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The emerging concept of psychobiotics—live microorganisms with a potential mental health benefit—represents a novel approach for the management of stress-related conditions. The majority of studies have focused on animal models. Recent preclinical studies have identified the B. longum 1714 strain as a putative psychobiotic with an impact on stress-related behaviors, physiology and cognitive performance. Whether such preclinical effects could be translated to healthy human volunteers remains unknown. We tested whether psychobiotic consumption could affect the stress response, cognition and brain activity patterns. In a within-participants design, healthy volunteers ( N= 22) completed cognitive assessments, resting electroencephalography and were exposed to a socially evaluated cold pressor test at baseline, post-placebo and post-psychobiotic. Increases in cortisol output and subjective anxiety in response to the socially evaluated cold pressor test were attenuated. Furthermore, daily reported stress was reduced by psychobiotic consumption. We also observed subtle improvements in hippocampus-dependent visuospatial memory performance, as well as enhanced frontal midline electroencephalographic mobility following psychobiotic consumption. These subtle but clear benefits are in line with the predicted impact from preclinical screening platforms. Our results indicate that consumption of B. longum 1714 is associated with reduced stress and improved memory. Further studies are warranted to evaluate the benefits of this putative psychobiotic in relevant stress-related conditions and to unravel the mechanisms underlying such effects.

Studies of inflammatory bowel disease (IBD) have been inconclusive in relating microbiota with distribution of inflammation. We report microbiota, host transcriptomics, epigenomics and genetics from matched inflamed and non-inflamed colonic mucosa [50 Crohn’s disease (CD); 80 ulcerative colitis (UC); 31 controls]. Changes in community-wide and within-patient microbiota are linked with inflammation, but we find no evidence for a distinct microbial diagnostic signature, probably due to heterogeneous host-microbe interactions, and show only marginal microbiota associations with habitual diet. Epithelial DNA methylation improves disease classification and is associated with both inflammation and microbiota composition. Microbiota sub-groups are driven by dominant Enterbacteriaceae and Bacteroides species, representative strains of which are pro-inflammatory in vitro, are also associated with immune-related epigenetic markers. In conclusion, inflamed and non-inflamed colonic segments in both CD and UC differ in microbiota composition and epigenetic profiles. Inflammatory bowel disease (IBD) has been linked to host-microbiota interactions. Here, the authors investigate mucosa-associated microbiota using endoscopically-targeted biopsies from inflamed and non-inflamed colon in patients with Crohn’s disease and ulcerative colitis, finding associations with inflammation and host epigenomic alterations.

Automated analysis of the neonatal EEG has the potential to assist clinical decision making for neonates with hypoxic-ischaemic encephalopathy. This paper proposes a method of automatically grading the degree of abnormality in an hour long epoch of neonatal EEG. The automated grading system (AGS) was based on a multi-class linear classifier grading of short-term epochs of EEG which were converted into a long-term grading of EEG using a majority vote operation. The features used in the AGS were summary measurements of two sub-signals extracted from a quadratic time-frequency distribution: the amplitude modulation and instantaneous frequency. These sub-signals were based on a model of EEG as a multiplication of a coloured random process with a slowly varying pseudo-periodic waveform and may be related to macroscopic neurophysiological function. The 4 grade AGS had a classification accuracy of 83% compared to human annotation of the EEG (level of agreement, κ  = 0.76). Features estimated on the developed sub-signals proved more effective at grading the EEG than measures based solely on the EEG and the incorporation of additional sub-grades based on EEG states into the AGS also improved performance.

Background/Aims EEG is the gold standard for the identification of neonatal seizures as the vast majority of electrographic seizures do not have a clinical correlate. Both under and over diagnosis of seizures is common in the neonatal intensive care unit (NICU). Computer assisted methods of interpreting the EEG have the potential to improve the accuracy of seizure detection. The aim of this study was to determine the clinical utility of our current neonatal seizure detection algorithm (NSDA). Methods Multi-channel video-EEG recordings of 70 term neonates admitted to the NICU were analysed: 35 babies with seizure (mixed aetiologies) and 35 babies without seizure. The EEGs were annotated by an experienced neurophysiologist. The performance of the NSDA was assessed using time and event based metrics. An additional, clinically relevant, performance metric (based on the number of neonates correctly administered an anti-epileptic drug (AED) as early as possible after electrographic seizure onset) was calculated. Results The sensitivity and specificity of the NSDA were 83% and 97% respectively when comparing to the experts annotation. The seizure detection rate and false alarm rate were 80% and 0.7/hr respectively. Thirty-four percent of neonates with seizures received an AED within the defined optimal timeframe, while 20% of neonates without seizure received an AED. These results were improved to 71% and 11%, respectively, by supplementing decision making with the output of the NSDA. Conclusion Current NSDA performance, while not perfect, would greatly improve the efficacy of seizure detection and optimal AED administration in the NICU.

The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability and efficacy of therapies. The ‘omics’ technologies used for microbiome analysis continuously evolve and, although much of the research is still at an early stage, large-scale datasets of ever increasing size and complexity are being produced. However, there are varying levels of difficulty in realizing the full potential of these new tools, which limit our ability to critically analyse much of the available data. In this Perspective, we provide a brief overview on the role of gut microbiome in cancer and focus on the need, role and limitations of a machine learning-driven approach to analyse large amounts of complex health-care information in the era of big data. We also discuss the potential application of microbiome-based big data aimed at promoting precision medicine in cancer.Large-scale datasets of increasing size and complexity are being produced in the microbiome and oncology field. This Perspective discusses the potential to harness gut microbiome analysis, big data and machine learning in cancer, and the potential and limitations with this approach.

This paper addresses Lukes’ and Hayward’s arguments that power should be conceived as agential versus structural. My fieldwork at Mitchell Primary School demonstrated that educators and students at Mitchell were structurally constrained and enabled but also exercised agency in navigating these institutional boundaries. Not only are both structural and agential conceptions of power valid, considering their interplay moves social analyses forward—at Mitchell, teachers’ otherwise-frequent metacognitive talk evaporated when their inclusion-oriented practices were more distant from institutional norms. Understanding power requires including its sources (from the individual actor to social structure) as one key dimension. Using this understanding could help educators more intentionally make conscious choices about their inclusion practices as they navigate their school environment.

Hypoxic–Ischemic Encephalopathy (HIE) occurs in patients who experience a decreased flow of blood and oxygen to the brain, with the optimal window for effective treatment being within the first six hours of life. This puts a significant demand on medical professionals to accurately and effectively grade the severity of the HIE present, which is a time-consuming and challenging task. This paper proposes a novel workflow for background EEG grading, implementing a blend of Digital Signal Processing (DSP) and Machine-Learning (ML) techniques. First, the EEG signal is transformed into an amplitude and frequency modulated audio spectrogram, which enhances its relevant signal properties. The difference between EEG Grades 1 and 2 is enhanced. A convolutional neural network is then designed as a regressor to map the input image into an EEG grade, by utilizing an optimized rounding module to leverage the monotonic relationship among the grades. Using a nested cross-validation approach, an accuracy of 89.97% was achieved, in particular improving the AUC of the most challenging grades, Grade 1 and Grade 2, to 0.98 and 0.96. The results of this study show that the proposed representation and workflow increase the potential for background grading of EEG signals, increasing the accuracy of grading background patterns that are most relevant for therapeutic intervention, across large windows of time.

Women’s rights advocates in Iowa successfully got state laws adopted in the late 1980s and in 2009 requiring gender balance on state and local boards and commissions, the only such laws in the USA. Through interview and archival methods, this paper uses a critical juncture framework to unveil how this was accomplished in part through a strategy underexplored in academic and practitioner literature—deradicalizing an issue through a series of “piecemeal” efforts. Small less controversial changes can build up to alter the status quo, making room for changes previously thought unaccomplishable. This study brings normatizing —the process of incrementally institutionalizing new norms—forward as a socialization strategy for social movement actors to intentionally consider employing in situations they encounter where political will on an issue is substantially lacking.

The performance gains achieved by deep learning models nowadays are mainly attributed to the usage of ever larger datasets. In this study, we present and contrast the performance gains that can be achieved via accessing larger high-quality datasets versus the gains that can be achieved from harnessing the latest deep learning architectural and training advances. Modelling neonatal EEG is particularly affected by the lack of publicly available large datasets. It is shown that greater performance gains can be achieved from harnessing the latest deep learning advances than using a larger training dataset when adopting AUC as a metric, whereas using AUC90 or AUC-PR as metrics greater performance gains are achieved from using a larger dataset than harnessing the latest deep learning advances. In all scenarios the best performance is obtained by combining both deep learning advances and larger datasets. A novel developed architecture is presented that outperforms the current state-of-the-art model for the task of neonatal seizure detection. A novel method to fine-tune the presented model towards site-specific settings based on pseudo labelling is also outlined. The code and the weights of the model are made publicly available for benchmarking future model performances for neonatal seizure detection.