Duration of shedding of respiratory syncytial virus in a community study of Kenyan children

Background: Our understanding of the transmission dynamics of respiratory syncytial virus (RSV) infection will be better informed with improved data on the patterns of shedding in cases not limited only to hospital admissions. Methods: In a household study, children testing RSV positive by direct immunofluorescent antibody test (DFA) were enrolled. Nasal washings were scheduled right away, then every three days until day 14, every 7 days until day 28 and every 2 weeks until a maximum of 16 weeks, or until the first DFA negative RSV specimen. The relationship between host factors, illness severity and viral shedding was investigated using Cox regression methods. Results: From 151 families a total of 193 children were enrolled with a median age of 21 months (range 1-164 months), 10% infants and 46% male. The rate of recovery from infection was 0.22/person/day (95% CI 0.19-0.25) equivalent to a mean duration of shedding of 4.5 days (95%CI 4.0-5.3), with a median duration of shedding of 4 days (IQR 2-6, range 1-14). Children with a history of RSV infection had a 40% increased rate of recovery i.e. shorter duration of viral shedding (hazard ratio 1.4, 95% CI 1.01-1.86). The rate of cessation of shedding did not differ significantly between males and females, by severity of infection or by age. Conclusion: We provide evidence of a relationship between the duration of shedding and history of infection, which may have a bearing on the relative role of primary versus re-infections in RSV transmission in the community

Whole animal experiments should be more like human randomized controlled trials

Beverly S. Muhlhausler, Frank H. Bloomfield, Matthew W. Gillma

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.FCT under the Neuroclinomics2 project [PTDC/EEI-SII/1937/2014, SFRH/BD/95846/2013]; INESC-ID plurianual [UID/CEC/50021/2013]; LASIGE Research Unit [UID/CEC/00408/2013

Using fractional exhaled nitric oxide (FeNO) to diagnose steroid-responsive disease and guide asthma management in routine care

Acknowledgements We thank Robin Taylor for his informative thinking and publications on FeNO, which have helped to influence and direct the thinking of the authors. Funding Extraction of the real-life dataset was funded by Research in Real Life Limited, the analysis of the dataset and the writing of this manuscript were co-funded (50:50) by Research in Real Life Limited and Aerocrine.Peer reviewedPublisher PD

Structural Characteristics and Stellar Composition of Low Surface Brightness Disk Galaxies

We present UBVI surface photometry of a sample of low surface brightness (LSB) disk galaxies. LSB disk galaxies are fairly well described as exponential disks with no preferred value for either scale length, central surface brightness, or rotational velocity. Indeed, the distribution of scale lengths is indistinguishable from that of high surface brightness spirals, indicating that dynamically similar galaxies (e.g., those with comparable Rv^2) exist over a large range in surface density. These LSB galaxies are strikingly blue. The complete lack of correlation between central surface brightness and color rules out any fading scenario. Similarly, the oxygen abundances inferred from HII region spectra are uncorrelated with color so the low metallicities are not the primary cause of the blue colors. While these are difficult to interpret in the absence of significant star formation, the most plausible scenario is a stellar population with a young mean age stemming from late formation and subsequent slow evolution. These properties suggest that LSB disks formed from low initial overdensities with correspondingly late collapse times.Comment: Astronomical Journal, in press 45 pages uuencoded postscript (368K) including 9 multipart figures also available by anonymous ftp @ ftp.ast.cam.ac.uk /pub/ssm/phot.uu CAP-30-210442962983742937

Tuning ultrafast electron thermalization pathways in a van der Waals heterostructure

Ultrafast electron thermalization - the process leading to Auger recombination, carrier multiplication via impact ionization and hot carrier luminescence - occurs when optically excited electrons in a material undergo rapid electron-electron scattering to redistribute excess energy and reach electronic thermal equilibrium. Due to extremely short time and length scales, the measurement and manipulation of electron thermalization in nanoscale devices remains challenging even with the most advanced ultrafast laser techniques. Here, we overcome this challenge by leveraging the atomic thinness of two-dimensional van der Waals (vdW) materials in order to introduce a highly tunable electron transfer pathway that directly competes with electron thermalization. We realize this scheme in a graphene-boron nitride-graphene (G-BN-G) vdW heterostructure, through which optically excited carriers are transported from one graphene layer to the other. By applying an interlayer bias voltage or varying the excitation photon energy, interlayer carrier transport can be controlled to occur faster or slower than the intralayer scattering events, thus effectively tuning the electron thermalization pathways in graphene. Our findings, which demonstrate a novel means to probe and directly modulate electron energy transport in nanoscale materials, represent an important step toward designing and implementing novel optoelectronic and energy-harvesting devices with tailored microscopic properties.Comment: Accepted to Nature Physic

Inter-rater reliability of categorical versus continuous scoring of fish vitality: does it affect the utility of the reflex action mortality predictor (RAMP) approach?

Scoring reflex responsiveness and injury of aquatic organisms has gained popularity as predictors of discard survival. Given this method relies upon the individual interpretation of scoring criteria, an evaluation of its robustness is done here to test whether protocol-instructed, multiple raters with diverse backgrounds (research scientist, technician, and student) are able to produce similar or the same reflex and injury score for one of the same flatfish (European plaice, Pleuronectes platessa) after experiencing commercial fishing stressors. Inter-rater reliability for three raters was assessed by using a 3-point categorical scale (‘absent’, ‘weak’, ‘strong’) and a tagged visual analogue continuous scale (tVAS, a 10 cm bar split in three labelled sections: 0 for ‘absent’, ‘weak’, ‘moderate’, and ‘strong’) for six reflex responses, and a 4-point scale for four injury types. Plaice (n = 304) were sampled from 17 research beam-trawl deployments during four trips. Fleiss kappa (categorical scores) and intra-class correlation coefficients (ICC, continuous scores) indicated variable inter-rater agreement by reflex type (ranging between 0.55 and 0.88, and 67% and 91% for Fleiss kappa and ICC, respectively), with least agreement among raters on extent of injury (Fleiss kappa between 0.08 and 0.27). Despite differences among raters, which did not significantly influence the relationship between impairment and predicted survival, combining categorical reflex and injury scores always produced a close relationship of such vitality indices and observed delayed mortality. The use of the continuous scale did not improve fit of these models compared with using the reflex impairment index based on categorical scores. Given these findings, we recommend using a 3-point categorical over a continuous scale. We also determined that training rather than experience of raters minimised inter-rater differences. Our results suggest that cost-efficient reflex impairment and injury scoring may be considered a robust technique to evaluate lethal stress and damage of this flatfish species on-board commercial beam-trawl vessels

A Statistically Rigorous Method for Determining Antigenic Switching Networks

Many vector-borne pathogens rely on antigenic variation to prolong infections and increase their likelihood of onward transmission. This immune evasion strategy often involves mutually exclusive switching between members of gene families that encode functionally similar but antigenically different variants during the course of a single infection. Studies of different pathogens have suggested that switching between variant genes is non-random and that genes have intrinsic probabilities of being activated or silenced. These factors could create a hierarchy of gene expression with important implications for both infection dynamics and the acquisition of protective immunity. Inferring complete switching networks from gene transcription data is problematic, however, because of the high dimensionality of the system and uncertainty in the data. Here we present a statistically rigorous method for analysing temporal gene transcription data to reconstruct an underlying switching network. Using artificially generated transcription profiles together with in vitro var gene transcript data from two Plasmodium falciparum laboratory strains, we show that instead of relying on data from long-term parasite cultures, accuracy can be greatly improved by using transcription time courses of several parasite populations from the same isolate, each starting with different variant distributions. The method further provides explicit indications about the reliability of the resulting networks and can thus be used to test competing hypotheses with regards to the underlying switching pathways. Our results demonstrate that antigenic switch pathways can be determined reliably from short gene transcription profiles assessing multiple time points, even when subject to moderate levels of experimental error. This should yield important new information about switching patterns in antigenically variable organisms and might help to shed light on the molecular basis of antigenic variation

Aurora kinase A drives the evolution of resistance to third-generation EGFR inhibitors in lung cancer.

Although targeted therapies often elicit profound initial patient responses, these effects are transient due to residual disease leading to acquired resistance. How tumors transition between drug responsiveness, tolerance and resistance, especially in the absence of preexisting subclones, remains unclear. In epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma cells, we demonstrate that residual disease and acquired resistance in response to EGFR inhibitors requires Aurora kinase A (AURKA) activity. Nongenetic resistance through the activation of AURKA by its coactivator TPX2 emerges in response to chronic EGFR inhibition where it mitigates drug-induced apoptosis. Aurora kinase inhibitors suppress this adaptive survival program, increasing the magnitude and duration of EGFR inhibitor response in preclinical models. Treatment-induced activation of AURKA is associated with resistance to EGFR inhibitors in vitro, in vivo and in most individuals with EGFR-mutant lung adenocarcinoma. These findings delineate a molecular path whereby drug resistance emerges from drug-tolerant cells and unveils a synthetic lethal strategy for enhancing responses to EGFR inhibitors by suppressing AURKA-driven residual disease and acquired resistance