The microbiota is vital for the development of the immune system and homeostasis. Changes in microbial composition and function, termed dysbiosis, in the respiratory tract and the gut have recently ...been linked to alterations in immune responses and to disease development in the lungs. In this Opinion article, we review the microbial species that are usually found in healthy gastrointestinal and respiratory tracts, their dysbiosis in disease and interactions with the gut-lung axis. Although the gut-lung axis is only beginning to be understood, emerging evidence indicates that there is potential for manipulation of the gut microbiota in the treatment of lung diseases.
Hyperpolarisation of nuclear spins is important in overcoming sensitivity and resolution limitations of magnetic resonance imaging and nuclear magnetic resonance spectroscopy. Current ...hyperpolarisation techniques require high magnetic fields, low temperatures, or catalysts. Alternatively, the emergence of room temperature spin qubits has opened new pathways to achieve direct nuclear spin hyperpolarisation. Employing a microwave-free cross-relaxation induced polarisation protocol applied to a nitrogen vacancy qubit, we demonstrate quantum probe hyperpolarisation of external molecular nuclear spins to ~50% under ambient conditions, showing a single qubit increasing the polarisation of ~10
nuclear spins by six orders of magnitude over the thermal background. Results are verified against a detailed theoretical treatment, which also describes how the system can be scaled up to a universal quantum hyperpolarisation platform for macroscopic samples. Our results demonstrate the prospects for this approach to nuclear spin hyperpolarisation for molecular imaging and spectroscopy and its potential to extend beyond into other scientific areas.
Ischaemic heart disease has a multifactorial aetiology and can be prevented from developing in populations primordially, and in individuals at high risk by primary prevention. The primordial approach ...focuses on social determinants of health in populations: political, economic, and social factors, principally unplanned urbanisation, illiteracy, poverty, and working and living conditions. Implementation of the UN Sustainable Development Goals can lead to major improvements in cardiovascular health, and adequate health-care financing and universal health care are important for achieving these goals. Population-level interventions should focus on tobacco control, promotion of healthy foods (fruits, vegetables, legumes, and nuts), curbing unhealthy foods (saturated fats, trans fats, refined carbohydrates, excessive salt, and alcohol), promotion of physical activity in everyday living, and control of ambient and indoor pollution. At the individual level, identification of people at high multifactorial risk and guideline-driven management of hypertension, LDL cholesterol, and diabetes is required. Strategies to improve adherence to healthy lifestyles and drug therapies are essential and can be implemented at health system, health care, and patient levels with use of education, technology, and personalised approaches. Improving quality of medical education with a focus on ischaemic heart disease prevention for physicians, nurses, allied health workers, and the public is required.
Chronic obstructive pulmonary disease (COPD) is the third commonest cause of death globally, and manifests as a progressive inflammatory lung disease with no curative treatment. The lung microbiome ...contributes to COPD progression, but the function of the gut microbiome remains unclear. Here we examine the faecal microbiome and metabolome of COPD patients and healthy controls, finding 146 bacterial species differing between the two groups. Several species, including Streptococcus sp000187445, Streptococcus vestibularis and multiple members of the family Lachnospiraceae, also correlate with reduced lung function. Untargeted metabolomics identifies a COPD signature comprising 46% lipid, 20% xenobiotic and 20% amino acid related metabolites. Furthermore, we describe a disease-associated network connecting Streptococcus parasanguinis_B with COPD-associated metabolites, including N-acetylglutamate and its analogue N-carbamoylglutamate. While correlative, our results suggest that the faecal microbiome and metabolome of COPD patients are distinct from those of healthy individuals, and may thus aid in the search for biomarkers for COPD.
Recorded particulate matter (PM2.5) hourly trends are compared for fifteen urban recording sites distributed across central England for the period 2018 to 2022. They include 10 urban-background and ...five urban-traffic (roadside) sites with some located within the same urban area. The sites all show consistent background and peak distributions with mean annual values and standard deviations higher for 2018 and 2019 than for 2020 to 2022. The objective of this study is to demonstrate that trend attributes extracted from hourly recorded univariate PM2.5 trends at these sites can be used to provide reliable short-term hourly predictions and provide valuable insight into the regional variations in the recorded trends. Fifteen trend attributes extracted from the prior 12 h (t-1 to t-12) of recorded PM2.5 data were compiled and used as input to four supervised machine learning models (SML) to forecast PM2.5 concentrations up to 13 h ahead (t0 to t+12). All recording sites delivered forecasts with similar ranges of error levels for specific hours ahead which are consistent with their PM2.5 recorded ranges. Forecasting results for four representative sites are presented in detail using models trained and cross-validated with 2020 and 2021 hourly data to forecast 2021 and 2022 hourly data, respectively. A novel optimized feature selection procedure using a suite of five optimizers is used to improve the efficiency of the forecasting models. The LASSO and support vector regression models generate the best and most generalizable hourly PM2.5 forecasts from trained and validated SML models with mean average error (MAE) of between ∼1 and ∼3 μg/m3 for t0 to t+3 h ahead. A novel overfitting indicator, exploiting the cross-validation mean values, demonstrates that these two models are not affected by overfitting. Forecasts for t+6 to t+12 h forward generate higher MAE values between ∼3 and ∼4 μg/m3 due to their tendency to underestimate some of the extreme PM2.5 peaks. These findings indicate that further model refinements are required to generate more reliable short-term predictions for the t+6 to t+24 h ahead.
•Hourly PM2.5 trends for 2018–2022 compared for 15 Central England sites.•15 trend attributes extracted from hours t-1 to t-12 past to forecast t0 to t+12.•5 optimizers applied to identify the most efficient input feature selections.•LASSO and support vector regression shown to be the most generalizable models.•Feature selection/importance shown to vary depending on the hours ahead forecast.
Western lifestyles differ markedly from those of our hunter-gatherer ancestors, and these differences in diet and activity level are often implicated in the global obesity pandemic. However, few ...physiological data for hunter-gatherer populations are available to test these models of obesity. In this study, we used the doubly-labeled water method to measure total daily energy expenditure (kCal/day) in Hadza hunter-gatherers to test whether foragers expend more energy each day than their Western counterparts. As expected, physical activity level, PAL, was greater among Hadza foragers than among Westerners. Nonetheless, average daily energy expenditure of traditional Hadza foragers was no different than that of Westerners after controlling for body size. The metabolic cost of walking (kcal kg(-1) m(-1)) and resting (kcal kg(-1) s(-1)) were also similar among Hadza and Western groups. The similarity in metabolic rates across a broad range of cultures challenges current models of obesity suggesting that Western lifestyles lead to decreased energy expenditure. We hypothesize that human daily energy expenditure may be an evolved physiological trait largely independent of cultural differences.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Near-past wind capacity factor trend attributes are useful for short-term forecasts.•Novel transparent method extracts trend attributes from wind capacity factor data.•14 machine/deep learning ...(ML/DL) models applied to forecast up to 12 h ahead.•ELM, SVR and XGBoost provide better forecasts than DL and ARIMA models.•ML/DL models substantially outperform ARIMA in forecasting accuracy.
A straightforward decomposition technique applied to hourly wind capacity factor time-series data compiled at the country level can enhance short-term forecasting over the coming twelve hours. The method overcomes the limitations of relying upon limited exogeneous input variables and incorporates near-term impacts of system curtailments and market irregularities, potentially aiding system operators and regulators in optimizing ongoing supply–demand balances. The method establishes multiple attributes from the previous twelve hours of wind capacity data. It then uses those attributes as input variables for forecasting with multiple machine- and deep-learning methods. The method is evaluated using 43,824 hourly data records for combined onshore and offshore wind capacity for Britain from 2015 to 2019. Fourteen machine- and deep-learning models are trained and validated with 2015–2018 data and the trained models are applied to forecast the 8760 hourly records for 2019. 10-fold cross validation is used to verify the robustness of the validation procedure. Forecasting performance is benchmarked against autoregressive integrated moving average and multi-linear regression models. The best-performing models (extreme learning machine, support vector regression and extreme gradient boosting) outperform both autoregressive integrated moving average and multi-linear regression methods in forecasting t0 to t + 12 h ahead. Machine learning models substantially outperform the five deep learning models evaluated in forecasting accuracy and execution time. The transparency of the multi-linear regression model provides useful insight to the relative contributions of each input variable to the forecasts, highlighting that the trend attribute impacts vary significantly across the t0 to t + 12 time periods forecast.
Surgical aortic valve replacement (SAVR) has long been the mainstay of therapy for severe aortic stenosis. However, transcatheter aortic valve replacement (TAVR) is now generally accepted as the new ...standard of care for patients with symptomatic aortic stenosis who are not candidates for open surgery. Arguably TAVR may also be a preferred alternative to SAVR in carefully selected high-risk, but still operable, patients in whom morbidity and mortality may be reduced. Although TAVR outcomes continue to improve, concerns remain with respect to vascular injury, stroke, paravalvular regurgitation, and valve durability. However, it seems likely that with ongoing refinement of transcatheter valve systems, techniques, and patient selection TAVR is becoming an increasingly appealing option for a much broader range of patients. Randomized trials and ongoing surveillance will play an important role as we enter a new era of rigorous clinical evaluation for minimally invasive therapies for structural heart disease.
Scenario development approaches are designed to deal with chaotic behaviors of complex systems and are widely used in the case of energy-related demand forecasting and policy planning. Building on ...traditional qualitative scenario models, a novel Learning Scenario Development Model (LSDM), incorporating qualitative and quantitative components, is proposed to generate different scenarios for global natural gas demand to 2025 in order to discover and compare the likely behavior of alternative future natural gas markets. This model, consists of five phases: 1) organize the fundamental data set, 2) investigate a data mining based pre-process procedure to initialize the quantitative dimension of the model, 3) select a set of procedures for forecasting global natural gas demand to 2025, referred to as the mixed model, 4) generate a reference case scenario (business as usual) using the mixed model, and 5) develop alternative scenarios (five in this study) applying a qualitative expert-based process. Unlike other scenario models, the LSDM is equipped with validation procedures that enable decision makers to develop alternative scenarios based on various input strategies to evaluate and simulate them. For the application of global natural gas demand, results suggest a gentle uptrend for the reference case (about 4232 bcm in 2025). The alternative scenarios considered support a continued increase for the global natural gas demand, but at different rates depending on the removal or addition of multiple natural gas suppliers (from 2013 to 2025, the scenarios considered display demand growth varying from 23.5% to 25%).
•A reliable long-run energy demand forecasting model is proposed.•A novel quantitative-qualitative learning scenario method has been presented.•A set of environmental input features were investigated to address sustainability concerns.•Findings revealed that in mid-term future natural gas consumption will remain interesting.•Evaluations showed that the global market is inclined towards more consumption scenarios.
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