-mass energy of √s=7 TeV corresponding to an integrated luminosity of 4.7 fb −1. Jets are reconstructed from energy deposits forming topological clusters of calorimeter cells using the anti-kt algorithm with distance parameters R=0.4 or R=0.6, and are calibrated using MC simulations. A residual JES correction is applied to account for differences between data and MC simulations. This correction and its systematic uncertainty are estimated using a combination of in situ techniques exploiting the transverse momentum balance between a jet and a reference object such as a photon or a Z boson, for 20≤pTjet1 TeV. The calibration of forward jets is derived from dijet pT balance measurements. The resulting uncertainty reaches its largest value of 6 % for low-pT jets at |η|=4.5. Additional JES uncertainties due to specific event topologies, such as close-by jets or selections of event samples with an enhanced content of jets originating from light quarks or gluons, are also discussed. The magnitude of these uncertainties depends on the event sample used in a given physics analysis, but typically amounts to 0.5–3 %

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Quantification of signal-to-noise ratio in cerebral cortex recordings using flexible MEAs with co-localized platinum black, carbon nanotubes, and gold electrodes

6 FigurasDeveloping new standardized tools to characterize brain recording devices is critical to evaluate neural probes and for translation to clinical use. The signal-to-noise ratio (SNR) measurement is the gold standard for quantifying the performance of brain recording devices. Given the drawbacks with the SNR measure, our first objective was to devise a new method to calculate the SNR of neural signals to distinguish signal from noise. Our second objective was to apply this new SNR method to evaluate electrodes of three different materials (platinum black, Pt; carbon nanotubes, CNTs; and gold, Au) co-localized in tritrodes to record from the same cortical area using specifically designed multielectrode arrays. Hence, we devised an approach to calculate SNR at different frequencies based on the features of cortical slow oscillations (SO). Since SO consist in the alternation of silent periods (Down states) and active periods (Up states) of neuronal activity, we used these as noise and signal, respectively. The spectral SNR was computed as the power spectral density (PSD) of Up states (signal) divided by the PSD of Down states (noise). We found that Pt and CNTs electrodes have better recording performance than Au electrodes for the explored frequency range (5-1500 Hz). Together with two proposed SNR estimators for the lower and upper frequency limits, these results substantiate our SNR calculation at different frequency bands. Our results provide a new validated SNR measure that provides rich information of the performance of recording devices at different brain activity frequency bands (<1500 Hz).This work was supported by Ministerio de Ciencia, Innovación y Universidades (Spain), BFU2017-85048-R and PCIN-2015-162-C02-01 (FLAG ERA) to MVSV, and by CERCA Programme/Generalitat de Catalunya.Peer Reviewe