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Conventional coronary artery disease risk factors might potentially explain at least 90% of the attributable risk of coronary artery disease. To better understand the association between the pre-industrial lifestyle and low prevalence of coronary artery disease risk factors, we examined the Tsimane, a Bolivian population living a subsistence lifestyle of hunting, gathering, fishing, and farming with few cardiovascular risk factors, but high infectious inflammatory burden. We did a cross-sectional cohort study including all individuals who self-identified as Tsimane and who were aged 40 years or older. Coronary atherosclerosis was assessed by coronary artery calcium (CAC) scoring done with non-contrast CT in Tsimane adults. We assessed the difference between the Tsimane and 6814 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). CAC scores higher than 100 were considered representative of significant atherosclerotic disease. Tsimane blood lipid and inflammatory biomarkers were obtained at the time of scanning, and in some patients, longitudinally. Between July 2, 2014, and Sept 10, 2015, 705 individuals, who had data available for analysis, were included in this study. 596 (85%) of 705 Tsimane had no CAC, 89 (13%) had CAC scores of 1–100, and 20 (3%) had CAC scores higher than 100. For individuals older than age 75 years, 31 (65%) Tsimane presented with a CAC score of 0, and only four (8%) had CAC scores of 100 or more, a five-fold lower prevalence than industrialised populations (p≤0·0001 for all age categories of MESA). Mean LDL and HDL cholesterol concentrations were 2·35 mmol/L (91 mg/dL) and 1·0 mmol/L (39·5 mg/dL), respectively; obesity, hypertension, high blood sugar, and regular cigarette smoking were rare. High-sensitivity C-reactive protein was elevated beyond the clinical cutoff of 3·0 mg/dL in 360 (51%) Tsimane participants. Despite a high infectious inflammatory burden, the Tsimane, a forager-horticulturalist population of the Bolivian Amazon with few coronary artery disease risk factors, have the lowest reported levels of coronary artery disease of any population recorded to date. These findings suggest that coronary atherosclerosis can be avoided in most people by achieving a lifetime with very low LDL, low blood pressure, low glucose, normal body-mass index, no smoking, and plenty of physical activity. The relative contributions of each are still to be determined. National Institute on Aging, National Institutes of Health; St Luke's Hospital of Kansas City; and Paleocardiology Foundation.

Dynamic chest radiography (DCR) is a real-time sequential high-resolution digital X-ray imaging system of the thorax in motion over the respiratory cycle, utilising pulsed image exposure and a larger field of view than fluoroscopy coupled with a low radiation dose, where post-acquisition image processing by computer algorithm automatically characterises the motion of thoracic structures. We conducted a systematic review of the literature and found 29 relevant publications describing its use in humans including the assessment of diaphragm and chest wall motion, measurement of pulmonary ventilation and perfusion, and the assessment of airway narrowing. Work is ongoing in several other areas including assessment of diaphragmatic paralysis. We assess the findings, methodology and limitations of DCR, and we discuss the current and future roles of this promising medical imaging technology.Critical relevance statement Dynamic chest radiography provides a wealth of clinical information, but further research is required to identify its clinical niche.Key pointsDynamic chest radiography (DCR) captures high-resolution moving images of the thorax.The ionising radiation dose of DCR is low.DCR can image the diaphragm, chest wall, ventilation and perfusion.Most papers on DCR are small, with heterogeneity in study design or outcome.Large, multicentre studies with similar outcomes and healthy controls are desirable.

The Regenerative Medicine Manufacturing Society (RMMS) is the first and only professional society dedicated toward advancing manufacturing solutions for the field of regenerative medicine. RMMS's vision is to provide greater patient access to regenerative medicine therapies through innovative manufacturing solutions. Our mission is to identify unmet needs and gaps in regenerative medicine manufacturing and catalyze the generation of new ideas and solutions by working with private and public stakeholders. We aim to accomplish our mission through outreach and education programs and securing grants for public‐private collaborations in regenerative medicine manufacturing. This perspective will cover four impact areas that the society's leadership team has identified as critical: (a) cell manufacturing and scale‐up/out, respectively, for allogeneic and autologous cell therapies, (b) standards for regenerative medicine, (c) 3D bioprinting, and (d) artificial intelligence‐enabled automation. In addition to covering these areas and ways in which the society intends to advance the field in a collaborative nature, we will also discuss education and training. Education and training is an area that is critical for communicating the current challenges, developing solutions to accelerate the commercialization of the latest technological advances, and growing the workforce in the rapidly expanding sector of regenerative medicine. Depicted are key areas of focus for the Regenerative Medicine Manufacturing Society. Advancing each of these areas will assist in moving the needle forward to improve patient outcomes with regenerative medicine.

IntroductionDynamic chest radiography (DCR) uses novel, low-dose radiographic technology to capture images of the thoracic cavity while in motion. Pulmonary function testing is important in cystic fibrosis (CF). The tolerability, rapid acquisition and lower radiation and cost compared with CT imaging may make DCR a useful adjunct to current standards of care.Methods and analysisThis is an observational, non-controlled, non-randomised, single-centre, prospective study. This study is conducted at the Liverpool Heart and Chest Hospital (LHCH) adult CF unit. Participants are adults with CF. This study reviews DCR taken during routine CF Annual Review (n=150), validates DCR-derived lung volumes against whole body plethysmography (n=20) and examines DCR at the start and end of pulmonary exacerbations of CF (n=20). The primary objectives of this study are to examine if DCR provides lung function information that correlates with PFT, and lung volumes that correlate whole body plethysmography.Ethics and disseminationThis study has received the following approvals: HRA REC (11 December 2019) and LHCH R&I (11 October 2019). Results are made available to people with CF, the funders and other researchers. Processed, anonymised data are available from the research team on request.Trial registration numberISRCTN 64994816.

Objectives: To assess subclinical atherosclerotic cardiovascular disease (ASCVD) using B-mode ultrasound, with special emphasis on the incremental value of performing imaging in multiple peripheral arteries, and to compare imaging findings with traditiol risk factors for medical intervention eligibility.Methods: Data from 2 asymptomatic cohorts from India with unknown ASCVD risk factors were compared to 2 cohorts from North America with known ASCVD risk factors. Carotid and iliofemoral arteries of the Indian cohorts were examined with automated ultrasound in a high-pace environment by non-experts. A simplified metric of atherosclerotic disease burden (FUster-rula or FUN Score) was developed from 3D imaging data by summing intima-media volume (IMV) over 5-cm arterial segments. Effectiveness of ASCVD prevention guidelines to direct therapy was compared to results from direct imaging.Results: Of the 941 (mean age 44.27 ± 13.76 years, 34% female) enrollees from India, 224 (24%) demonstrated plaques in at least 1 of the 4 arterial sites examined; 107 (11%) had plaques in only the carotids, 70 (7%) in both the carotids and iliofemoral arteries, and 47 (5%) had plaques in only the iliofemoral arteries. Older age and male sex were associated with the presence of plaque, but association with systolic blood pressure was not observed.Data from 2 North American clinics (n = 481, mean age 59.68 ± 11.95 years, 39% female) showed that 203 subjects (42%) had carotid plaque; 82% of whom would not have qualified for lipid-lowering therapy under the Adult Treatment Panel (ATP) III Guidelines. Using the recently published ATP IV Guidelines, 33% of the individuals with carotid plaque would also have failed to qualify for treatment.Conclusions: B-mode ultrasound examition of bilateral iliofemoral arteries provided an incremental yield in identifying subclinical atherosclerotic disease compared to carotid evaluation alone. Ultrasound examition allowed improved identification of individuals who could be targeted for prophylactic medical intervention compared to ATP III and ATP IV Guidelines.Highlights* B-mode ultrasound was used for rapid screening of asymptomatic adults to assess atherosclerotic cardiovascular disease risk.* Adding examition of the iliofemoral arteries improved yield.* Intima-media volume summed into a new quantification index (FUster-rula or FUN score).* Intervention eligibility based on American College of Cardiology/American Heart Association guidelines was compared with imaging results.

Understanding the link between farmers’ information needs and crop yield is vital for crafting effective, sustainable agricultural policies. However, existing research has yet to comprehensively investigate the impact of farmers’ information demand on crop yield using advanced analytical tools. In this direction, the presented study introduces the AgriFact framework to explore the relationship between Indian farmers’ information inquiries and crop yield using Deep Learning (DL)-based modelling and numerical methods-based variables’ relationship analysis. The study examines 1.8 million farmer query calls collected over a decade from Kisan Call Centers, alongside district-wise wheat yield data across India. In the first phase, six DL models are developed and compared to estimate crop productivity based on topic-wise query calls per hectare. From the experiments, it is noted that the 1-D CNN model delivered the highest predictive accuracy, achieving the lowest RMSE (0.759 t/ha) and MAE (0.585 t/ha) among all evaluated models. Later, the study integrates ceteris paribus analysis and factor-wise partial derivatives, demonstrated through a nationwide wheat yield case study. The presented research offers deeper insights into the association between farmers’ information demand and wheat crop productivity, potentially informing the formulation of evidence-based agricultural interventions.

Purpose The purpose of this study is to develop an algorithm for approximate solutions of nonlinear hyperbolic partial differential equations. Design/methodology/approach In this paper, an algorithm based on the Haar wavelets operational matrix for computational modelling of nonlinear hyperbolic type wave equations has been developed. These types of equations describe a variety of physical models in nonlinear optics, relativistic quantum mechanics, solitons and condensed matter physics, interaction of solitons in collision-less plasma and solid-state physics, etc. The algorithm reduces the equations into a system of algebraic equations and then the system is solved by the Gauss-elimination procedure. Some well-known hyperbolic-type wave problems are considered as numerical problems to check the accuracy and efficiency of the proposed algorithm. The numerical results are shown in figures and Linf, RMS and L2 error forms. Findings The developed algorithm is used to find the computational modelling of nonlinear hyperbolic-type wave equations. The algorithm is well suited for some well-known wave equations. Originality/value This paper extends the idea of one dimensional Haar wavelets algorithms (Jiwari, 2015, 2012; Pandit et al., 2015; Kumar and Pandit, 2014, 2015) for two-dimensional hyperbolic problems and the idea of this algorithm is quite different from the idea for elliptic problems (Lepik, 2011; Shi et al., 2012). Second, the algorithm and error analysis are new for two-dimensional hyperbolic-type problems.

Time series analysis and prediction have attained significant attention from the research community in the past few decades. However, the prediction accuracy of the models highly depends on the models’ learning process. In order to optimize resource usage, a better learning methodology, in terms of accuracy and learning time, is needed. In this context, the current research work proposes EvoLearn, a novel method to improve and optimize the learning process of neural-based models. The presented technique integrates the genetic algorithm with back-propagation to train model weights during the learning process. The fundamental idea behind the proposed work is to select the best components from multiple models during the training process to obtain an adequate model. To demonstrate the applicability of EvoLearn, the method is tested on the state-of-the-art neural models (namely MLP, DNN, CNN, RNN, and GRU), and performances are compared. Furthermore, the presented study aims to forecast two types of time series, i.e. air pollution and energy consumption time series, using the developed framework. In addition, the considered neural models are tested on two datasets of each time series type. From the performance comparison and evaluation of EvoLearn using a one-tailed paired T -test against the conventional back-propagation-based learning approach, it was found that the proposed method significantly improves the prediction accuracy.

To meet the challenges of increasing food production demand globally, extracting insights regarding the persistent agriculture-related problems on a nationwide scale is the need of the hour. Policymakers now have limited possibilities for acquiring a comprehensive knowledge of the difficulties that farmers face on a national level. In this direction, the presented work proposes a new artificial intelligence-based pipeline to gain insights at country level regarding the farmers’ demand for assistance in India. The presented study uses the data from the Kisan Call Centres, a nationwide network of farmer’s helplines, including 28.6 million call-log records, made available by the Ministry of Agriculture & Farmers’ Welfare, Government of India. Additionally, the extracted insights are presented in the form of “Topic-wise Problems’ Trend Clusters” (TPTC), which can be used by policymakers in both the government and private sectors to aid decision-making. The article also introduces a pipeline for designing forecasting models to estimate the monthly frequency of farmer inquiries (in terms of the number of query calls). The seven statistical forecasting models were examined in the study with the TBATP1 (Trigonometric seasonal components with Box-Cox transformation incorporating ARIMA errors and Trend including the Seasonal components) model attaining the lowest error rates in terms of Root Mean Square Error (0.034) and Mean Absolute Error (0.107). The study also explores numerous applications of the derived insights in the real world as well as the future scope of the presented work.