Linear approaches to intramolecular Förster Resonance Energy Transfer probe measurements for quantitative modeling

Numerous unimolecular, genetically-encoded Forster Resonance Energy Transfer (FRET) probes for monitoring biochemical activities in live cells have been developed over the past decade. As these probes allow for collection of high frequency, spatially resolved data on signaling events in live cells and tissues, they are an attractive technology for obtaining data to develop quantitative, mathematical models of spatiotemporal signaling dynamics. However, to be useful for such purposes the observed FRET from such probes should be related to a biological quantity of interest through a defined mathematical relationship, which is straightforward when this relationship is linear, and can be difficult otherwise. First, we show that only in rare circumstances is the observed FRET linearly proportional to a biochemical activity. Therefore in most cases FRET measurements should only be compared either to explicitly modeled probes or to concentrations of products of the biochemical activity, but not to activities themselves. Importantly, we find that FRET measured by standard intensity-based, ratiometric methods is inherently non-linear with respect to the fraction of probes undergoing FRET. Alternatively, we find that quantifying FRET either via (1) fluorescence lifetime imaging (FLIM) or (2) ratiometric methods where the donor emission intensity is divided by the directly-excited acceptor emission intensity (denoted R<sub>alt</sub>) is linear with respect to the fraction of probes undergoing FRET. This linearity property allows one to calculate the fraction of active probes based on the FRET measurement. Thus, our results suggest that either FLIM or ratiometric methods based on R<sub>alt</sub> are the preferred techniques for obtaining quantitative data from FRET probe experiments for mathematical modeling purpose

Los jóvenes, los usos del tiempo y el consumo de drogas en espacios recreativos nocturnos

Consideramos que la vida recreativa nocturna, ámbito que se aborda en este proyecto, es uno de los espacios donde los jóvenes construyen sus identidades de maneras heterogéneas, al igual que lo hacen en su transitar por la familia, la escuela y el trabajo. Se intenta visibilizar la diversidad y pluralidad de los jóvenes que participan en el espacio recreativo nocturno, a través de sus formas de gestionar el tiempo y sus vínculos con el consumo de drogas. Además nos planteamos que existen especificidades en la gestión que realizan los jóvenes de su tiempo libre, particularmente, cuando concurren a espacios de diversión nocturna en ciudades de distinto tamaño. Otros interrogantes que guían el desarrollo de la investigación tienen que ver con las actitudes y prácticas diferenciales que encontremos en la gestión del tiempo libre nocturno por parte de las y los jóvenes, pertenecientes a distintos sectores socioeconómicos.Facultad de Trabajo Socia

Single-mode instability in standing-wave lasers: The quantum cascade laser as a self-pumped parametric oscillator

We report the observation of a clear single-mode instability threshold in continuous-wave Fabry-Perot quantum cascade lasers (QCLs). The instability is characterized by the appearance of sidebands separated by tens of free spectral ranges (FSR) from the first lasing mode, at a pump current not much higher than the lasing threshold. As the current is increased, higher-order sidebands appear that preserve the initial spacing, and the spectra are suggestive of harmonically phase-locked waveforms. We present a theory of the instability that applies to all homogeneously broadened standing-wave lasers. The low instability threshold and the large sideband spacing can be explained by the combination of an unclamped, incoherent Lorentzian gain due to the population grating, and a coherent parametric gain caused by temporal population pulsations that changes the spectral gain line shape. The parametric term suppresses the gain of sidebands whose separation is much smaller than the reciprocal gain recovery time, while enhancing the gain of more distant sidebands. The large gain recovery frequency of the QCL compared to the FSR is essential to observe this parametric effect, which is responsible for the multiple-FSR sideband separation. We predict that by tuning the strength of the incoherent gain contribution, for example by engineering the modal overlap factors and the carrier diffusion, both amplitude-modulated (AM) or frequency-modulated emission can be achieved from QCLs. We provide initial evidence of an AM waveform emitted by a QCL with highly asymmetric facet reflectivities, thereby opening a promising route to ultrashort pulse generation in the mid-infrared. Together, the experiments and theory clarify a deep connection between parametric oscillation in optically pumped microresonators and the single-mode instability of lasers, tying together literature from the last 60 years.United States. Defense Advanced Research Projects Agency. Spectral Combs from UV to THz Program (Grant W31P4Q-16-1-0002)National Science Foundation (U.S.) (Awards ECCS-1230477, ECCS-1614631 and ECCS- 1614531)United States. Dept. of Defense. Assistant Secretary of Defense for Research & Engineering (Air Force Contracts FA8721-05-C- 0002 and No. FA8702-15-D-0001

U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions

BACKGROUND: Shedding light on the neuroscientific mechanisms of human upper limb motor control, in both healthy and disease conditions (e.g., after a stroke), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. Furthermore, the design and control of mechatronic devices can also benefit from such neuroscientific outcomes, with important implications for assistive and rehabilitation robotics and advanced human-machine interaction. To reach these goals, we believe that an exhaustive data collection on human behavior is a mandatory step. For this reason, we release U-Limb, a large, multi-modal, multi-center data collection on human upper limb movements, with the aim of fostering trans-disciplinary cross-fertilization. CONTRIBUTION: This collection of signals consists of data from 91 able-bodied and 65 post-stroke participants and is organized at 3 levels: (i) upper limb daily living activities, during which kinematic and physiological signals (electromyography, electro-encephalography, and electrocardiography) were recorded; (ii) force-kinematic behavior during precise manipulation tasks with a haptic device; and (iii) brain activity during hand control using functional magnetic resonance imaging

The Intimin periplasmic domain mediates dimerisation and binding to peptidoglycan

Intimin and Invasin are prototypical inverse (Type Ve) autotransporters and important virulence factors of enteropathogenic Escherichia coli and Yersinia spp., respectively. In addition to a C-terminal extracellular domain and a β-barrel transmembrane domain, both proteins also contain a short N- terminal periplasmic domain that, in Intimin, includes a lysin motif (LysM), which is thought to mediate binding to peptidoglycan. We show that the periplasmic domain of Intimin – but not the shorter domain of Invasin – does bind to peptidoglycan both in vitro and in vivo, but only under acidic conditions. We present the solution structure of the Intimin LysM, which has an additional, potentially functionally relevant α-helix compared to other LysMs. In contrast to previous reports, we demonstrate that the periplasmic domain of Intimin mediates dimerisation. Our data suggests that the periplasmic domain contains two dimerisation interfaces. We further show that dimerisation and peptidoglycan binding are general features of LysM-containing inverse autotransporters. The periplasmic domain could be involved in autotransport, and peptidoglycan binding may aid in resisting mechanical and chemical stress during transit through the gastrointestinal tract

B‐cell receptors of EBV‐negative Burkitt lymphoma bind modified isoforms of autoantigens

Burkitt lymphoma (BL) represents the most aggressive B‐cell‐lymphoma. Beside the hallmark of IG‐MYC‐translocation, surface B‐cell receptor (BCR) is expressed, and mutations in the BCR pathway are frequent. Coincidental infections in endemic BL, and specific extra‐nodal sites suggest antigenic triggers. To explore this hypothesis, BCRs of BL cell lines and cases were screened for reactivities against a panel of bacterial lysates, lysates of Plasmodium falciparum, a custom‐made virome array and against self‐antigens, including post‐translationally modified antigens. An atypically modified, SUMOylated isoform of Bystin, that is, SUMO1‐BYSL was identified as the antigen of the BCR of cell line CA46. SUMO1‐BYSL was exclusively expressed in CA46 cells with K139 as site of the SUMOylation. Secondly, an atypically acetylated isoform of HSP40 was identified as the antigen of the BCR of cell line BL41. K104 and K179 were the sites of immunogenic acetylation, and the acetylated HSP40 isoform was solely present in BL41 cells. Functionally, addition of SUMO1‐BYSL and acetylated HSP40 induced BCR pathway activation in CA46 and BL41 cells, respectively. Accordingly, SUMO1‐BYSL‐ETA’ immunotoxin, produced by a two‐step intein‐based conjugation, led to the specific killing of CA46 cells. Autoantibodies directed against SUMO1‐BYSL were found in 3 of 14 (21.4%), and autoantibodies against acetylated HSP40 in 1/14(7.1%) patients with sporadic Burkitt‐lymphoma. No reactivities against antigens of the infectious agent spectrum could be observed. These results indicate a pathogenic role of autoreactivity evoked by immunogenic post‐translational modifications in a subgroup of sporadic BL including two EBV‐negative BL cell lines

B-cell receptor reactivity against Rothia mucilaginosa in nodular lymphocyte-predominant Hodgkin lymphoma

Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is a Hodgkin lymphoma expressing functional B-cell receptors (BCR). Recently, we described a dual stimulation model of IgD+ lymphocyte-predominant cells by Moraxella catarrhalis antigen RpoC and its superantigen MID/hag, associated with extralong CDR3 and HLA-DRB1*04 or HLADRB1*07 haplotype. The aim of the present study was to extend the antigen screening to further bacteria and viruses. The fragment antibody-binding (Fab) regions of seven new and 15 previously reported cases were analyzed. The reactivity of non-Moraxella spp.-reactive Fab regions against lysates of Rothia mucilaginosa was observed in 5/22 (22.7%) cases. Galactofuranosyl transferase (Gltf) and 2,3-butanediol dehydrogenase (Bdh) of R. mucilaginosa were identified by comparative silver- and immuno-staining in two-dimensional gels, with subsequent mass spectrometry and validation by western blots and enzyme-linked immunosorbent assay. Both R. mucilaginosa Gltf and Bdh induced BCR pathway activation and proliferation in vitro. Apoptosis was induced by recombinant Gltf/ETA’-immunotoxin conjugates in DEV cells expressing recombinant R. mucilaginosa-reactive BCR. Reactivity against M. catarrhalis RpoC was confirmed in 3/7 newly expressed BCR (total 10/22 reactive to Moraxella spp.), resulting in 15/22 (68.2%) cases with BCR reactivity against defined bacterial antigens. These findings strengthen the hypothesis of bacterial trigger contributing to subsets of NLPHL

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

BACKGROUND: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal. RESULTS: A hierarchical statistical model named kernel-imbedded Gaussian process (KIGP) is developed under a unified Bayesian framework for binary disease classification problems using microarray gene expression data. In particular, based on a probit regression setting, an adaptive algorithm with a cascading structure is designed to find the appropriate kernel, to discover the potentially significant genes, and to make the optimal class prediction accordingly. A Gibbs sampler is built as the core of the algorithm to make Bayesian inferences. Simulation studies showed that, even without any knowledge of the underlying generative model, the KIGP performed very close to the theoretical Bayesian bound not only in the case with a linear Bayesian classifier but also in the case with a very non-linear Bayesian classifier. This sheds light on its broader usability to microarray data analysis problems, especially to those that linear methods work awkwardly. The KIGP was also applied to four published microarray datasets, and the results showed that the KIGP performed better than or at least as well as any of the referred state-of-the-art methods did in all of these cases. CONCLUSION: Mathematically built on the kernel-induced feature space concept under a Bayesian framework, the KIGP method presented in this paper provides a unified machine learning approach to explore both the linear and the possibly non-linear underlying relationship between the target features of a given binary disease classification problem and the related explanatory gene expression data. More importantly, it incorporates the model parameter tuning into the framework. The model selection problem is addressed in the form of selecting a proper kernel type. The KIGP method also gives Bayesian probabilistic predictions for disease classification. These properties and features are beneficial to most real-world applications. The algorithm is naturally robust in numerical computation. The simulation studies and the published data studies demonstrated that the proposed KIGP performs satisfactorily and consistently