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Background: Disease incidence and prevalence are both core indicators of population health. Incidence is generally not as readily accessible as prevalence. Cohort studies and electronic health record systems are two major way to estimate disease incidence. The former is time-consuming and expensive; the latter is not available in most developing countries. Alternatively, mathematical models could be used to estimate disease incidence from prevalence.Methods: We proposed and validated a method to estimate the age-standardized incidence of cardiovascular disease (CVD), with prevalence data from successive surveys and mortality data from empirical studies. Hallett's method designed for estimating HIV infections in Africa was modified to estimate the incidence of myocardial infarction (MI) in the U.S. population and incidence of heart disease in the Canadian population.Results: Model-derived estimates were in close agreement with observed incidence from cohort studies and population surveillance systems. This method correctly captured the trend in incidence given sufficient waves of cross-sectional surveys. The estimated MI declining rate in the U.S. population was in accordance with the literature. This method was superior to closed cohort, in terms of the estimating trend of population cardiovascular disease incidence.Conclusion: It is possible to estimate CVD incidence accurately at the population level from cross-sectional prevalence data. This method has the potential to be used for age- and sex- specific incidence estimates, or to be expanded to other chronic conditions. [ABSTRACT FROM AUTHOR]

Oat (Avena sativa L.) is one of the most important forage crops in the Southern Great Plains of the United States. However, it is more sensitive to cold stress than other small grains. In this study, diverse oat germplasm was evaluated for winter survival across multiple years and locations in the region. Field screening started with an observation trial of 1,861 diverse genotypes in the 2012–2013 season and was followed by four seasons of replicated trials from 2013 to 2017. Selection of good winter survivors was started in 2014–2015 season. All trials were laid out in randomized complete blocks with replications of two in 2013–2014 and 2014–2015, four in 2015–2016, and three in 2016–2017. Winter survival was scored in a 1‐to‐9 scale. Data were analysed for each year and location separately. Additive main effects and multiplicative interaction (AMMI) analysis were carried out on combined data of 35 genotypes that were commonly grown in each year and location. Highly significant (p < 0.001) variations were observed among genotypes, environments and genotype‐by‐environment interaction (GEI). The first three interaction principal components (IPCs) were highly significant (p < 0.001), explaining 96% of GEI. Broad sense heritability ranged from 46% to 93%, while heritability for all environments combined was relatively low (24.6%). At the end of the two cycles (2014/2015‐to‐2016/2017) of selection, mean winter survival was improved by more than 38% per cycle compared with the base population mean. Genotypes CIav 4390, CIav 6909 and CIav 7618 showed significantly higher winter survival than the standard checks Okay and Dallas. Genotypes CIav 4390 showed 20% and 35% improvement over the standard checks Okay and Dallas, respectively. Winter survival improvement in oat will remain a difficult task because of high GEI and low heritability. The identified superior genotypes will be used as crossing parents to transfer cold tolerance genes to other elite lines. [ABSTRACT FROM AUTHOR]

Alfalfa ( Medicago sativa L.) is one of the most widely grown crops in the USA. Phosphate (P) deficiency is common in areas where forage crops are grown. To improve the use of organic phosphate by alfalfa, two Medicago truncatula genes, phytase ( MtPHY1) and purple acid phosphatase ( MtPAP1), were overexpressed in alfalfa under the control of the constitutive CaMV35S promoter or the root-specific MtPT1 promoter. Root enzyme activity analyses revealed that although both genes lead to similar levels of acid phosphatase activities, overexpression of the MtPHY1 gene usually results in a higher level of phytase activity than overexpression of the MtPAP1 gene. The MtPT1 promoter was more effective than the CaMV35S promoter in regulating gene expression and extracellular secretion under P-deficient conditions. Measurement of growth performance of the transgenic lines further proved that the best promoter-gene combination is the MtPHY1 gene driven by the MtPT1 promoter. Compared to the control, the plants with high levels of transgene expression showed improved growth. The biomass of several transgenic lines was three times that of the control when plants were grown in sand supplied with phytate as the sole P source. When the plants were grown in natural soils without additional P supplement, the best performing transgenic lines produced double the amount of biomass after 12 weeks (two cuts) of growth. Transgene effects were more obvious in soil with lower pH and lower natural P reserves than in soil with neutral pH and relatively higher P storage. The total P concentration in leaf tissues of the high-expressing transgenic lines was significantly higher than that of the control. The transgenes have great potential for improving plant P acquisition and biomass yield in P-deficient agricultural soils. [ABSTRACT FROM AUTHOR]

• Bitcoin has appeared as safe haven asset and a winner amid uncertainties in the US. • Political and economic uncertainties can lead the bitcoin prices in the US. • Wavelet-based quantile-on-quantile approach helps to validate safe haven hypothesis. • Causality structure between uncertainties and bitcoin prices varies in cycles. • Investors in the US may cautiously use bitcoin as a hedge against uncertainties. Bitcoin is considered to be an exclusive marvel of the Fourth Industrial Revolution, and is one of the most sophisticated technological and financial products. It has long been a pivot point of attention for investors who are in pursuit of a safe haven asset. In this paper, we use the wavelet-based quantile-on-quantile method, and the quantile-based Granger causality method, in order to investigate the notion of Bitcoin in actually being a safe-haven asset, amid political and economic uncertainty in the US for the period between 2010:M06, and 2020:M10. Using the Partisan Conflict Index (PCI), and the Economic Policy Uncertainty Index (EPU) as proxies of uncertainties, we find that although Bitcoin effectively appears to be a safe haven asset when uncertainties are on the rise, however, this relationship tends to change during the short- to long-run. In this regard, our sample provides us with a unique opportunity to evaluate the safe haven hypothesis for Bitcoin, amid a time span with three Presidential elections in the US, and recently, an ongoing COVID-19 outbreak, which has been declared as a global pandemic. We have also supplemented our analysis with the bootstrap rolling window causality method, as a measure of robustness. In light of the recent COVID-19 pandemic, and the dynamic economic situation, our work provides valuable knowledge for investors, who wish to construct investment portfolios based on Bitcoin, and also provide insights for regulators about how to regulate the cryptocurrency speculation in an effective manner. [ABSTRACT FROM AUTHOR]

Following the adaptation of the Paris Agreement at COP21, it was noted that the traditional measures of carbon emissions have several limitations; and a reliable and relevant carbon emissions measurement is important to formulate a response to the challenge of climate change. This study, therefore, explores the relationship between international trade and consumption-based carbon emissions, which is a trade adjusted indicator; and measures the outflow and the inflow of emissions through exports and imports separately. We also include technological innovation in the model to understand its impact on consumption-based carbon emissions. The results show that exports and consumption-based carbon emissions are negatively associated, and technological innovation helps reducing the adverse effect of CO 2 growth. In contrast, Imports and gross domestic product are positively linked with consumption-based carbon emissions. The findings also suggest the countries which embraced the Paris Climate Agreement must focus on consumption-based carbon emissions rather than the production-based carbon emissions. • We employ ARDL bound test and error correction model to analyze consumption based carbon emission in USA. • Consumption-based carbon emission is a reliable and more appropriate measure of climate change. • Technological innovation is an important factor in formulating policy response to combat climate change. • Exports play a positive role and help reducing carbon emission. • United States of America (USA) is the net importer of carbon emission. [ABSTRACT FROM AUTHOR]

Drug-associated kidney injury is related to longer hospitalization and increased risk of chronic kidney disease and mortality. However, there is currently a lack of large population studies on drug-associated kidney injury in children. This study aimed to study perform data mining to generate hypotheses on drugs, which may deserve to be assessed as per their potential risk of increasing kidney injury in children. We extracted and analyzed reports on drugs associated with kidney injury in children in the FDA Adverse Event Reporting System (FAERS). We conducted a disproportionality analysis using proportional reporting ratio (PRR) to evaluate the association between drugs and kidney injury in children. Meanwhile, comparisons were performed with drug labels to identify drugs that, despite not having kidney injury currently mentioned in their labels, may potentially be associated with risks of kidney injury in children. A total of 6347 children had drug-associated kidney injury in the FAERS database. The top five drugs with the highest PRR were gentamicin (PRR = 12.28, N = 157 cases, Chi-Squared = 1602.77), piperacillin-tazobactam (PRR = 9.77, N = 129 cases, Chi-Squared = 1003.24), amlodipine (PRR = 8.98, N = 271 cases, Chi-Squared = 1861.46), vancomycin (PRR = 8.91, N = 295 cases, Chi-Squared = 1998.64), and ceftriaxone (PRR = 8.00, N = 251 cases, Chi-Squared = 1494.02). According to drug labels, 9 drugs (9/30) were classified as potential nephrotoxins., Conclusions: Approximately one-third of drugs associated with kidney injury in children do not list kidney injury as a side effect in their drug labels. Future studies are therefore warranted to evaluate whether these drugs are associated with such a risk., What Is Known: • Nephrotoxic drugs are an increasingly common cause of acute kidney injury in hospitalized children. • Currently, no study has systematically combed drugs associated with kidney injury in children., What Is New: • Approximately a third of drugs showing signals for potential kidney injury in children in data mining do not mention this side effect in their drug labels. • This study provides data on drugs needing further study to determine whether they might increase the risk of kidney injury in children., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Classical plant breeding has been instrumental in changing the genetic makeup of crop plants for better ecological adaptation and improved quality. This paper provides insights of the genomic changes effected in hard winter wheat (Triticum aestivum L.) through decades of breeding and selection in the Great Plains of the United States. Population structure and differentiation analyses were conducted on 185 wheat cultivars released from 1943 to 2013. Cultivars were grouped into four distinct clusters using discriminant analysis of principal components (DAPC). One of the clusters was unique in that 15 out of the 18 individuals were recent releases (2000-2010), while 12 of the 18 shared the cultivar 'Jagger' in their genetic background. Jagger carries a 2NS/2AS translocation segment from Aegilops ventricosa, an important segment for resistance to several foliar diseases. Using the outlier approach, Wright's population fixation index (Fst) identified 450 loci that were directionally selected. The largest signature of selection was found on chromosome 2A. Genetic diversity was high while the inbreeding coefficient was low, indicating extensive hybridization and germplasm exchange among breeding programs within the region. Foliar disease pressure and selection for resistance helped shape the microevolution of wheat in the southern Great Plains. The results showed that high genetic diversity remains in hard winter wheat cultivars adapted to the Great Plains of the USA, and modern plant breeding did not cause any sizable reduction in genetic diversity of the crop in this region., (© 2020 The Authors. The Plant Genome published by Wiley Periodicals, Inc. on behalf of Crop Science Society of America.)

Experimental studies have demonstrated statin-induced toxicity for ovary and uterus. However, the safety of statins on the functions of ovary and uterus in real-world clinical settings remains unknown. The aim of this study was to identify ovary and uterus related adverse events (AEs) associated with statin use by analyzing data from FDA Adverse Event Reporting System (FAERS). We used OpenVigil 2.1 to query FAERS database. Ovary and uterus related AEs were defined by 383 Preferred Terms, which could be classified into ten aspects. Disproportionality analysis was performed to assess the association between AEs and statin use. Our results suggest that statin use may be associated with a series of ovary and uterus related AEs. These AEs are involved in ovarian cysts and neoplasms, uterine neoplasms, cervix neoplasms, uterine disorders (excl neoplasms), cervix disorders (excl neoplasms), endocrine disorders of gonadal function, menstrual cycle and uterine bleeding disorders, menopause related conditions, and sexual function disorders. Moreover, there are variabilities in the types and signal strengths of ovary and uterus related AEs across individual statins. According to our findings, the potential ovary and uterus related AEs of statins should attract enough attention and be closely monitored in future clinical practice.

Background: The factors affecting the prognosis and role of adjuvant therapy in advanced gallbladder carcinoma (GBC) after curative resection remain unclear., Aim: To provide a survival prediction model to patients with GBC as well as to identify the role of adjuvant therapy., Methods: Patients with curatively resected advanced gallbladder adenocarcinoma (T3 and T4) were selected from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. A survival prediction model based on Bayesian network (BN) was constructed using the tree-augmented naïve Bayes algorithm, and composite importance measures were applied to rank the influence of factors on survival. The dataset was divided into a training dataset to establish the BN model and a testing dataset to test the model randomly at a ratio of 7:3. The confusion matrix and receiver operating characteristic curve were used to evaluate the model accuracy., Results: A total of 818 patients met the inclusion criteria. The median survival time was 9.0 mo. The accuracy of BN model was 69.67%, and the area under the curve value for the testing dataset was 77.72%. Adjuvant radiation, adjuvant chemotherapy (CTx), T stage, scope of regional lymph node surgery, and radiation sequence were ranked as the top five prognostic factors. A survival prediction table was established based on T stage, N stage, adjuvant radiotherapy (XRT), and CTx. The distribution of the survival time (>9.0 mo) was affected by different treatments with the order of adjuvant chemoradiotherapy (cXRT) > adjuvant radiation > adjuvant chemotherapy > surgery alone. For patients with node-positive disease, the larger benefit predicted by the model is adjuvant chemoradiotherapy. The survival analysis showed that there was a significant difference among the different adjuvant therapy groups (log rank, surgery alone vs CTx, P < 0.001; surgery alone vs XRT, P = 0.014; surgery alone vs cXRT, P < 0.001)., Conclusion: The BN-based survival prediction model can be used as a decision-making support tool for advanced GBC patients. Adjuvant chemoradiotherapy is expected to improve the survival significantly for patients with node-positive disease., Competing Interests: Conflict-of-interest statement: All authors declare no conflict of interest related to this article., (©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.)

Background Observational studies have suggested that selenium (Se) may have beneficial effects against certain cardiovascular outcomes, with a possible U-shaped association. We assessed the hypothesis that blood Se concentration might be inversely associated with the prevalence of stroke and the relationship would be nonlinear. Methods and Results Data collected from adult participants (aged ≥20 years) in the Canadian Health Measures Survey ( CHMS 2007-2011, n=7065) and the US National Health and Nutrition Examination Survey ( NHANES 2011-2012, n=5030) were analyzed. A total of 82 (1.16%) and 202 (4.02%) stroke cases were identified in CHMS and NHANES . Respondents with stroke had lower Se levels than those without stroke, with a mean difference of 16 μg/L and 12 μg/L for CHMS and NHANES , respectively. Respondents with high blood Se concentration (tertile 3) had a lower prevalence of stroke compared with those with low Se concentration (tertile 1). The adjusted odds ratios were 0.38 (95% CI : 0.15, 0.92) and 0.57 (95% CI : 0.31, 1.03) for CHMS and NHANES , respectively. A continuous decreasing trend of stroke with whole blood selenium was observed in CHMS , whereas the curve plateaued starting at 190 μg/L for NHANES , based on the cubic restricted spline regression. Sensitivity analysis using the serum and urinary Se concentrations demonstrates that our results were consistent across different selenium biomarkers. Conclusions We observed inverse cross-sectional associations between whole blood Se and the prevalence of stroke in representative samples of the Canadian and the US population.

Carcinogenic and mutagenic polycyclic aromatic hydrocarbons (PAHs) generated in coal combustion have caused great environmental health concern. Seventeen PAHs (16 high priority PAHs recommended by USEPA plus Benzo[e]pyrene) present in five raw bituminous coals and released during bituminous coal combustion were studied. The effects of combustion temperature, gas atmosphere, and chlorine content of raw coal on PAHs formation were investigated. Two additives (copper and cupric oxide) were added when the coal was burned. The results indicated that significant quantities of PAHs were produced from incomplete combustion of coal pyrolysis products at high temperature, and that temperature is an important causative factor of PAHs formation. PAHs concentrations decrease with the increase of chlorine content in oxygen or in nitrogen atmosphere. Copper and cupric oxide additives can promote PAHs formation (especially the multi-ring PAHs) during coal combustion.