Quark-gluon plasma phenomenology from anisotropic lattice QCD

The FASTSUM collaboration has been carrying out simulations of N_f=2+1 QCD at nonzero temperature in the fixed-scale approach using anisotropic lattices. Here we present the status of these studies, including recent results for electrical conductivity and charge diffusion, and heavy quarkonium (charm and beauty) physics.Comment: Talk given at Quark Confinement and the Hadron Spectrum (Confinement XI), 8-12 September, St. Petersburg, Russia. 8 pages, 7 figure

Can stimulus enhancement explain the apparent success of the model-rival technique in the domestic dog (Canis familiaris)?

The model-rival technique is a method of training whereby an animal learns the distinguishing features of a target object, such as name and colour, by observing a trainer and a potential competitor engage in conversation about these features. In this study the apparent effectiveness of the model-rival technique in training dogs to perform a selection-retrieval task by McKinley and Young McKinley, S., Young, R.J., 2003. The efficacy of the model-rival method when compared with operant conditioning for training domestic dogs to perform a retrieval-selection task. Appl. Anim. Behav. Sci. 81, 357-365 was investigated to evaluate the hypothesis that simpler forms of learning may be responsible for the results. This was tested by repeating McKinley and Young's model-rival training method and comparing the results to those of training sessions devised to include different forms of stimulus enhancement of the object to be retrieved. These training sessions involved: minimal enhancement, during which the experimenters made no interactions with the target object; indirect stimulus enhancement, during which both experimenters switched their gaze between the dog and the target object; or direct stimulus enhancement, during which one of the experimenters held the target object. It was found that only the model-rival and direct enhancement methods resulted in a significant number of dogs successfully completing the selection-retrieval test. There was also evidence to suggest that with the direct stimulus enhancement training method dogs learned quicker than with the model-rival training method. It was concluded that dogs are able to learn to retrieve a named object in a selection-retrieval task as a result of simple stimulus enhancement, without necessarily understanding the complex cognitive processes which underpin learning in the model-rival process. c 2008 Elsevier B.V. All rights reserved

Birth weight is associated with brain tissue volumes seven decades later but not with MRI markers of brain ageing

Birth weight, an indicator of fetal growth, is associated with cognitive outcomes in early life (which are predictive of cognitive ability in later life) and risk of metabolic and cardiovascular disease across the life course. Brain health in older age, indexed by MRI features, is associated with cognitive performance, but little is known about how variation in normal birth weight impacts on brain structure in later life. In a community dwelling cohort of participants in their early seventies we tested the hypothesis that birth weight is associated with the following MRI features: total brain (TB), grey matter (GM) and normal appearing white matter (NAWM) volumes; whiter matter hyperintensity (WMH) volume; a general factor of fractional anisotropy (gFA) and peak width skeletonised mean diffusivity (PSMD) across the white matter skeleton. We also investigated the associations of birth weight with cortical surface area, volume and thickness. Birth weight was positively associated with TB, GM and NAWM volumes in later life (β ≥ 0.194), and with regional cortical surface area but not gFA, PSMD, WMH volume, or cortical volume or thickness. These positive relationships appear to be explained by larger intracranial volume, rather than by age-related tissue atrophy, and are independent of body height and weight in adulthood. This suggests that larger birth weight is linked to more brain tissue reserve in older life, rather than age-related brain structural features, such as tissue atrophy or WMH volume

Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis

Background: The propensity of diferent Anopheles mosquitoes to bite humans instead of other vertebrates infuences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Human Blood Index (HBI), currently requires expensive and time-consuming laboratory procedures involving enzyme-linked immunosorbent assays (ELISA) or polymerase chain reactions (PCR). Here, midinfrared (MIR) spectroscopy and supervised machine learning are used to accurately distinguish between vertebrate blood meals in guts of malaria mosquitoes, without any molecular techniques. Methods: Laboratory-reared Anopheles arabiensis females were fed on humans, chickens, goats or bovines, then held for 6 to 8 h, after which they were killed and preserved in silica. The sample size was 2000 mosquitoes (500 per host species). Five individuals of each host species were enrolled to ensure genotype variability, and 100 mosquitoes fed on each. Dried mosquito abdomens were individually scanned using attenuated total refection-Fourier transform infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra (4000 cm−1 to 400 cm−1 ). The spectral data were cleaned to compensate atmospheric water and CO2 interference bands using Bruker-OPUS software, then transferred to Python™ for supervised machine-learning to predict host species. Seven classifcation algorithms were trained using 90% of the spectra through several combinations of 75–25% data splits. The best performing model was used to predict identities of the remaining 10% validation spectra, which had not been used for model training or testing. Results: The logistic regression (LR) model achieved the highest accuracy, correctly predicting true vertebrate blood meal sources with overall accuracy of 98.4%. The model correctly identifed 96% goat blood meals, 97% of bovine blood meals, 100% of chicken blood meals and 100% of human blood meals. Three percent of bovine blood meals were misclassifed as goat, and 2% of goat blood meals misclassifed as human. Conclusion: Mid-infrared spectroscopy coupled with supervised machine learning can accurately identify multiple vertebrate blood meals in malaria vectors, thus potentially enabling rapid assessment of mosquito blood-feeding histories and vectorial capacities. The technique is cost-efective, fast, simple, and requires no reagents other than desiccants. However, scaling it up will require field validation of the findings and boosting relevant technical capacity in affected countries

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis. mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with other mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets

An online intervention for improving stroke survivors' health-related quality of life : study protocol for a randomised controlled trial

Background: Recurrent stroke is a major contributor to stroke-related disability and costs. Improving health-risk behaviours and mental health has the potential to significantly improve recovery, enhance health-related quality of life (HRQoL), independent living, and lower the risk of recurrent stroke. The primary aim will be to test the effectiveness of an online intervention to improve HRQoL among stroke survivors at 6 months' follow-up. Programme effectiveness on four health behaviours, anxiety and depression, cost-effectiveness, and impact on other hospital admissions will also be assessed. Methods/design: An open-label randomised controlled trial is planned. A total of 530 adults will be recruited across one national and one regional stroke registry and block randomised to the intervention or minimal care control group. The intervention group will receive access to the online programme Prevent 2nd Stroke (P2S); the minimal care control group will receive an email with Internet addresses of generic health sites designed for the general population. The primary outcome, HRQoL, will be measured using the EuroQol-5D. A full analysis plan will compare between groups from baseline to follow-up. Discussion: A low-cost per user option to supplement current care, such as P2S, has the potential to increase HRQoL for stroke survivors, and reduce the risk of second stroke

Tamoxifen for prevention of breast cancer: extended long-term follow-up of the IBIS-I breast cancer prevention trial

© Cuzick et al. Open Access article distributed under the terms of CC BY.http://dx.doi.org/10.1016/S1470-2045(14)71171-

Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis

Background: Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. Methods: Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm−1 to 500 cm−1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. Results: Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. Conclusion: These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance

Clinical sub-phenotypes of Staphylococcus aureus bacteraemia

Background: Staphylococcus aureus bacteraemia (SAB) is a clinically heterogeneous disease. The ability to identify sub-groups of patients with shared traits (sub-phenotypes) is an unmet need that could allow patient stratification for clinical management and research. We aimed to test the hypothesis that clinically-relevant sub-phenotypes can be reproducibly identified amongst patients with SAB. Methods: We studied three cohorts of hospitalised adults with monomicrobial SAB: a UK retrospective observational study (Edinburgh cohort, n=458), the UK ARREST randomised trial (n=758), and the Spanish SAFO randomised trial (n=214). Latent class analysis was used to identify sub-phenotypes using routinely-collected clinical data, without considering outcomes. Mortality and microbiologic outcomes were then compared between sub-phenotypes. Results: Included patients had predominantly methicillin-susceptible SAB (1366/1430,95.5%). We identified five distinct, reproducible clinical sub-phenotypes: (A) SAB associated with older age and comorbidity, (B) nosocomial intravenous catheter-associated SAB in younger people without comorbidity, (C) community-acquired metastatic SAB, (D) SAB associated with chronic kidney disease, and (E) SAB associated with injection drug use. Survival and microbiologic outcomes differed between the sub-phenotypes. 84-day mortality was highest in sub-phenotype A, and lowest in B and E. Microbiologic outcomes were worse in sub-phenotype C. In a secondary analysis of the ARREST trial, adjunctive rifampicin was associated with increased 84-day mortality in sub-phenotype B and improved microbiologic outcomes in sub-phenotype C. Conclusions: We have identified reproducible and clinically-relevant sub-phenotypes within SAB, and provide proof-of-principle of differential treatment effects. Through clinical trial enrichment and patient stratification, these sub-phenotypes could contribute to a personalised medicine approach to SAB