Left to Their Own Devices: Breakdowns in United States Medical Device Premarket Review

Using examples from recent FDA regulatory proceedings, Jonas Hines and colleagues critique the medical device premarket review and identify eight weaknesses in the process that should be remedied

Beyond the Jaynes-Cummings model: circuit QED in the ultrastrong coupling regime

In cavity quantum electrodynamics (QED), light-matter interaction is probed at its most fundamental level, where individual atoms are coupled to single photons stored in three-dimensional cavities. This unique possibility to experimentally explore the foundations of quantum physics has greatly evolved with the advent of circuit QED, where on-chip superconducting qubits and oscillators play the roles of two-level atoms and cavities, respectively. In the strong coupling limit, atom and cavity can exchange a photon frequently before coherence is lost. This important regime has been reached both in cavity and circuit QED, but the design flexibility and engineering potential of the latter allowed for increasing the ratio between the atom-cavity coupling rate and the cavity transition frequency above the percent level. While these experiments are well described by the renowned Jaynes-Cummings model, novel physics is expected in the ultrastrong coupling limit. Here, we report on the first experimental realization of a superconducting circuit QED system in the ultrastrong coupling limit and present direct evidence for the breakdown of the Jaynes-Cummings model.Comment: 5 pages, 3 figure

Genes Suggest Ancestral Colour Polymorphisms Are Shared across Morphologically Cryptic Species in Arctic Bumblebees

email Suzanne orcd idCopyright: © 2015 Williams et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

The what and where of adding channel noise to the Hodgkin-Huxley equations

One of the most celebrated successes in computational biology is the Hodgkin-Huxley framework for modeling electrically active cells. This framework, expressed through a set of differential equations, synthesizes the impact of ionic currents on a cell's voltage -- and the highly nonlinear impact of that voltage back on the currents themselves -- into the rapid push and pull of the action potential. Latter studies confirmed that these cellular dynamics are orchestrated by individual ion channels, whose conformational changes regulate the conductance of each ionic current. Thus, kinetic equations familiar from physical chemistry are the natural setting for describing conductances; for small-to-moderate numbers of channels, these will predict fluctuations in conductances and stochasticity in the resulting action potentials. At first glance, the kinetic equations provide a far more complex (and higher-dimensional) description than the original Hodgkin-Huxley equations. This has prompted more than a decade of efforts to capture channel fluctuations with noise terms added to the Hodgkin-Huxley equations. Many of these approaches, while intuitively appealing, produce quantitative errors when compared to kinetic equations; others, as only very recently demonstrated, are both accurate and relatively simple. We review what works, what doesn't, and why, seeking to build a bridge to well-established results for the deterministic Hodgkin-Huxley equations. As such, we hope that this review will speed emerging studies of how channel noise modulates electrophysiological dynamics and function. We supply user-friendly Matlab simulation code of these stochastic versions of the Hodgkin-Huxley equations on the ModelDB website (accession number 138950) and http://www.amath.washington.edu/~etsb/tutorials.html.Comment: 14 pages, 3 figures, review articl

Vitamin D supplementation and breast cancer prevention : a systematic review and meta-analysis of randomized clinical trials

In recent years, the scientific evidence linking vitamin D status or supplementation to breast cancer has grown notably. To investigate the role of vitamin D supplementation on breast cancer incidence, we conducted a systematic review and meta-analysis of randomized controlled trials comparing vitamin D with placebo or no treatment. We used OVID to search MEDLINE (R), EMBASE and CENTRAL until April 2012. We screened the reference lists of included studies and used the “Related Article” feature in PubMed to identify additional articles. No language restrictions were applied. Two reviewers independently extracted data on methodological quality, participants, intervention, comparison and outcomes. Risk Ratios and 95% Confident Intervals for breast cancer were pooled using a random-effects model. Heterogeneity was assessed using the I2 test. In sensitivity analysis, we assessed the impact of vitamin D dosage and mode of administration on treatment effects. Only two randomized controlled trials fulfilled the pre-set inclusion criteria. The pooled analysis included 5372 postmenopausal women. Overall, Risk Ratios and 95% Confident Intervals were 1.11 and 0.74–1.68. We found no evidence of heterogeneity. Neither vitamin D dosage nor mode of administration significantly affected breast cancer risk. However, treatment efficacy was somewhat greater when vitamin D was administered at the highest dosage and in combination with calcium (Risk Ratio 0.58, 95% Confident Interval 0.23–1.47 and Risk Ratio 0.93, 95% Confident Interval 0.54–1.60, respectively). In conclusions, vitamin D use seems not to be associated with a reduced risk of breast cancer development in postmenopausal women. However, the available evidence is still limited and inadequate to draw firm conclusions. Study protocol code: FARM8L2B5L

Measuring symmetry, asymmetry and randomness in neural network connectivity

Cognitive functions are stored in the connectome, the wiring diagram of the brain, which exhibits non-random features, so-called motifs. In this work, we focus on bidirectional, symmetric motifs, i.e. two neurons that project to each other via connections of equal strength, and unidirectional, non-symmetric motifs, i.e. within a pair of neurons only one neuron projects to the other. We hypothesise that such motifs have been shaped via activity dependent synaptic plasticity processes. As a consequence, learning moves the distribution of the synaptic connections away from randomness. Our aim is to provide a global, macroscopic, single parameter characterisation of the statistical occurrence of bidirectional and unidirectional motifs. To this end we define a symmetry measure that does not require any a priori thresholding of the weights or knowledge of their maximal value. We calculate its mean and variance for random uniform or Gaussian distributions, which allows us to introduce a confidence measure of how significantly symmetric or asymmetric a specific configuration is, i.e. how likely it is that the configuration is the result of chance. We demonstrate the discriminatory power of our symmetry measure by inspecting the eigenvalues of different types of connectivity matrices. We show that a Gaussian weight distribution biases the connectivity motifs to more symmetric configurations than a uniform distribution and that introducing a random synaptic pruning, mimicking developmental regulation in synaptogenesis, biases the connectivity motifs to more asymmetric configurations, regardless of the distribution. We expect that our work will benefit the computational modelling community, by providing a systematic way to characterise symmetry and asymmetry in network structures. Further, our symmetry measure will be of use to electrophysiologists that investigate symmetry of network connectivity

Impact of Tumor Grade on Prognosis in Pancreatic Cancer: Should We Include Grade in AJCC Staging?

AJCC staging of pancreatic cancer (PAC) is used to determine prognosis, yet survival within each stage shows wide variation and remains unpredictable. We hypothesized that tumor grade might be responsible for some of this variation and that the addition of grade to current AJCC staging would provide improved prognostication. The Surveillance, Epidemiology, and End Results (SEER) database (1991–2005) was used to identify 8082 patients with resected PAC. The impact of grade on overall and stage-specific survival was assessed using Cox regression analysis. Variables in the model were age, sex, tumor size, lymph node status, and tumor grade. For each AJCC stage, survival was significantly worse for high-grade versus low-grade tumors. On multivariate analysis, high tumor grade was an independent predictor of survival for the entire cohort (hazard ratio [HR] 1.40, 95% confidence interval [95% CI] 1.31–1.48) as well as for stage I (HR 1.28, 95% CI 1.07–1.54), stage IIA (HR 1.43, 95% CI 1.26–1.61), stage IIB (HR 1.38, 95% CI 1.27–1.50), stage III (HR 1.28, 95% CI 1.02–1.59), and stage IV (HR 1.58, 95% CI 1.21–2.05) patients. The addition of grade to staging results in a statistically significant survival discrimination between all stages. Tumor grade is an important prognostic variable of survival in PAC. We propose a novel staging system incorporating grade into current AJCC staging for pancreas cancer. The improved prognostication is more reflective of tumor biology and may impact therapy decisions and stratification of future clinical trials

Bioluminescence Imaging of Angiogenesis in a Murine Orthotopic Pancreatic Cancer Model

Angiogenesis is essential for physiological processes as well as for carcinogenesis. New approaches to cancer therapy include targeting angiogenesis. One target is VEGF-A and its receptor VEGFR2. In this study, we sought to investigate pancreatic cancer angiogenesis in a genetically modified VEGFR2-luc-KI mouse

The genomes of two key bumblebee species with primitive eusocial organization

Background: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats. Results: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits. Conclusions: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation