A novel point-to-point length measurement concept based on range-resolved interferometry

In many fields of research and high-value industry, the estimation of distances and displacements is crucial. Due to their extremely high spatial resolution and flexible application possibilities interferometers are cross-sectorally used in measurement practice. However, classical length measuring interferometers are subject to two residual restrictions. On the one hand, only displacements that are exactly aligned to the interferometer optical axis can be measured. On the other hand, deviating refractive indices in the measuring and reference arm due to different atmospheric conditions represent an accuracy-limiting disturbance. In this paper, a new interferometric concept for length measurement is presented. The concept is based on the range-resolved interferometry technology which enables the simultaneous readout and evaluation of two symmetrical interferometric signals which result from the superposition of two non-collimated spherical wavefronts. This allows a point-to-point measurement between two optical fiber ends and the separation of undesired changes of the optical path length outside the measurement cavity and within the measurement cavity

A heterodyne interferometer with separated beam paths for high-precision displacement and angular measurements

As standard concepts for precision positioning within a machine reach their limits with increasing measurement volumes, inverse concepts are a promising approach for addressing this problem. The inverse principle entails other limitations, as for high-precision positioning of a sensor head within a large measurement volume, three four-beam interferometers are required in order to measure all necessary translations and rotations of the sensor head and reconstruct the topography of the reference system consisting of fixed mirrors in the x-, y-, and z-directions. We present the principle of a passive heterodyne laser interferometer with consequently separated beam paths for the individual heterodyne frequencies. The beam path design is illustrated and described, as well as the design of the signal-processing and evaluation algorithm, which is implemented using a System-On-a-Chip with an integrated FPGA, CPU, and A/D converters. A streamlined bench-top optical assembly was set up and measurements were carried out to investigate the remaining non-linearities. Additionally, reference measurements with a commercial homodyne interferometer were executed

Heterodynes Interferometer mit vier Strahlen für hochpräzise Längen- und Winkel-messung in Nanopositionier- und Nanomessmaschinen

Um gestiegenen Anforderungen an das Messvolumen von Nanopositionier- und Nanomessmaschinen Rechnung zu tragen, wurde ein neues inverses Konzept entwickelt. Für die hochpräzise Positionierung des Sensorkopfs im Messraum werden dabei vier interferometrische Messachsen für jede Raumrichtung benötigt. Es wird ein Heterodyn-Laserinterferometer für diese Aufgabe vorgestellt sowie Messergebnisse präsentiert

Microbiota tryptophan metabolism induces aryl hydrocarbon receptor activation and improves alcohol-induced liver injury

International audienceObjective: Chronic alcohol consumption is an important cause of liver-related deaths. Specific intestinal microbiota profiles are associated with susceptibility or resistance to alcoholic liver disease in both mice and humans. We aimed to identify the mechanisms by which targeting intestinal microbiota can improve alcohol-induced liver lesions.Design: We used human associated mice, a mouse model of alcoholic liver disease transplanted with the intestinal microbiota of alcoholic patients and used the prebiotic, pectin, to modulate the intestinal microbiota. Based on metabolomic analyses, we focused on microbiota tryptophan metabolites, which are ligands of the aryl hydrocarbon receptor (AhR). Involvement of the AhR pathway was assessed using both a pharmacological approach and AhR-deficient mice.Results: Pectin treatment modified the microbiome and metabolome in human microbiota-associated alcohol-fed mice, leading to a specific faecal signature. High production of bacterial tryptophan metabolites was associated with an improvement of liver injury. The AhR agonist Ficz (6-formylindolo (3,2-b) carbazole) reduced liver lesions, similarly to prebiotic treatment. Conversely, inactivation of the ahr gene in alcohol-fed AhR knock-out mice abrogated the beneficial effects of the prebiotic. Importantly, patients with severe alcoholic hepatitis have low levels of bacterial tryptophan derivatives that are AhR agonists.Conclusions: Improvement of alcoholic liver disease by targeting the intestinal microbiota involves the AhR pathway, which should be considered as a new therapeutic target

Molecular Insights into Mammalian End-binding Protein Heterodimerization*

Microtubule plus-end tracking proteins (+TIPs) are involved in many microtubule-based processes. End binding (EB) proteins constitute a highly conserved family of +TIPs. They play a pivotal role in regulating microtubule dynamics and in the recruitment of diverse +TIPs to growing microtubule plus ends. Here we used a combination of methods to investigate the dimerization properties of the three human EB proteins EB1, EB2, and EB3. Based on Förster resonance energy transfer, we demonstrate that the C-terminal dimerization domains of EBs (EBc) can readily exchange their chains in solution. We further document that EB1c and EB3c preferentially form heterodimers, whereas EB2c does not participate significantly in the formation of heterotypic complexes. Measurements of the reaction thermodynamics and kinetics, homology modeling, and mutagenesis provide details of the molecular determinants of homo- versus heterodimer formation of EBc domains. Fluorescence spectroscopy and nuclear magnetic resonance studies in the presence of the cytoskeleton-associated protein-glycine-rich domains of either CLIP-170 or p150glued or of a fragment derived from the adenomatous polyposis coli tumor suppressor protein show that chain exchange of EBc domains can be controlled by binding partners. Extension of these studies of the EBc domains to full-length EBs demonstrate that heterodimer formation between EB1 and EB3, but not between EB2 and the other two EBs, occurs both in vitro and in cells as revealed by live cell imaging. Together, our data provide molecular insights for rationalizing the dominant negative control by C-terminal EB domains and form a basis for understanding the functional role of heterotypic chain exchange by EBs in cells

Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

Neuroanatomical findings on youth anxiety disorders are notoriously difficult to replicate, small in effect size and have limited clinical relevance. These concerns have prompted a paradigm shift toward highly powered (that is, big data) individual-level inferences, which are data driven, transdiagnostic and neurobiologically informed. Here we built and validated supervised neuroanatomical machine learning models for individual-level inferences, using a case–control design and the largest known neuroimaging database on youth anxiety disorders: the ENIGMA-Anxiety Consortium (N = 3,343; age = 10–25 years; global sites = 32). Modest, yet robust, brain-based classifications were achieved for specific anxiety disorders (panic disorder), but also transdiagnostically for all anxiety disorders when patients were subgrouped according to their sex, medication status and symptom severity (area under the receiver operating characteristic curve, 0.59–0.63). Classifications were driven by neuroanatomical features (cortical thickness, cortical surface area and subcortical volumes) in fronto-striato-limbic and temporoparietal regions. This benchmark study within a large, heterogeneous and multisite sample of youth with anxiety disorders reveals that only modest classification performances can be realistically achieved with machine learning using neuroanatomical data

Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

Neuroanatomical findings on youth anxiety disorders are notoriously difficult to replicate, small in effect size and have limited clinical relevance. These concerns have prompted a paradigm shift toward highly powered (that is, big data) individual-level inferences, which are data driven, transdiagnostic and neurobiologically informed. Here we built and validated supervised neuroanatomical machine learning models for individual-level inferences, using a case–control design and the largest known neuroimaging database on youth anxiety disorders: the ENIGMA-Anxiety Consortium (N = 3,343; age = 10–25 years; global sites = 32). Modest, yet robust, brain- based classifications were achieved for specific anxiety disorders (panic disorder), but also transdiagnostically for all anxiety disorders when patients were subgrouped according to their sex, medication status and symptom severity (area under the receiver operating characteristic curve, 0.59–0.63). Classifications were driven by neuroanatomical features (cortical thickness, cortical surface area and subcortical volumes) in fronto- striato-limbic and temporoparietal regions. This benchmark study within a large, heterogeneous and multisite sample of youth with anxiety disorders reveals that only modest classification performances can be realistically achieved with machine learning using neuroanatomical data

Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

Neuroanatomical findings on youth anxiety disorders are notoriously difficult to replicate, small in effect size and have limited clinical relevance. These concerns have prompted a paradigm shift toward highly powered (that is, big data) individual-level inferences, which are data driven, transdiagnostic and neurobiologically informed. Here we built and validated supervised neuroanatomical machine learning models for individual-level inferences, using a case–control design and the largest known neuroimaging database on youth anxiety disorders: the ENIGMA-Anxiety Consortium (N = 3,343; age = 10–25 years; global sites = 32). Modest, yet robust, brain-based classifications were achieved for specific anxiety disorders (panic disorder), but also transdiagnostically for all anxiety disorders when patients were subgrouped according to their sex, medication status and symptom severity (area under the receiver operating characteristic curve, 0.59–0.63). Classifications were driven by neuroanatomical features (cortical thickness, cortical surface area and subcortical volumes) in fronto-striato-limbic and temporoparietal regions. This benchmark study within a large, heterogeneous and multisite sample of youth with anxiety disorders reveals that only modest classification performances can be realistically achieved with machine learning using neuroanatomical data

Is the difference between right and left ATLs due to the distinction between general and social cognition or between verbal and non-verbal representations?

The present review aimed to check two proposals alternative to the original version of the 'semantic hub' hypothesis, based on semantic dementia (SD) data, which assumed that left and right anterior temporal lobes (ATLs) store in a unitary, amodal format all kinds of semantic representations.The first alternative proposal is that the right ATL might subsume non-verbal representations and the left ATL lexical-semantic representations and that only in the advanced stages of SD, when atrophy affects the ATLs bilaterally, the semantic impairment becomes 'multi-modal'.The second alternative suggestion is that right and left ATLs might underlie two different domains of knowledge, because general conceptual knowledge might be supported by the left ATL, and social cognition by the right ATL.Results of the review substantially support the first proposal, showing that the right ATL subsumes non-verbal representations and the left ATL lexical-semantic representations. They are less conclusive about the second suggestion, because the right ATL seems to play a more important role in behavioral and emotional functions than in higher level social cognition