Investigating the prevalence of Salmonella in dogs within the Midlands region of the United Kingdom

Background - The intimate relationship between dogs and their owners has the potential to increase the risk of human exposure to bacterial pathogens. Over the past 40 years, there have been several reports on transmission of salmonellae from dogs to humans. This study therefore aimed to determine the prevalence of Salmonella in the faeces of dogs from the Midlands region of the United Kingdom to assess exposure risk and potential for zoonotic transmission. Results - A total of 436 apparently healthy dogs without diarrhoea from households (n = 126), rescue centres (n = 96), boarding kennels (n = 43), retired greyhound kennels (n = 39) and a pet nutrition facility (n = 132) were investigated for Salmonella shedding. Faecal samples were processed by an enrichment culture based method. The faeces from one dog (0.23 %; 95 % confidence limit 0.006 %, 1.27 %) was positive for Salmonella. The species was S. enterica subspecies arizonae. Conclusion - This study showed that the prevalence of Salmonella from faeces from apparently healthy dogs from a variety of housing conditions is low; however, Salmonella shedding was still identified

Inferring single-trial neural population dynamics using sequential auto-encoders

Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics

A case-control study of pathogen and lifestyle risk factors for diarrhoea in dogs

Diarrhoea is a common and multi-factorial condition in dogs, the aetiology of which is often incompletely understood. A case-control study was carried out to compare the carriage of some common canine enteric pathogens (enteric coronavirus, parvovirus, distemper, endoparasites, Campylobacter and Salmonella spp.), as well as lifestyle factors such as vaccination history, diet and contact with other species, in dogs presenting at first opinion veterinary practices with and without diarrhoea. Multivariable conditional logistic regression showed that dogs in the study which scavenged or had had a recent change of diet (OR 3.5, p = 0.002), had recently stayed in kennels (OR 9.5, p = 0.01), or were fed a home-cooked diet (OR 4, p = 0.002) were at a significantly greater risk of diarrhoea, whilst being female (OR 0.4, p = 0.01), currently up to date with routine vaccinations (OR 0.4, p = 0.05) and having contact with horse faeces (OR 0.4, p = 0.06) were associated with a reduced risk. None of the pathogens tested for was a significant factor in the final multivariable model suggesting that in this predominantly vaccinated population, diarrhoea may be more associated with lifestyle risk factors than specific pathogens. © 2011 Elsevier B.V

Workshops of the eighth international brain-computer interface meeting: BCIs: the next frontier

The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9, 2021 in a virtual format. The conference continued the BCI Meeting series’ interactive nature with 21 workshops covering the breadth of topics in BCI (also called brain-machine interface) research. Some workshops provided detailed examinations of methods, hardware, or processes. Others focused on BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and improve comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, summarizes discussions, and describes the resulting conclusion, challenges, or initiatives

A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes

OBJECTIVE: Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. APPROACH: Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. MAIN RESULTS: LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. SIGNIFICANCE: These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs