Arachnids secrete a fluid over their adhesive pads.

BACKGROUND: Many arachnids possess adhesive pads on their feet that help them climb smooth surfaces and capture prey. Spider and gecko adhesives have converged on a branched, hairy structure, which theoretically allows them to adhere solely by dry (solid-solid) intermolecular interactions. Indeed, the consensus in the literature is that spiders and their smooth-padded relatives, the solifugids, adhere without the aid of a secretion. METHODOLOGY AND PRINCIPAL FINDINGS: We investigated the adhesive contact zone of living spiders, solifugids and mites using interference reflection microscopy, which allows the detection of thin liquid films. Like insects, all the arachnids we studied left behind hydrophobic fluid footprints on glass (mean refractive index: 1.48-1.50; contact angle: 3.7-11.2°). Fluid was not always secreted continuously, suggesting that pads can function in both wet and dry modes. We measured the attachment forces of single adhesive setae from tarantulas (Grammostola rosea) by attaching them to a bending beam with a known spring constant and filming the resulting deflection. Individual spider setae showed a lower static friction at rest (26%±2.8 SE of the peak friction) than single gecko setae (Thecadactylus rapicauda; 96%±1.7 SE). This may be explained by the fact that spider setae continued to release fluid after isolation from the animal, lubricating the contact zone. SIGNIFICANCE: This finding implies that tarsal secretions occur within all major groups of terrestrial arthropods with adhesive pads. The presence of liquid in an adhesive contact zone has important consequences for attachment performance, improving adhesion to rough surfaces and introducing rate-dependent effects. Our results leave geckos and anoles as the only known representatives of truly dry adhesive pads in nature. Engineers seeking biological inspiration for synthetic adhesives should consider whether model species with fluid secretions are appropriate to their design goals

Ancestrally high elastic modulus of gecko setal β-keratin

Typical bulk adhesives are characterized by soft, tacky materials with elastic moduli well below 1 MPa. Geckos possess subdigital adhesives composed mostly of β-keratin, a relatively stiff material. Biological adhesives like those of geckos have inspired empirical and modelling research which predicts that even stiff materials can be effective adhesives if they take on a fibrillar form. The molecular structure of β-keratin is highly conserved across birds and reptiles, suggesting that material properties of gecko setae should be similar to that of β-keratin previously measured in birds, but this has yet to be established. We used a resonance technique to measure elastic bending modulus in two species of gecko from disparate habitats. We found no significant difference in elastic modulus between Gekko gecko (1.6 GPa±0.15 s.e.; n=24 setae) and Ptyodactylus hasselquistii (1.4 GPa±0.15 s.e.; n=24 setae). If the elastic modulus of setal keratin is conserved across species, it would suggest a design constraint that must be compensated for structurally, and possibly explain the remarkable variation in gecko adhesive morphology

Orientation angle and the adhesion of single gecko setae

We investigated the effects of orientation angle on the adhesion of single gecko setae using dual-axis microelectromechanical systems force sensors to simultaneously detect normal and shear force components. Adhesion was highly sensitive to the pitch angle between the substrate and the seta's stalk. Maximum lateral adhesive force was observed with the stalk parallel to the substrate, and adhesion decreased smoothly with increasing pitch. The roll orientation angle only needed to be roughly correct with the spatular tuft of the seta oriented grossly towards the substrate for high adhesion. Also, detailed measurements were made to control for the effect of normal preload forces. Higher normal preload forces caused modest enhancement of the observed lateral adhesive force, provided that adequate contact was made between the seta and the substrate. These results should be useful in the design and manufacture of gecko-inspired synthetic adhesives with anisotropic properties, an area of substantial recent research efforts

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics

Acknowledgements: This work was supported by the Gates Cambridge Trust (OPP 1144) [A.I.L.]; the Canadian Institute for Advanced Research (CIFAR; grant RCZB/072 RG93193) [to D.K.M. and E.A.S.]; The National Institute for Health Research (NIHR, UK), Cambridge Biomedical Research Centre and NIHR Senior Investigator Awards [D.K.M.]; The British Oxygen Professorship of the Royal College of Anaesthetists [D.K.M.]; The Stephen Erskine Fellowship, Queens’ College, University of Cambridge [E.A.S.]; the Wellcome Trust Research Training Fellowship (grant no. 083660/Z/07/Z), Raymond and Beverly Sackler Studentship, and the Cambridge Commonwealth Trust [to R.A.]; the Medical Research Council Doctoral Training Grant (#RG86932) [H.M.G.]; Pinsent Darwin Award [H.M.G.]; Joachim Herz Foundation Add-on Fellowship in Interdisciplinary Life Sciences [H.M.G.]; MRC grant MR/K004360/1 [S.I.D.], a Marie Sklodowska-Curie COFUND EU-UK Research Fellowship [S.I.D.], a Beatriu de Pinós fellowship (2020 BP 00116) [S.I.D.]; AMO acknowledges support by the Canada Excellence Research Chairs program (215063); LN acknowledges support by the L’Oreal-Unesco for Women in Science Excellence Research Fellowship; ZQL is supported by the Fonds de Recherche du Quebec - Nature et Technologies (FRQNT). Acquisition of the NYU test–retest dataset was funded by Stavros S. Niarchos Foundation, the Leon Lowenstein Foundation, NARSAD (The Mental Health Research Association) grants to F.Xavier Castellanos; and Linda and Richard Schaps, Jill and Bob Smith, and the Taubman Foundation gifts to F. Xavier Castellanos. This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/T022159/1), and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk). For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.Funder: Fonds de Recherche du Québec - Nature et Technologies (Quebec Fund for Research in Nature and Technology); doi: https://doi.org/10.13039/501100003151Funder: L’Oreal-Unesco for Women in Science Excellence Research FellowshipFunder: EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 Marie Skłodowska-Curie Actions (H2020 Excellent Science - Marie Skłodowska-Curie Actions); doi: https://doi.org/10.13039/100010665Funder: Beatriu de Pinós fellowship (2020 BP 00116)Funder: Canadian Institute for Advanced Research (L'Institut Canadien de Recherches Avancées)Funder: Stephen Erskine Fellowship, Queens’ College, University of CambridgeAbstractFunctional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines’ suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline’s performance across criteria and datasets, to inform future best practices in functional connectomics.</jats:p

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics.

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.This work was supported by the Gates Cambridge Trust (OPP 1144) [AIL]; the Canadian Institute for Advanced Research (CIFAR; grant RCZB/072 RG93193) [to DKM and EAS]; The National Institute for Health Research (NIHR, UK), Cambridge Biomedical Research Centre and NIHR Senior Investigator Awards [DKM]; The British Oxygen Professorship of the Royal College of Anaesthetists [DKM]; The Stephen Erskine Fellowship, Queens’ College, University of Cambridge [EAS]; the Wellcome Trust Research Training Fellowship (grant no. 083660/Z/07/Z), Raymond and Beverly Sackler Studentship, and the Cambridge Commonwealth Trust [to RA]; the Medical Research Council Doctoral Training Grant (#RG86932) [HMG]; Pinsent Darwin Award [HMG]; Joachim Herz Foundation Add-on Fellowship in Interdisciplinary Life Sciences [HMG]; MRC grant MR/K004360/1 [SID], a Marie Sklodowska-Curie COFUND EU-UK Research Fellowship [SID], a Beatriu de Pinós fellowship (2020 BP 00116) [SID]; AMO acknowledges support by the Canada Excellence Research Chairs program (215063); LN acknowledges support by the L’Oreal-Unesco for Women in Science Excellence Research Fellowship; ZQL is supported by the Fonds de Recherche du Quebec - Nature et Technologies (FRQNT). Acquisition of the NYU Test-Retest dataset was funded by Stavros S. Niarchos Foundation, the Leon Lowenstein Foundation, NARSAD (The Mental Health Research Association) grants to F.Xavier Castellanos; and Linda and Richard Schaps, Jill and Bob Smith, and the Taubman Foundation gifts to F. Xavier Castellanos. This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/T022159/1), and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk)

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines’ suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline’s performance across criteria and datasets, to inform future best practices in functional connectomics

Dopaminergic brainstem disconnection is common to pharmacological and pathological consciousness perturbation.

Clinical research into consciousness has long focused on cortical macroscopic networks and their disruption in pathological or pharmacological consciousness perturbation. Despite demonstrating diagnostic utility in disorders of consciousness (DoC) and monitoring anesthetic depth, these cortico-centric approaches have been unable to characterize which neurochemical systems may underpin consciousness alterations. Instead, preclinical experiments have long implicated the dopaminergic ventral tegmental area (VTA) in the brainstem. Despite dopaminergic agonist efficacy in DoC patients equally pointing to dopamine, the VTA has not been studied in human perturbed consciousness. To bridge this translational gap between preclinical subcortical and clinical cortico-centric perspectives, we assessed functional connectivity changes of a histologically characterized VTA using functional MRI recordings of pharmacologically (propofol sedation) and pathologically perturbed consciousness (DoC patients). Both cohorts demonstrated VTA disconnection from the precuneus and posterior cingulate (PCu/PCC), a main default mode network node widely implicated in consciousness. Strikingly, the stronger VTA-PCu/PCC connectivity was, the more the PCu/PCC functional connectome resembled its awake configuration, suggesting a possible neuromodulatory relationship. VTA-PCu/PCC connectivity increased toward healthy control levels only in DoC patients who behaviorally improved at follow-up assessment. To test whether VTA-PCu/PCC connectivity can be affected by a dopaminergic agonist, we demonstrated in a separate set of traumatic brain injury patients without DoC that methylphenidate significantly increased this connectivity. Together, our results characterize an in vivo dopaminergic connectivity deficit common to reversible and chronic consciousness perturbation. This noninvasive assessment of the dopaminergic system bridges preclinical and clinical work, associating dopaminergic VTA function with macroscopic network alterations, thereby elucidating a critical aspect of brainstem-cortical interplay for consciousness.This work was supported by a grant from the Wellcome Trust: Clinical Research Training Fellowship [to R.A.] (Contract grant number: 083660/Z/07/Z). The work was also supported by grants from the UK Medical Research Council (U.1055.01.002.00001.01) [to J.D.P.]; The James S. McDonnell Foundation [to J.D.P.]; The Canadian Institute for Advanced Research (CIFAR) [to D.K.M. and E.A.S.]; The National Institute for Health Research (NIHR, UK), Cambridge Biomedical Research Centre and NIHR Senior Investigator Awards [to J.D.P. and D.K.M.]; The British Oxygen Professorship of the Royal College of Anaesthetists [to D.K.M.]; The Cambridge International Trust and the Cambridge European Scholarship [to LRBS.]; The Evelyn Trust, Cambridge and the EoE CLAHRC fellowship [to J.A.]; The Stephen Erskine Fellowship, Queens’ College, University of Cambridge [to E.A.S.] and the Gates Cambridge Trust [to A.I.L.]. The research was also supported by the NIHR Brain Injury Healthcare Technology Cooperative based at Cambridge University Hospitals NHS Foundation Trust and University of Cambridge. Computing infrastructure at the Wolfson Brain Imaging Centre. (WBIC-HPHI) was funded by MRC research infrastructure award (MR/M009041/1)