Extension of nano-confined DNA: quantitative comparison between experiment and theory

The extension of DNA confined to nanochannels has been studied intensively and in detail. Yet quantitative comparisons between experiments and model calculations are difficult because most theoretical predictions involve undetermined prefactors, and because the model parameters (contour length, Kuhn length, effective width) are difficult to compute reliably, leading to substantial uncertainties. Here we use a recent asymptotically exact theory for the DNA extension in the "extended de Gennes regime" that allows us to compare experimental results with theory. For this purpose we performed new experiments, measuring the mean DNA extension and its standard deviation while varying the channel geometry, dye intercalation ratio, and ionic buffer strength. The experimental results agree very well with theory at high ionic strengths, indicating that the model parameters are reliable. At low ionic strengths the agreement is less good. We discuss possible reasons. Our approach allows, in principle, to measure the Kuhn length and effective width of a single DNA molecule and more generally of semiflexible polymers in solution.Comment: Revised version, 6 pages, 2 figures, 1 table, supplementary materia

A combined tactile and Raman probe for tissue characterization - Design considerations

Histopathology is the golden standard for cancer diagnosis and involves the characterization of tissue components. It is labour intensive and time consuming. We have earlier proposed a combined fibre-optic near-infrared Raman spectroscopy (NIR-RS) and tactile resonance method (TRM) probe for detecting positive surgical margins as a complement to interoperative histopathology. The aims of this study were to investigate the effects of attaching an RS probe inside a cylindrical TRM sensor and to investigate how laser-induced heating of the fibre-optic NIR-RS affected the temperature of the RS probe tip and an encasing TRM sensor. In addition, the possibility to perform fibre-optic NIR-RS in a well-lit environment was investigated. A small amount of rubber latex was preferable for attaching the thin RS probe inside the TRM sensor. The temperature rise of the TRM sensor due to a fibre-optic NIR-RS at 270 mW during 20 s was less than 2 degrees C. Fibre-optic NIR-RS was feasible in a dimmed bright environment using a small light shield and automatic subtraction of a pre-recorded contaminant spectrum. The results are promising for a combined probe for tissue characterization

Genetic evidence implicating natriuretic peptide receptor-3 in cardiovascular disease risk: a Mendelian randomization study

Background: C-type natriuretic peptide (CNP) is a known target for promoting growth and has been implicated as a therapeutic opportunity for the prevention and treatment of cardiovascular disease (CVD). This study aimed to explore the effect of CNP on CVD risk using the Mendelian randomization (MR) framework. Methods Instrumental variables mimicking the effects of pharmacological intervention on CNP were identified as uncorrelated genetic variants located in the genes coding for its primary receptors, natriuretic peptide receptors-2 and 3 (NPR2 and NPR3), that associated with height. We performed MR and colocalization analyses to investigate the effects of NPR2 signalling and NPR3 function on CVD outcomes and risk factors. MR estimates were compared to those obtained when considering height variants from throughout the genome. Results: Genetically-proxied reduced NPR3 function was associated with a lower risk of CVD, with odds ratio (OR) 0.74 per standard deviation (SD) higher NPR3-predicted height, and 95% confidence interval (95% CI) 0.64–0.86. This effect was greater in magnitude than observed when considering height variants from throughout the genome. For CVD subtypes, similar MR associations for NPR3-predicted height were observed when considering the outcomes of coronary artery disease (0.75, 95% CI 0.60–0.92), stroke (0.69, 95% CI 0.50–0.95) and heart failure (0.77, 95% CI 0.58–1.02). Consideration of CVD risk factors identified systolic blood pressure (SBP) as a potential mediator of the NPR3-related CVD risk lowering. For stroke, we found that the MR estimate for NPR3 was greater in magnitude than could be explained by a genetically predicted SBP effect alone. Colocalization results largely supported the MR findings, with no evidence of results being driven by effects due to variants in linkage disequilibrium. There was no MR evidence supporting effects of NPR2 on CVD risk, although this null finding could be attributable to fewer genetic variants being identified to instrument this target. Conclusions: This genetic analysis supports the cardioprotective effects of pharmacologically inhibiting NPR3 receptor function, which is only partly mediated by an effect on blood pressure. There was unlikely sufficient statistical power to investigate the cardioprotective effects of NPR2 signalling

Turning back the rising sea: Theory performativity in the shift from climate science to popular authority

Action on climate change continues to be hampered by vested interests seeding doubt about science and the need to reduce carbon emissions. Using a qualitative case study of local climate adaptation to sea level rise, we show how climate change science is translated into a self-referential theory focussed on property prices. Our analysis develops two mechanisms – enablement and theorization – to explain the relationship between theory performativity and power within a process of translation. This contributes to i) the performativity debate by showing how the constitution of power relations shapes theory performativity; ii) theories of power, by tracing the ways in which certain actors are able to enrol others and impact the authority of particular theories, and; iii) processes of translation by developing mechanisms for following the ways in which power and theory performativity interact. We conclude by arguing that a performative understanding of how power shapes beliefs is central to combat the failure to address climate change

Complete Experimental Structure Determination of the p(3x2)pg Phase of Glycine on Cu{110}

We present a quantitative low energy electron diffraction (LEED) surface-crystallograpic study of the complete adsorption geometry of glycine adsorbed on Cu{110} in the ordered p(3×2) phase. The glycine molecules form bonds to the surface through the N atoms of the amino group and the two O atoms of the de-protonated carboxylate group, each with separate Cu atoms such that every Cu atom in the first layer is involved in a bond. Laterally, N atoms are nearest to the atop site (displacement 0.41 Å). The O atoms are asymmetrically displaced from the atop site by 0.54 Å and 1.18 Å with two very different O-Cu bond lengths of 1.93 Å and 2.18 Å. The atom positions of the upper-most Cu layers show small relaxations within 0.07 Å of the bulk-truncated surface geometry. The unit cell of the adsorbate layer consists of two glycine molecules, which are related by a glide-line symmetry operation. This study clearly shows that a significant coverage of adsorbate structures without this glide-line symmetry must be rejected, both on the grounds of the energy dependence of the spot intensities (LEED-IV curves) and of systematic absences in the LEED pattern

Summary of Compression Testing of U-10Mo

The mechanical properties of depleted uranium plus 10 weight percent molybdenum alloy have been evaluated by high temperature compression testing

Exposure to environmental stressors result in increased viral load and further reduction of production parameters in pigs experimentally infected with PCV2b

Porcine circovirus type 2 (PCV2) has been identified as the essential, but not sole, underlying infectious component for PCV-associated diseases (PCVAD). Several co-factors have been suggested to convert an infection with PCV2 into the clinical signs of PCVAD, including co-infection with a secondary pathogen and the genetic background of the pig. In the present study, we investigated the role of environmental stressors in the form of changes in environmental temperature and increased stocking-density on viral load in serum and tissue, average daily weight gain (ADG) and food conversion rate (FCR) of pigs experimentally infected with a defined PCV2b strain over an eight week period. These stressors were identified recently as risk factors leading to the occurrence of severe PCVAD on a farm level. In the current study, PCV2-free pigs were housed in separate, environmentally controlled rooms, and the experiment was performed in a 2 × 2 factorial design. In general, PCV2b infection reduced ADG and increased FCR, and these were further impacted on by the environmental stressors. Furthermore, all stressors led to an increased viral load in serum and tissue as assessed by qPCR, although levels did not reach statistical significance. Our data suggest that there is no need for an additional pathogen to develop PCVAD in conventional status pigs, and growth retardation and clinical signs can be induced in PCV2 infected pigs that are exposed to environmental stressors alone

Long‐term risk of dementia following hospitalization due to physical diseases: A multicohort study

Introduction Conventional risk factors targeted by prevention (e.g., low education, smoking, and obesity) are associated with a 1.2‐ to 2‐fold increased risk of dementia. It is unclear whether having a physical disease is an equally important risk factor for dementia. Methods In this exploratory multicohort study of 283,414 community‐dwelling participants, we examined 22 common hospital‐treated physical diseases as risk factors for dementia. Results During a median follow‐up of 19 years, a total of 3416 participants developed dementia. Those who had erysipelas (hazard ratio = 1.82; 95% confidence interval = 1.53 to 2.17), hypothyroidism (1.94; 1.59 to 2.38), myocardial infarction (1.41; 1.20 to 1.64), ischemic heart disease (1.32; 1.18 to 1.49), cerebral infarction (2.44; 2.14 to 2.77), duodenal ulcers (1.88; 1.42 to 2.49), gastritis and duodenitis (1.82; 1.46 to 2.27), or osteoporosis (2.38; 1.75 to 3.23) were at a significantly increased risk of dementia. These associations were not explained by conventional risk factors or reverse causation. Discussion In addition to conventional risk factors, several physical diseases may increase the long‐term risk of dementia.Peer reviewe

Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

Pharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare. New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics

Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

Pharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare. New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics