To Blink or Not to Blink: Fine Cognitive Tuning of the Defensive Peripersonal Space

The blink reflex elicited by the electrical stimulation of the median nerve at the wrist (hand blink reflex, HBR) is a subcortical, defensive response that is enhanced when the stimulated hand is inside the peripersonal space of the face. Such enhancement results from a tonic, topdown modulation of the excitability of the brainstem interneurons mediating the HBR. Here we aim to (1) characterize the somatotopical specificity of this top down modulation and (2) investigate the effect of cognitive expectations on such modulation. Experiment 1 showed that the somatotopical specificity of the HBR enhancement is not only heterosegmental, but also partially homosegmental, i.e. the enhancement is greater for the HBR elicited by stimulation of the hand located inside the peripersonal space of the face, as compared to the HBR elicited by the stimulation of the other hand, always kept far from the face. Experiment 2 showed that the top-down modulation of the HBR is triggered only when the participants expect to receive stimuli on the hand placed inside the peripersonal space of the face, and is thus strongly dependent on cognitive expectations. Taken together, these findings indicate a fine somatotopical and cognitive tuning of the excitability of brainstem circuits subserving the HBR, whose strength is adjusted depending on the context in a purposeful manner

Making use of transcription factor enrichment to identify functional microRNA-regulons

microRNAs (miRNAs) are important modulators of messenger RNA stability and translation, controlling wide gene networks. Albeit generally modest on individual targets, the regulatory effect of miRNAs translates into meaningful pathway modulation through concurrent targeting of regulons with functional convergence. Identification of miRNA-regulons is therefore essential to understand the function of miRNAs and to help realise their therapeutic potential, but it remains challenging due to the large number of false positive target sites predicted per miRNA. In the current work, we investigated whether genes regulated by a given miRNA were under the transcriptional control of a predominant transcription factor (TF). Strikingly we found that for ~50% of the miRNAs analysed, their targets were significantly enriched in at least one common TF. We leveraged such miRNA-TF co-regulatory networks to identify pathways under miRNA control, and demonstrated that filtering predicted miRNA-target interactions (MTIs) relying on such pathways significantly enriched the proportion of predicted true MTIs. To our knowledge, this is the first description of an in- silico pipeline facilitating the identification of miRNA-regulons, to help understand miRNA function.Pacôme B. Prompsy, John Toubia, Linden J. Gearing, Randle L. Knight, Samuel C. Forster, Cameron P. Bracken, Michael P. Gantie

Evidence for geometry-dependent universal fluctuations of the Kardar-Parisi-Zhang interfaces in liquid-crystal turbulence

We provide a comprehensive report on scale-invariant fluctuations of growing interfaces in liquid-crystal turbulence, for which we recently found evidence that they belong to the Kardar-Parisi-Zhang (KPZ) universality class for 1+1 dimensions [Phys. Rev. Lett. 104, 230601 (2010); Sci. Rep. 1, 34 (2011)]. Here we investigate both circular and flat interfaces and report their statistics in detail. First we demonstrate that their fluctuations show not only the KPZ scaling exponents but beyond: they asymptotically share even the precise forms of the distribution function and the spatial correlation function in common with solvable models of the KPZ class, demonstrating also an intimate relation to random matrix theory. We then determine other statistical properties for which no exact theoretical predictions were made, in particular the temporal correlation function and the persistence probabilities. Experimental results on finite-time effects and extreme-value statistics are also presented. Throughout the paper, emphasis is put on how the universal statistical properties depend on the global geometry of the interfaces, i.e., whether the interfaces are circular or flat. We thereby corroborate the powerful yet geometry-dependent universality of the KPZ class, which governs growing interfaces driven out of equilibrium.Comment: 31 pages, 21 figures, 1 table; references updated (v2,v3); Fig.19 updated & minor changes in text (v3); final version (v4); J. Stat. Phys. Online First (2012

Review article: the future of microbiome-based therapeutics

Published online 24 May 2022Background: From consumption of fermented foods and probiotics to emerging applications of faecal microbiota transplantation, the health benefit of manipulating the human microbiota has been exploited for millennia. Despite this history, recent technological advances are unlocking the capacity for targeted microbial manipulation as a novel therapeutic.Aim: This review summarises the current developments in microbiome- based medicines and provides insight into the next steps required for therapeutic development.Methods: Here we review current and emerging approaches and assess the capabilities and weaknesses of these technologies to provide safe and effective clinical inter-ventions. Key literature was identified through Pubmed searches with the following key words, ‘microbiome’, ‘microbiome biomarkers’, ‘probiotics’, ‘prebiotics’, ‘synbiotics’, ‘faecal microbiota transplant’, ‘live biotherapeutics’, ‘microbiome mimetics’ and ‘postbiotics’.Results: Improved understanding of the human microbiome and recent technological advances provide an opportunity to develop a new generation of therapies. These therapies will range from dietary interventions, prebiotic supplementations, single probiotic bacterial strains, human donor-derived faecal microbiota transplants, ra-tionally selected combinations of bacterial strains as live biotherapeutics, and the beneficial products or effects produced by bacterial strains, termed microbiome mimetics.Conclusions: Although methods to identify and refine these therapeutics are continually advancing, the rapid emergence of these new approaches necessitates accepted technological and ethical frameworks for measurement, testing, laboratory practices and clinical translation.Emily L. Gulliver, Remy B. Young, Michelle Chonwerawong, Gemma L. D'Adamo, Tamblyn Thomason, James T. Widdop, Emily L. Rutten, Vanessa Rossetto Marcelino, Robert V. Bryant, Samuel P. Costello, Claire L. O'Brien, Georgina L. Hold, Edward M. Giles, Samuel C. Forste

Combination of searches for Higgs boson pairs in pp collisions at \sqrts = 13 TeV with the ATLAS detector

This letter presents a combination of searches for Higgs boson pair production using up to 36.1 fb(-1) of proton-proton collision data at a centre-of-mass energy root s = 13 TeV recorded with the ATLAS detector at the LHC. The combination is performed using six analyses searching for Higgs boson pairs decaying into the b (b) over barb (b) over bar, b (b) over barW(+)W(-), b (b) over bar tau(+)tau(-), W+W-W+W-, b (b) over bar gamma gamma and W+W-gamma gamma final states. Results are presented for non-resonant and resonant Higgs boson pair production modes. No statistically significant excess in data above the Standard Model predictions is found. The combined observed (expected) limit at 95% confidence level on the non-resonant Higgs boson pair production cross-section is 6.9 (10) times the predicted Standard Model cross-section. Limits are also set on the ratio (kappa(lambda)) of the Higgs boson self-coupling to its Standard Model value. This ratio is constrained at 95% confidence level in observation (expectation) to -5.0 &lt; kappa(lambda) &lt; 12.0 (-5.8 &lt; kappa(lambda) &lt; 12.0). In addition, limits are set on the production of narrow scalar resonances and spin-2 Kaluza-Klein Randall-Sundrum gravitons. Exclusion regions are also provided in the parameter space of the habemus Minimal Supersymmetric Standard Model and the Electroweak Singlet Model. For complete list of authors see http://dx.doi.org/10.1016/j.physletb.2019.135103</p

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa

Whole-genome sequencing of chronic lymphocytic leukemia identifies subgroups with distinct biological and clinical features

The value of genome-wide over targeted driver analyses for predicting clinical outcomes of cancer patients is debated. Here, we report the whole-genome sequencing of 485 chronic lymphocytic leukemia patients enrolled in clinical trials as part of the United Kingdom’s 100,000 Genomes Project. We identify an extended catalog of recurrent coding and noncoding genetic mutations that represents a source for future studies and provide the most complete high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and genomic complexity. We demonstrate the relationship of these features with clinical outcome and show that integration of 186 distinct recurrent genomic alterations defines five genomic subgroups that associate with response to therapy, refining conventional outcome prediction. While requiring independent validation, our findings highlight the potential of whole-genome sequencing to inform future risk stratification in chronic lymphocytic leukemia