The Painful Long Head of the Biceps Brachii: Nonoperative Treatment Approaches

Pain associated with the long head of the biceps (LHB) brachii seems to be increasingly recognized in the past 4 to 5 years. The LHB has long been considered a troublesome pain generator in the shoulder. Abnormality involving the LHB brachii has long been an area of debate, with Codman in 1934 even questioning the specificity of the diagnosis of biceps tendinitis. Biceps tendon abnormality is often associated with rotator cuff impingement. Shoulder pain originating from the biceps tendon can be debilitating, causing a severe decrease in shoulder function. As a result of the frequent clinical presentation of biceps pain, there is currently a great deal of interest regarding the diagnosis, treatment, and prevention of biceps abnormality. This article describes a classification system of LHB pain and discusses nonoperative treatment concepts and techniques for the painful LHB

Vibronic resonances facilitate excited state coherence in light harvesting proteins at room temperature

Until recently it was believed that photosynthesis, a fundamental process for life on earth, could be fully understood with semi-classical models. However, puzzling quantum phenomena have been observed in several photosynthetic pigment-protein complexes, prompting questions regarding the nature and role of these effects. Recent attention has focused on discrete vibrational modes that are resonant or quasi-resonant with excitonic energy splittings and strongly coupled to these excitonic states. Here we unambiguously identify excited state coherent superpositions in photosynthetic light-harvesting complexes using a new experimental approach. Decoherence on the timescale of the excited state lifetime allows low energy (56 cm-1) oscillations on the signal intensity to be observed. In conjunction with an appropriate model, these oscillations provide clear and direct experimental evidence that the persistent coherences observed require strong vibronic mixing among excited states

Can cosmic strangelets reach the earth?

The mechanism for the propagation of strangelets with low baryon number through the atmosphere of the Earth has been explored. It has been shown that under suitable initial conditions, such strangelets may indeed reach depths near mountain altitudes with mass numbers and charges close to the observed values in cosmic ray experiments.Comment: RevTeX text, with 3 encoded eps figures. To appear in Physical Review Letter

Fluctuations of g-factors in metal nanoparticles: Effects of electron-electron interaction and spin-orbit scattering

We investigate the combined effect of spin-orbit scattering and electron-electron interactions on the probability distribution of $g$-factors of metal nanoparticles. Using random matrix theory, we find that even a relatively small interaction strength %(ratio of exchange constant $J$ and mean level %spacing \spacing $\simeq 0.3$) significantly increases $g$-factor fluctuations for not-too-strong spin-orbit scattering (ratio of spin-orbit rate and single-electron level spacing 1/\tau_{\rm so} \spacing \lesssim 1), and leads to the possibility to observe $g$-factors larger than two.Comment: RevTex, 2 figures inserte

Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models

Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have been applied to both regression and non-linear dimensionality reduction, and offer desirable properties such as uncertainty estimates, robustness to over-fitting, and principled ways for tuning hyper-parameters. However the scalability of these models to big datasets remains an active topic of research. We introduce a novel re-parametrisation of variational inference for sparse GP regression and latent variable models that allows for an efficient distributed algorithm. This is done by exploiting the decoupling of the data given the inducing points to re-formulate the evidence lower bound in a Map-Reduce setting. We show that the inference scales well with data and computational resources, while preserving a balanced distribution of the load among the nodes. We further demonstrate the utility in scaling Gaussian processes to big data. We show that GP performance improves with increasing amounts of data in regression (on flight data with 2 million records) and latent variable modelling (on MNIST). The results show that GPs perform better than many common models often used for big data.Comment: 9 pages, 8 figure

VarSight: prioritizing clinically reported variants with binary classification algorithms.

BackgroundWhen applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient's phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance.MethodsWe tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network.ResultsWe treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20.ConclusionsWe demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets