Origins of choice-related activity in mouse somatosensory cortex.

During perceptual decisions about faint or ambiguous sensory stimuli, even identical stimuli can produce different choices. Spike trains from sensory cortex neurons can predict trial-to-trial variability in choice. Choice-related spiking is widely studied as a way to link cortical activity to perception, but its origins remain unclear. Using imaging and electrophysiology, we found that mouse primary somatosensory cortex neurons showed robust choice-related activity during a tactile detection task. Spike trains from primary mechanoreceptive neurons did not predict choices about identical stimuli. Spike trains from thalamic relay neurons showed highly transient, weak choice-related activity. Intracellular recordings in cortex revealed a prolonged choice-related depolarization in most neurons that was not accounted for by feed-forward thalamic input. Top-down axons projecting from secondary to primary somatosensory cortex signaled choice. An intracellular measure of stimulus sensitivity determined which neurons converted choice-related depolarization into spiking. Our results reveal how choice-related spiking emerges across neural circuits and within single neurons

Developing theory-informed interventions to implement evidence into practice: a systematic approach using the Theoretical Domains Framework

Background: There is little systematic operational guidance about how best to develop complex interventions to reduce the gap between practice and evidence. This article is one in a series of articles documenting the development and use of the Theoretical Domains Framework (TDF) to advance the science of implementation research. Methods: The intervention was developed considering three main components: theory, evidence, and practical issues. We used a four-step approach, consisting of guiding questions, to direct the choice of the most appropriate components of an implementation intervention: Who needs to do what, differently? Using a theoretical framework, which barriers and enablers need to be addressed? Which intervention components (behaviour change techniques and mode(s) of delivery) could overcome the modifiable barriers and enhance the enablers? And how can behaviour change be measured and understood? Results: A complex implementation intervention was designed that aimed to improve acute low back pain management in primary care. We used the TDF to identify the barriers and enablers to the uptake of evidence into practice and to guide the choice of intervention components. These components were then combined into a cohesive intervention. The intervention was delivered via two facilitated interactive small group workshops. We also produced a DVD to distribute to all participants in the intervention group. We chose outcome measures in order to assess the mediating mechanisms of behaviour change. Conclusions: We have illustrated a four-step systematic method for developing an intervention designed to change clinical practice based on a theoretical framework. The method of development provides a systematic framework that could be used by others developing complex implementation interventions. While this framework should be iteratively adjusted and refined to suit other contexts and settings, we believe that the four-step process should be maintained as the primary framework to guide researchers through a comprehensive intervention development process

Transfer Learning for Multi-language Twitter Election Classification

Both politicians and citizens are increasingly embracing social media as a means to disseminate information and comment on various topics, particularly during significant political events, such as elections. Such commentary during elections is also of interest to social scientists and pollsters. To facilitate the study of social media during elections, there is a need to automatically identify posts that are topically related to those elections. However, current studies have focused on elections within English-speaking regions, and hence the resultant election content classifiers are only applicable for elections in countries where the predominant language is English. On the other hand, as social media is becoming more prevalent worldwide, there is an increasing need for election classifiers that can be generalised across different languages, without building a training dataset for each election. In this paper, based upon transfer learning, we study the development of effective and reusable election classifiers for use on social media across multiple languages. We combine transfer learning with different classifiers such as Support Vector Machines (SVM) and state-of-the-art Convolutional Neural Networks (CNN), which make use of word embedding representations for each social media post. We generalise the learned classifier models for cross-language classification by using a linear translation approach to map the word embedding vectors from one language into another. Experiments conducted over two election datasets in different languages show that without using any training data from the target language, linear translations outperform a classical transfer learning approach, namely Transfer Component Analysis (TCA), by 80% in recall and 25% in F1 measure

Dimensional Crossover in the Large N Limit

We consider dimensional crossover for an $O(N)$ Landau-Ginzburg-Wilson model on a $d$-dimensional film geometry of thickness $L$ in the large $N$-limit. We calculate the full universal crossover scaling forms for the free energy and the equation of state. We compare the results obtained using ``environmentally friendly'' renormalization with those found using a direct, non-renormalization group approach. A set of effective critical exponents are calculated and scaling laws for these exponents are shown to hold exactly, thereby yielding non-trivial relations between the various thermodynamic scaling functions.Comment: 25 pages of PlainTe

A Parameterized Neutrino Emission Model to Study Mass Ejection in Failed Core-collapse Supernovae

Some massive stars end their lives as \textit{failed} core-collapse supernovae (CCSNe) and become black holes (BHs). Although in this class of phenomena the stalled supernova shock is not revived, the outer stellar envelope can still be partially ejected. This occurs because the hydrodynamic equilibrium of the star is disrupted by the gravitational mass loss of the protoneutron star (PNS) due to neutrino emission. We develop a simple model that emulates PNS evolution and its neutrino emission and use it to simulate failed CCSNe in spherical symmetry for a wide range of progenitor stars. Our model allows us to study mass ejection of failed CCSNe where the PNS collapses into a BH within $\sim100\,{\rm ms}$ and up to $\sim10^6\,{\rm s}$. We perform failed CCSNe simulations for 262 different pre-SN progenitors and determine how the energy and mass of the ejecta depend on progenitor properties and the equation of state (EOS) of dense matter. In the case of a future failed CCSN observation, the trends obtained in our simulations can be used to place constraints on the pre-SN progenitor characteristics, the EOS, and on PNS properties at BH formation time.Comment: 21 pages, 12 figures, 2 table

The Sero-prevalence of Salmonella spp. in Finishing Swine in Iowa

This study represents the first attempt to classify Iowa production sites for Salmonella spp. sero-prevalence. The data suggest that the Iowa herds are similar in their distribution with respect to sero-prevalence of salmonella as Danish herds. Ignoring herd size, 91.2 of surveyed herds were negative or level 1, 8.2 % were level 2 herds, and 1.6 % level 3. These results are similar to previous Danish studies (Alban et al., 2002, Mousing et al., 1997). The current data suggests that larger herds tend to have a higher sero-prevalence than smaller units; however, formal analysis has yet to be conducted to determine the direct association between herd size and salmonella sero-prevalence. Studies by Carstensen et al. (1998) suggested that herd size was statistically associated, albeit weakly, with Salmonella sero-prevalence, but the authors concluded the relationship was probably not biologically significant

Determination of Toxoplasma gondii Antibody Prevalence in Midwest Market Swine

Pork has been identified as one of the food source(s) for human exposure to Toxoplasma gondii. This project was designed to determine the current prevalence of Toxoplasma gondii antibodies in the Midwestern USA market swine population. Test samples were selected, using random numbers generated from the Excel database, from approximately 2,500 daily meat juice samples submitted for Aujeszky’s Disease from eight Iowa abattoirs. Producer identification and lot size were recorded for each lot. Two hundred fifty samples were selected for 12 consecutive weeks – total of 15,014 samples. The presence of antibodies was determined using ELISA test kits by Safepath Laboratories. The prevalence for all samples was 0.75 % with a higher prevalence found in lots of 20 - 40 compared to 150 - 190 head. Additional on-farm evaluations of exposure risk factors are required to determine an association between sero-prevalence and lot size and to develop suitable prevention strategies

Dynamics and Gravitational Wave Signature of Collapsar Formation

We perform 3+1 general relativistic simulations of rotating core collapse in the context of the collapsar model for long gamma-ray bursts. We employ a realistic progenitor, rotation based on results of stellar evolution calculations, and a simplified equation of state. Our simulations track self-consistently collapse, bounce, the postbounce phase, black hole formation, and the subsequent early hyperaccretion phase. We extract gravitational waves from the spacetime curvature and identify a unique gravitational wave signature associated with the early phase of collapsar formation

Semantic analysis of field sports video using a petri-net of audio-visual concepts

The most common approach to automatic summarisation and highlight detection in sports video is to train an automatic classifier to detect semantic highlights based on occurrences of low-level features such as action replays, excited commentators or changes in a scoreboard. We propose an alternative approach based on the detection of perception concepts (PCs) and the construction of Petri-Nets which can be used for both semantic description and event detection within sports videos. Low-level algorithms for the detection of perception concepts using visual, aural and motion characteristics are proposed, and a series of Petri-Nets composed of perception concepts is formally defined to describe video content. We call this a Perception Concept Network-Petri Net (PCN-PN) model. Using PCN-PNs, personalized high-level semantic descriptions of video highlights can be facilitated and queries on high-level semantics can be achieved. A particular strength of this framework is that we can easily build semantic detectors based on PCN-PNs to search within sports videos and locate interesting events. Experimental results based on recorded sports video data across three types of sports games (soccer, basketball and rugby), and each from multiple broadcasters, are used to illustrate the potential of this framework