Substitutions in the redox-sensing PAS domain of the NifL regulatory protein define an inter-subunit pathway for redox signal transmission

The Per-ARNT-Sim (PAS) domain is a conserved a/ß fold present within a plethora of signalling proteins from all kingdoms of life. PAS domains are often dimeric and act as versatile sensory and interaction modules to propagate environmental signals to effector domains. The NifL regulatory protein from Azotobacter vinelandii senses the oxygen status of the cell via an FAD cofactor accommodated within the first of two amino-terminal tandem PAS domains, termed PAS1 and PAS2. The redox signal perceived at PAS1 is relayed to PAS2 resulting in conformational reorganization of NifL and consequent inhibition of NifA activity. We have identified mutations in the cofactor-binding cavity of PAS1 that prevent 'release' of the inhibitory signal upon oxidation of FAD. Substitutions of conserved ß-sheet residues on the distal surface of the FAD-binding cavity trap PAS1 in the inhibitory signalling state, irrespective of the redox state of the FAD group. In contrast, substitutions within the flanking A'a-helix that comprises part of the dimerization interface of PAS1 prevent transmission of the inhibitory signal. Taken together, these results suggest an inter-subunit pathway for redox signal transmission from PAS1 that propagates from core to the surface in a conformation-dependent manner requiring a flexible dimer interface

Polymorphisms in the methylenetetrahydrofolate reductase (MTHFR) gene, intakes of folate and related B vitamins and colorectal cancer : a case-control study in a population with relatively low folate intake

Peer reviewedPublisher PD

Lyman-alpha Forest Constraints on the Mass of Warm Dark Matter and the Shape of the Linear Power Spectrum

High resolution N-body simulations of cold dark matter (CDM) models predict that galaxies and clusters have cuspy halos with excessive substructure. Observations reveal smooth halos with central density cores. One possible resolution of this conflict is that the dark matter is warm (WDM); this will suppress the power spectrum on small scales. The Lyman-alpha forest is a powerful probe of the linear power spectrum on these scales. We use collisionless N-body simulations to follow the evolution of structure in WDM models, and analyze artificial Lyman-alpha forest spectra extracted from them. By requiring that there is enough small-scale power in the linear power spectrum to reproduce the observed properties of the Lyman-alpha forest in quasar spectra, we derive a lower limit to the mass of the WDM particle of 750 eV. This limit is robust to reasonable uncertainties in our assumption about the temperature of the mean density gas (T0) at z=3. We argue that any model that suppresses the CDM linear theory power spectrum more severely than a 750 eV WDM particle cannot produce the Lyman-alpha forest.Comment: 13 pages including 4 color Figures and 1 Table, submitted to ApJ Letter

A multistate single-connection calibration for microwave microfluidics

©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.With emerging medical, chemical, and biological applications of microwave-microfluidic devices, many researchers desire a fast and accurate calibration that can be achieved in a single connection. However, traditional on-wafer or coaxial calibrations require measurements of several different artifacts to the data prior to measuring the microwave-microfluidic device. Ideally, a single artifact would be able to present different impedance states to correct the vector network analyzer data, minimizing drift and eliminating artifact-to-artifact connection errors. Here, we developed a multistate single-connection calibration that used a coplanar waveguide loaded with a microfluidic channel. We then used measurements of the uncorrected scattering parameters of the coplanar waveguide with the channel empty, filled with deionized water, and filled with 30 w% (30 grams per liter) of saline to construct an eight-term error model and switch-term correction. After correction, the residuals between measured scattering parameters and with the literaturebased finite-element simulations were below -40 dB from 100 MHz to 110 GHz. This multistate single-connection calibration is compatible with both wafer-probed and connectorized microwave-microfluidic devices for accurate impedance spectroscopy and materials characterization without the need for multiple device measurements.Peer ReviewedPostprint (author's final draft

A bi-dimensional finite mixture model for longitudinal data subject to dropout

In longitudinal studies, subjects may be lost to follow-up, or miss some of the planned visits, leading to incomplete response sequences. When the probability of non-response, conditional on the available covariates and the observed responses, still depends on unobserved outcomes, the dropout mechanism is said to be non ignorable. A common objective is to build a reliable association structure to account for dependence between the longitudinal and the dropout processes. Starting from the existing literature, we introduce a random coefficient based dropout model where the association between outcomes is modeled through discrete latent effects. These effects are outcome-specific and account for heterogeneity in the univariate profiles. Dependence between profiles is introduced by using a bi-dimensional representation for the corresponding distribution. In this way, we define a flexible latent class structure which allows to efficiently describe both dependence within the two margins of interest and dependence between them. By using this representation we show that, unlike standard (unidimensional) finite mixture models, the non ignorable dropout model properly nests its ignorable counterpart. We detail the proposed modeling approach by analyzing data from a longitudinal study on the dynamics of cognitive functioning in the elderly. Further, the effects of assumptions about non ignorability of the dropout process on model parameter estimates are (locally) investigated using the index of (local) sensitivity to non-ignorability

A Bayesian sensitivity model for intention-to-treat analysis on binary outcomes with dropouts

Intention-to-treat (ITT) analysis is commonly used in randomized clinical trials. However, the use of ITT analysis presents a challenge: how to deal with subjects who drop out. Here we focus on randomized trials where the primary outcome is a binary endpoint. Several approaches are available for including the dropout subject in the ITT analysis, mainly chosen prior to unblinding the study. These approaches reduce the potential bias due to breaking the randomization code. However, the validity of the results will highly depend on untestable assumptions about the dropout mechanism. Thus, it is important to evaluate the sensitivity of the results across different missing-data mechanisms. We propose here a Bayesian pattern-mixture model for ITT analysis of binary outcomes with dropouts that applies over different types of missing-data mechanisms. We introduce a new parameterization to identify the model, which is then used for sensitivity analysis. The parameterization is defined as the odds ratio of having an endpoint between the subjects who dropped out and those who completed the study. Such parameterization is intuitive and easy to use in sensitivity analysis; it also incorporates most of the available methods as special cases. The model is applied to TRial Of Preventing HYpertension. Copyright © 2008 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/61532/1/3494_ftp.pd

Towards an Approach for Validating the Internet-of-Transactional-Things

© 2020, Springer Nature Switzerland AG. This paper examines the impact of transactional properties, known as pivot, retriable, and compensatable, on Internet-of-Things (IoT). Despite the ever-growing number of things in today’s cyber-physical world, a limited number of studies examine this impact while considering things’ particularities in terms of reduced size, restricted connectivity, continuous mobility, limited energy, and constrained storage. To address this gap, this paper proceeds first, with exposing things’ duties, namely sensing, actuating, and communicating. Then, it examines the appropriateness of each transactional property for each duty. During the performance of transactional things, (semi)-atomicity criterion is adopted allowing to approve when these things’ duties could be either canceled or compensated. A system that runs a set of what-if experiments is presented in the paper allowing to demonstrate the technical doability of transactional things

Competitive aminal formation during the synthesis of a highly soluble, isopropyl-decorated imine porous organic cage.

The synthesis of a new porous organic cage decorated with isopropyl moieties (CC21) was achieved from the reaction of triformylbenzene and an isopropyl functionalised diamine. Unlike structurally analogous porous organic cages, its synthesis proved challenging due to competitive aminal formation, rationalised using control experiments and computational modelling. The use of an additional amine was found to increase conversion to the desired cage

Missing binary outcomes under covariate-dependent missingness in cluster randomised trials.

Missing outcomes are a commonly occurring problem for cluster randomised trials, which can lead to biased and inefficient inference if ignored or handled inappropriately. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. In this study, we assessed the performance of unadjusted cluster-level analysis, baseline covariate-adjusted cluster-level analysis, random effects logistic regression and generalised estimating equations when binary outcomes are missing under a baseline covariate-dependent missingness mechanism. Missing outcomes were handled using complete records analysis and multilevel multiple imputation. We analytically show that cluster-level analyses for estimating risk ratio using complete records are valid if the true data generating model has log link and the intervention groups have the same missingness mechanism and the same covariate effect in the outcome model. We performed a simulation study considering four different scenarios, depending on whether the missingness mechanisms are the same or different between the intervention groups and whether there is an interaction between intervention group and baseline covariate in the outcome model. On the basis of the simulation study and analytical results, we give guidance on the conditions under which each approach is valid. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd