Neural patterns of the implicit association test

The Implicit Association Test (IAT) is a reaction time based categorization task that measures the differential associative strength between bipolar targets and evaluative attribute concepts as an approach to indexing implicit beliefs or biases. An open question exists as to what exactly the IAT measures, and here EEG (Electroencephalography) has been used to investigate the time course of ERPs (Event-related Potential) indices and implicated brain regions in the IAT. IAT-EEG research identifies a number of early (250–450 ms) negative ERPs indexing early-(pre-response) processing stages of the IAT. ERP activity in this time range is known to index processes related to cognitive control and semantic processing. A central focus of these efforts has been to use IAT-ERPs to delineate the implicit and explicit factors contributing to measured IAT effects. Increasing evidence indicates that cognitive control (and related top-down modulation of attention/perceptual processing) may be components in the effective measurement of IAT effects, as factors such as physical setting or task instruction can change an IAT measurement. In this study we further implicate the role of proactive cognitive control and top-down modulation of attention/perceptual processing in the IAT-EEG. We find statistically significant relationships between D-score (a reaction-time based measure of the IAT-effect) and early ERP-time windows, indicating where more rapid word categorizations driving the IAT effect are present, they are at least partly explainable by neural activity not significantly correlated with the IAT measurement itself. Using LORETA, we identify a number of brain regions driving these ERP-IAT relationships notably involving left-temporal, insular, cingulate, medial frontal and parietal cortex in time regions corresponding to the N2- and P3-related activity. The identified brain regions involved with reduced reaction times on congruent blocks coincide with those of previous studies

BodyScan: Enabling Radio-based Sensing on Wearable Devices for Contactless Activity and Vital Sign Monitoring

Wearable devices are increasingly becoming mainstream consumer products carried by millions of consumers. However, the potential impact of these devices is currently constrained by fundamental limitations of their built-in sensors. In this paper, we introduce radio as a new powerful sensing modality for wearable devices and propose to transform radio into a mobile sensor of human activities and vital signs. We present BodyScan, a wearable system that enables radio to act as a single modality capable of providing whole-body continuous sensing of the user. BodyScan overcomes key limitations of existing wearable devices by providing a contactless and privacy-preserving approach to capturing a rich variety of human activities and vital sign information. Our prototype design of BodyScan is comprised of two components: one worn on the hip and the other worn on the wrist, and is inspired by the increasingly prevalent scenario where a user carries a smartphone while also wearing a wristband/smartwatch. This prototype can support daily usage with one single charge per day. Experimental results show that in controlled settings, BodyScan can recognize a diverse set of human activities while also estimating the user's breathing rate with high accuracy. Even in very challenging real-world settings, BodyScan can still infer activities with an average accuracy above 60% and monitor breathing rate information a reasonable amount of time during each day

Modifying executive function and self-regulatory behaviours in developmental dyslexia: cognitive and neural bases of response inhibition

Dyslexia is characterised by impaired reading, but socio-emotional problems typically co-occur (1). It is also associated with response inhibition (RI) impairments at the behavioural (2,3) and neural levels as indexed by reduced response-inhibition related P3 amplitude (4). Studies have shown that variability in RI is predictive of the severity of reading and socio-emotional problems in dyslexia (2,5), suggesting that RI may underpin these issues. RI appears modifiable at the behavioural and neural levels with training (6,7). Therefore, RI training may improve RI (behavioural & neural), and reduce reading and socio-emotional problems in dyslexia. No study to date has explored whether RI is modifiable in dyslexia and whether training transfers to reduced symptoms

Archaea catalyze iron-dependent anaerobic oxidation of methane

Anaerobic oxidation of methane (AOM) is crucial for controlling the emission of this potent greenhouse gas to the atmosphere. Nitrite-, nitrate-, and sulfate-dependent methane oxidation is well-documented, but AOM coupled to the reduction of oxidized metals has so far been demonstrated only in environmental samples. Here, using a freshwater enrichment culture, we show that archaea of the order Methanosarcinales, related to “Candidatus Methanoperedens nitroreducens,” couple the reduction of environmentally relevant forms of Fe^(3+) and Mn^(4+) to the oxidation of methane. We obtained an enrichment culture of these archaea under anaerobic, nitrate-reducing conditions with a continuous supply of methane. Via batch incubations using [^(13)C]methane, we demonstrated that soluble ferric iron (Fe^(3+), as Fe-citrate) and nanoparticulate forms of Fe^(3+) and Mn^(4+) supported methane-oxidizing activity. CO_2 and ferrous iron (Fe^(2+)) were produced in stoichiometric amounts. Our study connects the previous finding of iron-dependent AOM to microorganisms detected in numerous habitats worldwide. Consequently, it enables a better understanding of the interaction between the biogeochemical cycles of iron and methane

A Hybrid 3D/2D Field Response Calculation for Liquid Argon Detectors with PCB Based Anode Plane

Liquid Argon Time Projection Chamber (LArTPC) technology is commonly utilized in neutrino detector designs. It enables detailed reconstruction of neutrino events with high spatial precision and low energy threshold. Its field response (FR) model describes the time-dependent electric currents induced in the anode-plane electrodes when ionization electrons drift nearby. An accurate and precise FR is a crucial input to LArTPC detector simulations and charge reconstruction. Established LArTPC designs have been based on parallel wire planes. It allows accurate and computationally economic two-dimensional (2D) FR models utilizing the translational symmetry along the direction of the wires. Recently, novel LArTPC designs utilize electrodes formed on printed circuit board (PCB) in the shape of strips with through holes. The translational symmetry is no longer a good approximation near the electrodes and a new FR calculation that employs regions with three dimensions (3D) has been developed. Extending the 2D models to 3D would be computationally expensive. Fortuitously, the nature of strips with through holes allows for a computationally economic approach based on the finite-difference method (FDM). In this paper, we present a new software package "pochoir" that calculates LArTPC field response for these new strip-based anode designs. This package combines 3D calculations in the volume near the electrodes with 2D far-field solutions to achieve fast and precise field response computation. We apply the resulting FR to simulate and reconstruct samples of cosmic-ray muons and $^{39}$Ar decays from a Vertical Drift (VD) detector prototype operated at CERN. We find the difference between real and simulated data within 5 %. Current state-of-the-art LArTPC software requires a 2D FR which we provide by averaging over one dimension and estimate that variations lost in this average are smaller than 7 %.Comment: 16 pages, 12 figure

Analysis of fishing vessel accidents with Bayesian network and Chi-square methods

Commercial fishing is an important industry that generates income directly or indirectly to many people in the world. It is impossible to carry out a fishing activity on this scale without a vessel. Therefore, fishing vessels are the most important element of modern fishing industry. Fishing vessels play a key role in fishing, transporting and storing fish. Thousands of people die every year as a result of fishing vessel accidents. In order to carry out sustainable fishing operations, fishing vessel accidents should be investigated and measures should be taken to prevent them. Therefore, in this study for analysing of accidents occurred between 2008 and 2018 in fishing vessels, with full lengths of 7 m and above, Bayesian network, chi-square methods were used. As a result, recommendations were made to prevent accidents. Also, Accident (Bayes) Network, which summarizes the occurrence of accidents on fishing vessels, is presented. These networks allow to understand the occurrence of accidents in fishing vessels and to estimate the occurrence of accidents in variable conditions. It was also found that there was a significant relationship between accident category and vessel length, vessel age, loss of life and loss of vessel

Neural and cognitive correlates of human decision-making in domestic energy usage

Decision-making is a central component of every facet of human life, and is generally understood to be either conscious (deliberate) or automatic (non-deliberate). There has been little research to date on decision-making in the context of domestic energy consumption. Our study elucidated the human processes related to decisions around domestic energy use. In particular, the study investigated the neural and cognitive triggers of decision-making which differentiate between optimal and non-optimal energy consumers. Using EEG (electroencephalography) to assess brain function, we investigated brain activity associated with decisions around energy consumption and in this paper we report results from a study of 30 participants for whom we recorded their neural activity as they made decisions. As well as this, behavioural data related to cognitive processes involved were recorded. By examining this data, we aim to clarify some of the reasons why people make certain decisions about domestic energy consumption

Reinterpreting Interpretability for Fuzzy Linguistic Descriptions of Data

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Dolphin morbillivirus infection in different parts of the Mediterranean Sea

Morbillivirus were isolated from Mediterranean striped dolphins (Stenella coeruleoalba) dying along the coasts of Italy and Greece in 1991. They were antigenically identical to the morbilliviruses isolated from striped dolphins in Spain in 1990