High-Precision Potential Evapotranspiration Model Using GNSS Observation

Potential evapotranspiration (PET) can reflect the characteristics of drought change in different time scales and is the key parameter for calculating the standardized precipitation evapotranspiration index (SPEI). The Thornthwaite (TH) and Penman–Monteith (PM) models are generally used to calculate PET, but the precision of PET derived from the TH model is poor, and a large number of meteorological parameters are required to evaluate the PM model. To obtain high-precision PET with fewer meteorological parameters, a high-precision PET (HPET) model is proposed to calculate PET by introducing precipitable water vapor (PWV) from Global Navigation Satellite System (GNSS) observation. The PET difference (DPET) between TH- and PM-derived PET was calculated first. Then, the relationship between the DPET and GNSS-derived PWV/temperature was analysed, and a piecewise linear regression model was calculated to fit the DPET. Finally, the HPET model was established by adding the fitted DPET to the initial PET derived from the TH model. The Loess Plateau (LP) was selected as the experiment area, and the statistical results show the satisfactory performance of the proposed HPET model. The averaged root mean square (RMS) of the HPET model over the whole LP area is 8.00 mm, whereas the values for the TH and revised TH (RTH) models are 34.25 and 12.55 mm, respectively, when the PM-derived PET is regarded as the reference. Compared with the TH and RTH models, the average improvement rates of the HPET model over the whole LP area are 77.5 and 40.5%, respectively. In addition, the HPET-derived SPEI is better than that of the TH and RTH models at different month scales, with average improvement rates of 49.8 and 23.1%, respectively, over the whole LP area. Such results show the superiority of the proposed HPET model to the existing PET models.

Fasting Blood Glucose to High-Density Lipoprotein Cholesterol Ratio in Relation to Short-Term Outcomes in Patients with Acute Coronary Syndrome

BackgroundBiochemical markers are crucial for determining risk in patients with coronary artery disease (CAD); however, the association between the fasting blood glucose to high-density lipoprotein cholesterol (FG/HDL-C) ratio and short-term outcomes in patients with acute coronary syndrome (ACS) remains unknown. We investigated the association between the FG/HDL-C ratio and 30-day major adverse cardiovascular events (MACEs) and cardiovascular (CV) death in patients with ACS.eMethodsWe performed a post-hoc analysis of data from the Acute Coronary Syndrome Quality Improvement in Kerala (ACS-QUIK) study. A total of 11,284 patients with ACS were subdivided into quartiles according to their FG/HDL-C ratios. We used a multivariate logistic regression model, generalized additive model (GAM), and two-piecewise linear regression model to determine the association of the FG/HDL-C ratio with MACEs (death, reinfarction, stroke, and major bleeding) and CV death. ResultsThe FG/HDL-C ratio was significantly associated with an increased risk of MACEs and CV death in patients with ACS in the highest quartile (MACEs, odds ratio [OR]: 1.49; 95% confidence interval [CI], [1.11, 1.99]; P<0.01; CV death, OR: 1.69; 95% CI, [1.01, 1.41]; P=0.04). The GAM and two-piecewise linear regression model demonstrated that the relationship between the FG/HDL-C ratio and MACEs and CV death was non-linear (non-linear P<0.05); the threshold values were 3.02 and 3.00 for MACEs and CV death, respectively.ConclusionsA higher FG/HDL-C ratio is associated with an increased risk of MACEs and CV death in patients with ACS.

Platelet-to-Lymphocyte Ratio and Neutrophil-to-Lymphocyte Ratio in Patients With Newly Diagnosed Moyamoya Disease: A Cross-Sectional Study

Inflammation has been proven to be one of the key factors in the pathogenesis of moyamoya disease (MMD). Platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) are cheap and reliable biomarkers of inflammation. Nevertheless, evidence regarding the relationship among PLR and NLR in patients with MMD is limited. The focus of this subject was to explore the relationship between PLR and NLR in patients with newly diagnosed MMD.Patients and methods: A cross-sectional study was performed including 261 patients with diagnosed MMD for the first time who were enrolled from our hospital, from 24 March 2013 to 24 December 2018. The clinical characteristics were collected for each patient. Univariate analysis, smooth curve fitting and multivariate piecewise linear regression were showed.Results: The mean levels or median values (interquartile range) of PLR and NLR were 146.979 ± 51.203 and 2.241 (1.589–2.984), respectively. A significant positive correlation between PLR and NLR levels (P &lt; 0.001) was showed by the univariate analysis. Furthermore, a non-linear relationship was detected between PLR and NLR by smooth curve fitting after adjusting for potential confounders. A multivariate piecewise linear regression model revealed a significant positive correlation between PLR and NLR when the PLR level was lower than 219.82 (β 0.012, 95% CI 0.005, 0.019; P = 0.001). PLR was also significantly positively associated with NLR when PLR concentrations were &gt;219.82 (β 0.098, 95% CI 0.069, 0.128; P &lt; 0.001).Conclusion: There seemed to be a positive association between PLR and NLR in patients with MMD. This may help to further explain the role of inflammation in the occurrence and progress of MMD.

Association of stent diameter and target vessel revascularization in patients undergoing percutaneous coronary intervention: a secondary retrospective analysis based on a Chinese cohort study

Background In the treatment of coronary heart disease, target vessel revascularization (TVR) has attracted increasing attention as an efficient means of percutaneous coronary intervention (PCI). The purpose of this study was to explore the association between stent diameter and TVR in patients undergoing PCI. Methods This was a secondary retrospective analysis involving patients with PCI with at least one stent implanted. Information was obtained from the Dryad Digital Repository. Multivariable logistic regression models, interaction analyses, subgroup analyses and piecewise linear regression models were used to evaluate the association between stent diameter and TVR. Results A total of 2522 patients were eventually enrolled in this study, of which 122 (4.8%) had undergone TVR. Significant positive associations were observed between stent diameter and TVR (continuous: odds ratio [OR] 0.485, 95% confidence interval [CI] 0.305–0.773, P = 0.002; categorical variable: T2 vs. T1, OR 0.541, 95% CI 0.348–0.843; T3 vs. T1, OR 0.520, 95% CI 0.334–0.809; P for trend = 0.005). The association remained stable in the fully adjusted model (continuous: OR 0.526, 95% CI 0.306–0.902, P = 0.020; categorical variable: T2 vs. T1, OR 0.510, 95% CI 0.310–0.839; T3 vs. T1, OR 0.585, 95% CI 0.352–0.973; P for trend = 0.042). Among the subgroups of differing clinical presentations, stent diameter was a powerful protective factor for TVR, especially in the delayed PCI group (P for interaction = 0.002). The association was highly consistent across all the other subgroups studied (all P for interaction > 0.05). In the piecewise linear regression model, the need for TVR decreased with an increase in stent diameter when this ranged between 2.5 and 2.9 mm (OR 0.01, 95% CI: 0.01–0.13, P < 0.001). Conclusions A large stent diameter is a powerful protective factor for TVR in PCI patients, especially in the delayed PCI group. This “bigger-is-better” protective effect is remarkable in stents with diameter 2.5–2.9 mm.

Fluoride and Arsenite Removal by Adsorption on La2O3-CeO2/Laterite

In the present article, the adsorbent prepared from laterite with lanthanum and cerium oxides (La2O3-CeO2/laterite (LCL)) was efficiently employed for the removal of arsenite and fluoride from an aqueous environment. The obtained materials were characterized by XRD, SEM, and nitrogen adsorption/desorption. The synthesized LCL exhibited a high adsorption capacity towards arsenite (As(III)) and fluoride. The adsorption of both analytes on LCL, which was well-fitted to a pseudo-second-order equation, was found to be kinetically fast in the first 20 minutes and reached equilibrium at around 180 minutes. Weber’s intraparticle diffusion model in multilinearity using the piecewise linear regression combined with Akaike’s criteria was addressed. The adsorption capacities of LCL calculated from Langmuir’s isotherm model were found to be 67.08 mg·g-1 for arsenite and 58.02 mg·g-1 for fluoride. Thermodynamic parameters presented an endothermic nature of arsenite adsorption but an exothermic nature for fluoride and a negative Gibbs free energy for the spontaneous process of arsenite or fluoride adsorption at the studied temperature range. The excellent adsorption performance and stability make the composite of laterite and La-Ce binary oxides an alternative efficient and cheap adsorbent for the removal of arsenite and fluoride in an aqueous solution.

Hb Levels and Sex Differences in Relation to Short-Term Outcomes in Patients With Acute Myocardial Infarction

Background: Women had worse outcomes after acute myocardial infarction (AMI), and physiologically, women had lower hemoglobin values. We examined whether there were sex-related differences in the relationship between hemoglobin levels and adverse outcomes in patients with acute myocardial infarction.Method: We conducted a post-hoc analysis of data from the Acute Coronary Syndrome Quality Improvement in Kerala (ACS-QUIK) Study. We explored the relationship between baseline hemoglobin level and 30-days adverse outcomes by logistic regression model, generalized additive model (GAM) and two-piecewise linear regression model. We used multiple imputation, based on five replications and a chained equation approach method in the R multiple imputation procedure, to account for missing data. The primary outcome were 30-day major adverse cardiovascular events (MACEs) defined as death, reinfarction, stroke, and major bleeding. The secondary outcomes were 30-day major bleeding, 30-day stroke and 30-day cardiovascular death (CVD death).Results: Twenty thousand, five hundred fifty-nine patients with AMI were included in our analysis. Baseline hemoglobin level was associated with major bleeding [OR: 0.74, 95%CI (0.60, 0.92) P &lt; 0.01], CVD death [OR: 0.94, 95%CI (0.90, 0.99) P &lt; 0.01], and MACEs [OR: 0.95, 95%CI (0.92, 0.99) P &lt; 0.01]. There was no significant relationship between baseline hemoglobin level and stroke incidence in both men [OR: 1.02, 95%CI (0.90, 1.14) P = 0.77] and women [OR: 1.15, 95%CI (0.96, 1.37) P = 0.18]. Baseline hemoglobin level was associated with major bleeding [OR: 0.71, 95%CI (0.58, 0.85) P &lt; 0.01] in male patients, however we did not find the same relationship in female patients [OR: 0.89, 95%CI (0.56, 1.41) P = 0.61]. GAM and two-piecewise linear regression model showed the relationships of hemoglobin level with major bleeding, CVD death, and MACEs were non-linear (non-linear P &lt; 0.05), and the threshold value were 13, 14.8, and 14.3 g/dL for MACEs and CVD death, respectively.Conclusion: Baseline hemoglobin level was one of the independent predictors of prognosis in South Asia patients with acute myocardial infarction. Moreover, its impact on prognosis was largely different depending on the patients' sex.

Reopening Italy’s schools in September 2020: a Bayesian estimation of the change in the growth rate of new SARS-CoV-2 cases

ObjectivesCOVID-19’s second wave started a debate on the potential role of schools as a primary factor in the contagion resurgence. Two opposite positions appeared: those convinced that schools played a major role in spreading SARS-CoV-2 infections and those who were not. We studied the growth rate of the total number of SARS-CoV-2 infections in all the Italian regions, before and after the school reopening (September–October 2020), investigating the hypothesis of an association between schools and the resurgence of the virus.MethodsUsing a Bayesian piecewise linear regression to scrutinise the number of daily SARS-CoV-2 infections in each region, we looked for an estimate of a changepoint in the growth rate of those confirmed cases. We compared the changepoints with the school opening dates, for each Italian region. The regression allows to discuss the change in steepness of the infection curve, before and after the changepoint.ResultsIn 15 out of 21 Italian regions (71%), an estimated change in the rate of growth of the total number of daily SARS-CoV-2 infection cases occurred after an average of 16.66 days (95% CI 14.47 to 18.73) since the school reopening. The number of days required for the SARS-CoV-2 daily cases to double went from an average of 47.50 days (95% CI 37.18 to 57.61) before the changepoint to an average of 7.72 days (95% CI 7.00 to 8.48) after it.ConclusionStudying the rate of growth of daily SARS-CoV-2 cases in all the regions provides some evidence in favour of a link between school reopening and the resurgence of the virus. The number of factors that could have played a role is too many to give a definitive answer. Still, the temporal correspondence warrants further systematic experiments to investigate on potential confounders that could clarify how much reopening schools mattered.

Reopening Italy's Schools in September 2020: A Bayesian Estimation of the Change in the Growth Rate of New SARS-CoV-2 Cases

Objectives: CoViD-19's second wave started a debate on the potential role of schools as a primary factor in the contagion resurgence. Two opposite positions appeared: those convinced that schools played a major role in spreading SARS-CoV-2 infections and those who were not. We studied the growth rate of the total number of SARS-CoV-2 infections in all the Italian regions, before and after the school reopening (September - October 2020), investigating the hypothesis of an association between schools and the resurgence of the virus in Italy. Methods: Using Bayesian piecewise linear regression to scrutinize the number of daily SARS-CoV-2 infections in each Italian, we looked for an estimate of a changepoint in the growth rate of those confirmed cases. We compared the changepoints with the school opening dates, for each Italian region. The regression allows to discuss the change in steepness of the infection curve, before and after the changepoint. Results: In 15 out of 21 Italian regions (71%), an estimated change in the rate of growth of the total number of daily SARS-CoV-2 infection cases occurred after an average of 16.66 days (CI 95% 14.47 to 18.73) since the school reopening. The number of days required for the SARS-CoV-2 daily cases to double went from an average of 47.50 days (CI 95% 37.18 to 57.61) before the changepoint to an average of 7.72 days (CI 95% 7.00 to 8.48) after it. Conclusion: Studying the rate of growth of daily SARS-CoV-2 cases in all the Italian regions provides some evidence in favor of a link between school reopening and the resurgence of the virus in Italy. The number of factors that could have played a role are too many to give a definitive answer. Still, the temporal correspondence warrants for a controlled experiment to clarify how much reopening schools mattered.