Evaluating RNAlater® as a preservative for using near-infrared spectroscopy to predict Anopheles gambiae age and species.

Mosquito age and species identification is a crucial determinant of the efficacy of vector control programmes. Near-infrared spectroscopy (NIRS) has previously been applied successfully to rapidly, non-destructively, and simultaneously determine the age and species of freshly anesthetized African malaria vectors from the Anopheles gambiae s.l. species complex: An. gambiae s. s. and Anopheles arabiensis. However, this has only been achieved on freshly-collected specimens and future applications will require samples to be preserved between field collections and scanning by NIRS. In this study, a sample preservation method (RNAlater(®)) was evaluated for mosquito age and species identification by NIRS against scans of fresh samples. Two strains of An. gambiae s.s. (CDC and G3) and two strains of An. arabiensis (Dongola, KGB) were reared in the laboratory while the third strain of An. arabiensis (Ifakara) was reared in a semi-field system. All mosquitoes were scanned when fresh and rescanned after preservation in RNAlater(®) for several weeks. Age and species identification was determined using a cross-validation. The mean accuracy obtained for predicting the age of young (<7 days) or old (≥ 7 days) of all fresh (n = 633) and all preserved (n = 691) mosquito samples using the cross-validation technique was 83% and 90%, respectively. For species identification, accuracies were 82% for fresh against 80% for RNAlater(®) preserved. For both analyses, preserving mosquitoes in RNAlater(®) was associated with a highly significant reduction in the likelihood of a misclassification of mosquitoes as young or old using NIRS. Important to note is that the costs for preserving mosquito specimens with RNAlater(®) ranges from 3-13 cents per insect depending on the size of the tube used and the number of specimens pooled in one tube. RNAlater(®) can be used to preserve mosquitoes for subsequent scanning and analysis by NIRS to determine their age and species with minimal costs and with accuracy similar to that achieved from fresh insects. Cold storage availability allows samples to be stored longer than a week after field collection. Further study to develop robust calibrations applicable to other strains from diverse ecological settings is recommended

A Multi-Wavelength Study of Sgr A*: The Role of Near-IR Flares in Production of X-ray, Soft γ\gamma-ray and Sub-millimeter Emission

(abridged) We describe highlights of the results of two observing campaigns in 2004 to investigate the correlation of flare activity in Sgr A* in different wavelength regimes, using a total of nine ground and space-based telescopes. We report the detection of several new near-IR flares during the campaign based on {\it HST} observations. The level of near-IR flare activity can be as low as $\sim0.15$ mJy at 1.6 $\mu$m and continuous up to about 40% of the total observing time. Using the NICMOS instrument on the {\it HST}, the {\it XMM-Newton} and CSO observatories, we also detect simultaneous bright X-ray and near-IR flare in which we observe for the first time correlated substructures as well as simultaneous submillimeter and near-IR flaring. X-ray emission is arising from the population of near-IR-synchrotron-emitting relativistic particles which scatter submillimeter seed photons within the inner 10 Schwarzschild radii of Sgr A* up to X-ray energies. In addition, using the inverse Compton scattering picture, we explain the high energy 20-120 keV emission from the direction toward Sgr A*, and the lack of one-to-one X-ray counterparts to near-IR flares, by the variation of the magnetic field and the spectral index distributions of this population of nonthermal particles. In this picture, the evidence for the variability of submillimeter emission during a near-IR flare is produced by the low-energy component of the population of particles emitting synchrotron near-IR emission. Based on the measurements of the duration of flares in near-IR and submillimeter wavelengths, we argue that the cooling could be due to adiabatic expansion with the implication that flare activity may drive an outflow.Comment: 48 pages, 12 figures, ApJ (in press

Age grading \u3cem\u3eAn. gambiae\u3c/em\u3e and \u3cem\u3eAn. arabiensis\u3c/em\u3e using near infrared spectra and artificial neural networks

Background Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into \u3c or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. Methods and findings We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. The ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0% for An. gambiae, and 90.2 ± 1.7% for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae and 88.7 ± 1.1% for An. arabiensis. We further tested the reproducibility of these results on different independent mosquito datasets. ANNs scored higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers scored higher accuracies on classifying age of mosquitoes than regression models translated as binary classifiers. Conclusion We recommend training models to estimate age of An. arabiensis and An. gambiae using ANN model architectures (especially for datasets with at least 70 mosquitoes per age group) and direct training of binary classifier instead of training a regression model and interpreting it as a binary classifier

The Fear Reduction Exercised Early (FREE) approach to low back pain: study protocol for a randomised controlled trial

This is the final version of the article. Available from BioMed Central via the DOI in this record.BACKGROUND: Low back pain (LBP) is a major health issue associated with considerable health loss and societal costs. General practitioners (GPs) play an important role in the management of LBP; however, GP care has not been shown to be the most cost-effective approach unless exercise and behavioural counselling are added to usual care. The Fear Reduction Exercised Early (FREE) approach to LBP has been developed to assist GPs to manage LBP by empowering exploration and management of psychosocial barriers to recovery and provision of evidence-based care and information. The aim of the Low Back Pain in General Practice (LBPinGP) trial is to explore whether patients with LBP who receive care from GPs trained in the FREE approach have better outcomes than those who receive usual care. METHODS/DESIGN: This is a cluster randomised controlled superiority trial comparing the FREE approach with usual care for LBP management with investigator-blinded assessment of outcomes. GPs will be recruited and then cluster randomised (in practice groups) to the intervention or control arm. Intervention arm GPs will receive training in the FREE approach, and control arm GPs will continue to practice as usual. Patients presenting to their GP with a primary complaint of LBP will be allocated on the basis of allocation of the GP they consult. We aim to recruit 60 GPs and 275 patients (assuming patients are recruited from 75% of GPs and an average of 5 patients per GP complete the study, accounting for 20% patient participant dropout). Patient participants and the trial statistician will be blind to group allocation throughout the study. Analyses will be undertaken on an intention-to-treat basis. The primary outcome will be back-related functional impairment 6 months post-initial LBP consultation (interim data at 2 weeks, 6 weeks and 3 months), measured with the Roland-Morris Disability Questionnaire. Secondary patient outcomes include pain, satisfaction, quality of life, days off from work and costs of care. Secondary GP outcomes include beliefs about pain and impairment, GP confidence, and actual and reported clinical behaviour. Health economic and process evaluations will be conducted. DISCUSSION: In the LBPinGP trial, we will investigate providing an intervention during the first interaction a person with back pain has with their GP. Because the FREE approach is used within a normal GP consultation, if effective, it may be a cost-effective means of improving LBP care. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12616000888460 . Registered on 6 July 2016.This study is funded and supported by the Accident Compensation Corporation (ACC), Wellington, New Zealand. The study funder has not been involved with study design. The study funder will not be involved with or have ultimate authority over the collection, management, analysis and interpretation of data; the writing of the report; or the decision to submit the report for publication. The funder will have the opportunity to comment on draft reports before publication. SD’s work was supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South West Peninsula at the Royal Devon and Exeter NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health

Extracting the time-dependent transmission rate from infection data via solution of an inverse ODE problem

The transmission rate of many acute infectious diseases varies significantly in time, but the underlying mechanisms are usually uncertain. They may include seasonal changes in the environment, contact rate, immune system response, etc. The transmission rate has been thought difficult to measure directly. We present a new algorithm to compute the time-dependent transmission rate directly from prevalence data, which makes no assumptions about the number of susceptible or vital rates. The algorithm follows our complete and explicit solution of a mathematical inverse problem for SIR-type transmission models. We prove that almost any infection profile can be perfectly fitted by an SIR model with variable transmission rate. This clearly shows a serious danger of overfitting such transmission models. We illustrate the algorithm with historic UK measles data and our observations support the common belief that measles transmission was predominantly driven by school contacts