Correction of dephasing oscillations in matter-wave interferometry

Vibrations, electromagnetic oscillations, and temperature drifts are among the main reasons for dephasing in matter-wave interferometry. Sophisticated interferometry experiments, e.g., with ions or heavy molecules, often require integration times of several minutes due to the low source intensity or the high velocity selection. Here we present a scheme to suppress the influence of such dephasing mechanisms—especially in the low-frequency regime—by analyzing temporal and spatial particle correlations available in modern detectors. Such correlations can reveal interference properties that would otherwise be washed out due to dephasing by external oscillating signals. The method is shown experimentally in a biprism electron interferometer where a perturbing oscillation is artificially introduced by a periodically varying magnetic field. We provide a full theoretical description of the particle correlations where the perturbing frequency and amplitude can be revealed from the disturbed interferogram. The original spatial fringe pattern without the perturbation can thereby be restored. The technique can be applied to lower the general noise requirements in matter-wave interferometers. It allows for the optimization of electromagnetic shielding and decreases the efforts for vibrational or temperature stabilization.

Computed Ultrasound Tomography in Echo mode (CUTE) of speed of sound for diagnosis and for aberration correction in pulse-echo sonography

Sound speed as a diagnostic marker for various diseases of human tissue has been of interest for a while. Up to now, mostly transmission ultrasound computed tomography (UCT) was able to detect spatially resolved sound speed, and its promise as a diagnostic tool has been demonstrated. However, UCT is limited to acoustically transparent samples such as the breast. We present a novel technique where spatially resolved detection of sound speed can be achieved using conventional pulse-echo equipment in reflection mode. For this purpose, pulse-echo images are acquired under various transmit beam directions and a two-dimensional map of the sound speed is reconstructed from the changing phase of local echoes using a direct reconstruction method. Phantom results demonstrate that a high spatial resolution (1 mm) and contrast (0.5 % of average sound speed) can be achieved suitable for diagnostic purposes. In comparison to previous reflection-mode based methods, CUTE works also in a situation with only diffuse echoes, and its direct reconstruction algorithm enables real-time application. This makes it suitable as an addition to conventional clinical ultrasound where it has the potential to benefit diagnosis in a multimodal approach. In addition, knowledge of the spatial distribution of sound speed allows full aberration correction and thus improved spatial resolution and contrast of conventional B-mode ultrasound. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Functional correction in mouse models of muscular dystrophy using exon-skipping tricyclo-DNA oligomers

Antisense oligonucleotides (AONs) hold promise for therapeutic correction of many genetic diseases via exon skipping, and the first AON-based drugs have entered clinical trials for neuromuscular disorders1, 2. However, despite advances in AON chemistry and design, systemic use of AONs is limited because of poor tissue uptake, and recent clinical reports confirm that sufficient therapeutic efficacy has not yet been achieved. Here we present a new class of AONs made of tricyclo-DNA (tcDNA), which displays unique pharmacological properties and unprecedented uptake by many tissues after systemic administration. We demonstrate these properties in two mouse models of Duchenne muscular dystrophy (DMD), a neurogenetic disease typically caused by frame-shifting deletions or nonsense mutations in the gene encoding dystrophin3, 4 and characterized by progressive muscle weakness, cardiomyopathy, respiratory failure5 and neurocognitive impairment6. Although current naked AONs do not enter the heart or cross the blood-brain barrier to any substantial extent, we show that systemic delivery of tcDNA-AONs promotes a high degree of rescue of dystrophin expression in skeletal muscles, the heart and, to a lesser extent, the brain. Our results demonstrate for the first time a physiological improvement of cardio-respiratory functions and a correction of behavioral features in DMD model mice. This makes tcDNA-AON chemistry particularly attractive as a potential future therapy for patients with DMD and other neuromuscular disorders or with other diseases that are eligible for exon-skipping approaches requiring whole-body treatment.

Classification of interstitial lung disease patterns using local DCT features and random forest.

Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.

Multinomial goodness-of-fit: large sample tests with survey design correction and exact tests for small samples

I introduce the new mgof command to compute distributional tests for discrete (categorical, multinomial) variables. The command supports largesample tests for complex survey designs and exact tests for small samples as well as classic large-sample x2-approximation tests based on Pearson’s X2, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read, 1984, Journal of the Royal Statistical Society, Series B (Methodological) 46: 440–464). The complex survey correction is based on the approach by Rao and Scott (1981, Journal of the American Statistical Association 76: 221–230) and parallels the survey design correction used for independence tests in svy: tabulate. mgof computes the exact tests by using Monte Carlo methods or exhaustive enumeration. mgof also provides an exact one-sample Kolmogorov–Smirnov test for discrete data.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

A large animal model of spinal muscular atrophy and correction of phenotype.

OBJECTIVES Spinal muscular atrophy (SMA) is caused by reduced levels of survival motor neuron (SMN) protein, which results in motoneuron loss. Therapeutic strategies to increase SMN levels including drug compounds, antisense oligonucleotides, and scAAV9 gene therapy have proved effective in mice. We wished to determine whether reduction of SMN in postnatal motoneurons resulted in SMA in a large animal model, whether SMA could be corrected after development of muscle weakness, and the response of clinically relevant biomarkers. METHODS Using intrathecal delivery of scAAV9 expressing an shRNA targeting pig SMN1, SMN was knocked down in motoneurons postnatally to SMA levels. This resulted in an SMA phenotype representing the first large animal model of SMA. Restoration of SMN was performed at different time points with scAAV9 expressing human SMN (scAAV9-SMN), and electrophysiology measurements and pathology were performed. RESULTS Knockdown of SMN in postnatal motoneurons results in overt proximal weakness, fibrillations on electromyography indicating active denervation, and reduced compound muscle action potential (CMAP) and motor unit number estimation (MUNE), as in human SMA. Neuropathology showed loss of motoneurons and motor axons. Presymptomatic delivery of scAAV9-SMN prevented SMA symptoms, indicating that all changes are SMN dependent. Delivery of scAAV9-SMN after symptom onset had a marked impact on phenotype, electrophysiological measures, and pathology. INTERPRETATION High SMN levels are critical in postnatal motoneurons, and reduction of SMN results in an SMA phenotype that is SMN dependent. Importantly, clinically relevant biomarkers including CMAP and MUNE are responsive to SMN restoration, and abrogation of phenotype can be achieved even after symptom onset.

U7 snRNAs induce correction of mutated dystrophin pre-mRNA by exon skipping.

Most cases of Duchenne muscular dystrophy are caused by dystrophin gene mutations that disrupt the mRNA reading frame. Artificial exclusion (skipping) of a single exon would often restore the reading frame, giving rise to a shorter, but still functional dystrophin protein. Here, we analyzed the ability of antisense U7 small nuclear (sn)RNA derivatives to alter dystrophin pre-mRNA splicing. As a proof of principle, we first targeted the splice sites flanking exon 23 of dystrophin pre-mRNA in the wild-type muscle cell line C2C12 and showed precise exon 23 skipping. The same strategy was then successfully adapted to dystrophic immortalized mdx muscle cells where exon-23-skipped dystrophin mRNA rescued dystrophin protein synthesis. Moreover, we observed a stimulation of antisense U7 snRNA expression by the murine muscle creatine kinase enhancer. These results demonstrate that alteration of dystrophin pre-mRNA splicing could correct dystrophin gene mutations by expression of specific U7 snRNA constructs.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon’s implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike’s preceding ISI. As we show, the EIF’s exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron’s ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation.

Regularity conditions in the realisability problem in applications to point processes and random closed sets

We study existence of random elements with partially specified distributions. The technique relies on the existence of a positive ex-tension for linear functionals accompanied by additional conditions that ensure the regularity of the extension needed for interpreting it as a probability measure. It is shown in which case the extens ion can be chosen to possess some invariance properties. The results are applied to the existence of point processes with given correlation measure and random closed sets with given two-point covering function or contact distribution function. It is shown that the regularity condition can be efficiently checked in many cases in order to ensure that the obtained point processes are indeed locally finite and random sets have closed realisations.

On degeneracy and invariances of random fields paths with applications in Gaussian process modelling

We study pathwise invariances and degeneracies of random fields with motivating applications in Gaussian process modelling. The key idea is that a number of structural properties one may wish to impose a priori on functions boil down to degeneracy properties under well-chosen linear operators. We first show in a second order set-up that almost sure degeneracy of random field paths under some class of linear operators defined in terms of signed measures can be controlled through the two first moments. A special focus is then put on the Gaussian case, where these results are revisited and extended to further linear operators thanks to state-of-the-art representations. Several degeneracy properties are tackled, including random fields with symmetric paths, centred paths, harmonic paths, or sparse paths. The proposed approach delivers a number of promising results and perspectives in Gaussian process modelling. In a first numerical experiment, it is shown that dedicated kernels can be used to infer an axis of symmetry. Our second numerical experiment deals with conditional simulations of a solution to the heat equation, and it is found that adapted kernels notably enable improved predictions of non-linear functionals of the field such as its maximum.