Least-squares Regression Model Research Articles

Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland.

Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Ecological Observatory Network's (NEON's) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high-resolution trait mapping. We tested the accuracy of readily available data products of NEON's AOP, such as Leaf Area Index (LAI), Total Biomass, Ecosystem Structure (Canopy height model [CHM]), and Canopy Nitrogen, by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground-measured LAI (r = 0.32) and AOP Total Biomass and ground-measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380-2,500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression (PLSR) models. Among all the eight traits examined, only Nitrogen had a validation of more than 0.25. For all vegetation traits, validation ranged from 0.08 to 0.29 and the range of the root mean square error of prediction (RMSEP) was 14-64%. Our results suggest that currently available AOP-derived data products should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field-based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogeneous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time. But the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in-situ field measurements across a diversity of sites.

Abstract 11780: Longitudinal Echocardiographic Parameters Before and After Pacemaker Placement in Congenital Complete Heart Block

Introduction: Congenital complete heart block (CCHB) is rare, 1:15,000 - 1:20,000 live births, with left ventricular (LV) dysfunction developing in 7-14% of subjects. We investigate longitudinal trajectories of echo parameters in CCHB and the effect of pacemaker implantation on those parameters. Methods: This is a retrospective cohort study of identifiable CCHB subjects at the Children’s Hospital of Philadelphia from 1970-2018, excluding those with structural, genetic, or acquired cardiac conditions. Subjects with echo data available before and after pacemaker implantation are included. LV end diastolic diameter (LVEDD) z-score and LV ejection fraction (EF) are modeled with least-squares regression models, with slopes compared before and after pacemaker placement. Results: Of 114 subjects with CCHB, 52 (40% male, 73% white) have echo data available before and after pacemaker placement. Median age of diagnosis is 0.6 years, age at pacemaker placement 3.4 years, and duration of pacing 10.8 years. Figure 1 displays modeled trajectories of LVEDD z-score and EF over 20 years. Mean LVEDD z-score at pacemaker placement is less than 2 and decreases significantly over time, especially 10 years post placement. Participants have mean LV ejection fraction of 54% at pacemaker placement, rising significantly prior to placement and in the first 10 years post-placement, after which significant decreases are observed. Older age at pacemaker placement is associated with a higher EF at placement but an earlier and steeper decline in EF beginning at 8 years post-placement. LVEDD and EF trajectories do not vary significantly by sex, race, maternal antibody status, age at diagnosis, or initial pacemaker mode. Conclusions: In a cohort of 52 subjects with CCHB, LVEDD z-score steadily decreases over time whereas EF rises for 10 years post-pacemaker placement and subsequently decreases. Pacemaker placement has no apparent immediate effect on trajectories of these echo parameters.

Psychological distress during the COVID-19 epidemic in Chile: The role of economic uncertainty.

Previous research has shown that the COVID-19 outbreak, social distancing, and lockdown can affect people's psychological well-being. The aims of this study were (1) to estimate the extent to which perceptions and expectations regarding the social, economic, and domestic effects of the COVID-19 outbreak are associated with psychological distress and (2) to identify some demographic, psychosocial, and economic factors associated with increased vulnerability to psychological distress during the COVID-19 outbreak in Chile. 1078 people participated in a telephone survey between May 30 and June 10, 2020. The sample is representative of the Chilean adult population. Psychological distress was assessed through a questionnaire of anxious and depressive symptoms (Patient Health Questionnaire-4). We analyzed the data set using ordinary least-squares regression models, first estimating models for the entire sample, and then stratifying the sample into different groups to explore differences by gender and age. 19.2% of participants displayed significant psychological distress (PHQ-4 ≥ 6), with moderate to severe anxiety-depression symptoms being more prevalent in women than in men (23.9% vs 14.1%, χ2 16.78, p<0.001). The results of this study suggest that being a woman, feeling lonely and isolated, living in the areas hit hardest by the pandemic and lockdown, expecting a lack of income due to having to stop working as a consequence of the pandemic, and having a history of diagnosed mental disorders are significantly associated with psychological distress (p<0.05). The results of this study highlight the need to implement psychosocial programs to guard people's psychological well-being and social policies to address economic uncertainty during the current COVID-19 outbreak in Chile.

Rapid estimation of photosynthetic leaf traits of tropical plants in diverse environmental conditions using reflectance spectroscopy.

Tropical forests are one of the main carbon sinks on Earth, but the magnitude of CO2 absorbed by tropical vegetation remains uncertain. Terrestrial biosphere models (TBMs) are commonly used to estimate the CO2 absorbed by forests, but their performance is highly sensitive to the parameterization of processes that control leaf-level CO2 exchange. Direct measurements of leaf respiratory and photosynthetic traits that determine vegetation CO2 fluxes are critical, but traditional approaches are time-consuming. Reflectance spectroscopy can be a viable alternative for the estimation of these traits and, because data collection is markedly quicker than traditional gas exchange, the approach can enable the rapid assembly of large datasets. However, the application of spectroscopy to estimate photosynthetic traits across a wide range of tropical species, leaf ages and light environments has not been extensively studied. Here, we used leaf reflectance spectroscopy together with partial least-squares regression (PLSR) modeling to estimate leaf respiration (Rdark25), the maximum rate of carboxylation by the enzyme Rubisco (Vcmax25), the maximum rate of electron transport (Jmax25), and the triose phosphate utilization rate (Tp25), all normalized to 25°C. We collected data from three tropical forest sites and included leaves from fifty-three species sampled at different leaf phenological stages and different leaf light environments. Our resulting spectra-trait models validated on randomly sampled data showed good predictive performance for Vcmax25, Jmax25, Tp25 and Rdark25 (RMSE of 13, 20, 1.5 and 0.3 μmol m-2 s-1, and R2 of 0.74, 0.73, 0.64 and 0.58, respectively). The models showed similar performance when applied to leaves of species not included in the training dataset, illustrating that the approach is robust for capturing the main axes of trait variation in tropical species. We discuss the utility of the spectra-trait and traditional gas exchange approaches for enhancing tropical plant trait studies and improving the parameterization of TBMs.

Predicting monthly evaporation from dam reservoirs using LS-SVR and ANFIS optimized by Harris hawks optimization algorithm.

Evaporation is a crucial factor in hydrological studies; its precise measurement has always been challenging due to the costly recording tolls. Therefore, machine learning models that can give reliable predictive results with the least information available have been recommended for evaporation prediction. This study was conducted in the central of Iran using the data related to the Doroudzan dam. Several hydrological and meteorological variables, including inflow and outflow of the reservoir, lake area behind the dam, temperature, overflow from the reservoir, precipitation, and evaporation at the previous month, were considered input data to predict the evaporation at the current month. Monthly data from October 1999 to September 2020 were used during the modeling. First, the single adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector regression (LS-SVR) models were evaluated for predicting the amount of evaporation using different scenarios defined based on the different combinations of input variables. The results showed that LS-SVR with RMSE = 2.77, MAPE = 2.48, and NSE = 0.93 provided a better prediction than ANFIS. Second, the Harris hawks optimization (HHO) algorithm was used to optimize the parameters of ANFIS to check for the possibility of performance improvement. The hybrid ANFIS-HHO model predicted the evaporation with RMSE = 2.35, MAPE = 1.55, and NSE = 0.95, respectively. The Taylor's diagram also demonstrated the superior performance of the hybrid ANFIS-HHO model than the LS-SVR and ANFIS models. The best scenario for all three models included all input variables but the area behind the dam into the models. The methodology proposed in this study is useful for predicting the evaporation from dam reservoirs under the influence of various dam variables.

Abdominal and gluteofemoral fat depots show opposing associations with postprandial lipemia

High postprandial lipemia is associated with increased risk of cardiovascular disease, independently of fasting lipid concentrations. Abdominal and gluteofemoral fat depots handle lipoproteins differently, which could affect postprandial lipemia and contribute to the relation between abdominal fat distribution and cardiovascular disease risk. We aimed to study the influences of higher abdominal compared with gluteofemoral fat on postprandial lipemia after a high-fat meal in individuals with obesity. A total of 755 adults with obesity from a randomized controlled trial in 7 European countries consumed a liquid high-fat meal. Concentrations of triglycerides (TG), glycerol, free fatty acids, and the cholesterol component of remnant-like particles (RLP), LDL, and HDL were measured postprandially for 3 h. Associations of waist circumference (WC), hip circumference (HC), and waist-hip ratio (WHR) with changes in postprandial lipid concentrations, adjusted for fasting concentrations and BMI, were examined using linear regression models. To assess whether the association of WHR with postprandial lipemia could be causal, we performed instrumental variable analyses using a genetic score of 442 variants known to be associated with WHR adjusted for BMI in 2-stage least-squares regression models. WHR was associated with higher TG and RLP cholesterol concentrations, independent of fasting lipid concentrations and BMI. Instrumental variable analyses suggested that the associations of WHR with postprandial TG (β=0.038 μmol/L*min, SE=0.019 μmol/L*min, P=0.044) and RLP cholesterol concentrations (β=0.059 mmol/L, SE=0.025mmol/L, P=0.020) may be causal. WC and HC showed opposite effects: higher WC was associated with higher TG and RLP cholesterol concentrations whereas higher HC was associated with lower concentrations. Our results suggest that higher fat deposition abdominally versus gluteofemorally may be causally associated with elevated postprandial lipemia after a high-fat meal, independent of fasting lipid concentrations and BMI. Furthermore, higher abdominal and gluteofemoral fat depots show opposing effects on postprandial lipemia.This trial was registered at www.isrctn.com as ISRCTN25867281.

A New Indicator to Better Represent the Impact of Landscape Pattern Change on Basin Soil Erosion and Sediment Yield in the Upper Reach of Ganjiang, China

Landscape patterns are a result of the combined action of natural and social factors. Quantifying the relationships between landscape pattern changes, soil erosion, and sediment yield in river basins can provide regulators with a foundation for decision-making. Many studies have investigated how land-use changes and the resulting landscape patterns affect soil erosion in river basins. However, studies examining the effects of terrain, rainfall, soil erodibility, and vegetation cover factors on soil erosion and sediment yield from a landscape pattern perspective remain limited. In this paper, the upper Ganjiang Basin was used as the study area, and the amount of soil erosion and the amount of sediment yield in this basin were first simulated using a hydrological model. The simulated values were then validated. On this basis, new landscape metrics were established through the addition of factors from the revised universal soil loss equation to the land-use pattern. Five combinations of landscape metrics were chosen, and the interactions between the landscape metrics in each combination and their effects on soil erosion and sediment yield in the river basin were examined. The results showed that there were highly similar correlations between the area metrics, between the fragmentation metrics, between the spatial structure metrics, and between the evenness metrics across all the combinations, while the correlations between the shape metrics in Combination 1 (only land use in each year) differed notably from those in the other combinations. The new landscape indicator established based on Combination 4, which integrated the land-use pattern and the terrain, soil erodibility, and rainfall erosivity factors, were the most significantly correlated with the soil erosion and sediment yield of the river basin. Finally, partial least-squares regression models for the soil erosion and sediment yield of the river basin were established based on the five landscape metrics with the highest variable importance in projection scores selected from Combination 4. The results of this study provide a simple approach for quantitatively assessing soil erosion in other river basins for which detailed observation data are lacking.

Diversity, Length-Weight Relations, and Condition Factor of Sea Cucumbers in Three Coastal Areas Along the Southern Coast of Sri Lanka: A Case Study

Economically and ecologically important ecosystems that consist of diverse finfish and shellfish resources are found along the southern coast of Sri Lanka. Thus, the current study intends in assessing the species diversity, length-weight (L-W) relationships, and condition factor of sea cucumber stocks in selected coastal sites of southern Sri Lanka. The diversity of sea cucumber species was surveyed using the systematic sampling technique in three selected sites (Bandaramulla, Mirissa, and Weligama) during February-December 2019. The growth pattern of each species was identified using a least-squares regression model computed for length-weight relationships. Fulton’s condition factor (K) was analysed for each species. There were four common sea cucumber species (Holothuria atra - Lolly Fish, Bohadschia marmorata - White Thread fish, Actinipyga miliaris- Deep Water Blackfish, and Thelenota ananas- Prickly Redfish) in study sites, while Holothuria atra and Bohadschia marmorata were recorded as the most abundant species. The length-weight relationship derived for the above four species exhibited a negative allometric growth pattern (“b” &lt; “3”), indicating faster length increment compared to weight. The habitat qualities of all sea cucumber species were in optimum environmental condition. Our findings would benefit as baseline data for implementing sustainable management practices of sea cucumber resources.

Reconsidering the Need for Empirical Alignment and Wavelength Calibration Steps in the Building of a Dispersive NIR Spectrometer with an Application for Ethanol Quantification Using a Polymer Filament 3D Printer.

The construction of a dispersive optical spectrometer using three-dimensional (3D) design software and printing, without applying any optical adjustments, its validation, and application to quantification of ethanol in multiproduct liquids, is the objective of this work. A 3D design software was used to design a near-infrared (NIR) spectrometer in the region from 800 to 1600 nm from the dimensions of commercially available optical components. The project was printed on a polymer filament 3D printer, and the components were fitted to the printed part. Software calculations using the model design parameters were applied to attribute the wavelength values to the abscissa axis in the spectra and estimate errors due to 3D printing limitations. The alignment of the spectrum was proven using the chloroform absorbance spectrum, which presented a maximum mispositioning of 4.1 nm concerning the literature data and effective bandwidths equivalent to commercial instruments. The 3D-printed instrument was applied to quantify ethanol in samples of cachaça, rum, beer, brandy, whiskey, vodka, mouth wash, alcohol gel, and commercial alcohol solutions. Partial least-squares regression models were built for the 3D-printed instrument and two commercial NIR instruments, the MPA II (Bruker) and the NIR DLP NIRscan (Texas Instruments), using a group of 180 standards. The three instruments reached excellent predictive capability with similar root mean square error of cross-validation (2.36-2.68) and prediction (2.31-2.87). The correlation coefficient of cross-validation and prediction for all models were between 0.97 and 0.98. The results show the feasibility of building a 3D-printed dispersive spectrometer ready for application with the simple docking of the optics, presenting acceptable accuracy to the project design concerning the printing limitations.

Variable selection for the determination of the soluble solid content of potatoes with surface impurities in the visible/near-infrared range

Non-destructive quality assessment of the inner content of potatoes is a key challenge in automatic grading of agricultural quality, especially when potatoes have surface impurities. This study compares different partial least-square regression (PLSR) models for the prediction of soluble solid content (SSC) of potatoes under conditions of surface cleanliness and surface impurities. Smoothing and spectral preprocessing with first-order derivatives and variable sorting for normalization (VSN) can effectively eliminate spectral noise. Variable selection algorithms were used to extract effective variables in order to further optimise the prediction models. The results showed that the method of the variable combination population analysis—iteratively retains informative variables (VCPA-IRIV) is the best method for selecting valid variables, and that the 35-variable VCPA-IRIV-PLSR prediction model could predict the potato SSC with a predictive correlation coefficient ( R p ), root-mean-square error of prediction ( RMSE P ), and residual predictive deviation ( RPD ) values of 0.831, 0.461 ○ Brix and 1.798, respectively. Therefore, the experimental results show the feasibility and applicability of the proposed SSC prediction method for potatoes with surface impurities using near-infrared spectroscopy. • Non-destructive detection of uncleaned potatoes (with surface impurities). • Good PLSR prediction model of potato SSC based on surface impurities. • Processing spectral data with multiple preprocessing algorithms to eliminate noise. • Proposed optimised variable selection algorithms for effective wavelength selection. • Application of VCPA-IRIV algorithm.

Study on starch content detection and visualization of potato based on hyperspectral imaging.

Starch is an important quality index in potato, which contributes greatly to the taste and nutritional quality of potato. At present, the determination of starch depends on chemical analysis, which is time consuming and laborious. Thus, rapid and accurate detection of the starch content of potatoes is important. This study combined hyperspectral imaging with chemometrics to predict potato starch content. Two varieties of Kexin No.1 and Holland No.15 potatoes were used as experimental samples. Hyperspectral data were collected from three sampling sites (the top, umbilicus, and middle regions). Standard normal variate (SNV) was used for spectral preprocessing, and three different methods of competitive adaptive reweighted sampling (CARS), iterative variable subset optimization (IVSO), and the variable iterative space shrinkage approach (VISSA) were used for characteristic wavelength selection. Linear partial least‐squares regression (PLSR) and nonlinear support vector regression (SVR) models were then established. The results indicated that the sampling site has a considerable impact on the accuracy of the prediction model, and the umbilicus region with CARS‐SVR model gave best performance with correlation coefficients in calibration (Rc) of 0.9415, in prediction (Rp) of 0.9346, root mean square errors in calibration (RMSEC) of 15.9 g/kg, in prediction (RMSEP) of 17.4 g/kg, and residual predictive deviation (RPD) of 2.69. The starch content in potatoes was visualized using the best model in combination with pseudo‐color technology. Our research provides a method for the rapid and nondestructive determination of starch content in potatoes, providing a good foundation for potato quality monitoring and grading.

Threshold models improve estimates of molt parameters in datasets with small sample sizes

Abstract The timing of events in birds’ annual cycles is important to understanding life history evolution and response to global climate change. Molt timing is often measured as an index of the sum of grown feather proportion or mass within the primary flight feathers. The distribution of these molt data over time has proven difficult to model with standard linear models. The parameters of interest are at change points in model fit over time, and so least-squares regression models that assume molt is linear violate the assumption of even variance. This has led to the introduction of other nonparametric models to estimate molt parameters. Hinge models directly estimate changes in model fit and have been used in many systems to find change points in data distributions. Here, we apply a hinge model to molt timing, through the introduction of a double-hinge (DH) threshold model. We then examine its performance in comparison to current models using simulated and empirical data. Our results suggest that the Underhill–Zucchini (UZ) and Pimm models perform well under many circumstances and appear to outperform the DH model in datasets with high variance. The DH model outperforms the UZ model at low sample sizes of birds in active molt and shorter molt durations and provides more realistic confidence intervals at smaller sample sizes. The DH model provides a novel addition to the toolkit for estimating molt phenology, expanding the conditions under which molt can accurately be estimated.

The impact of firm size on the performance of Vietnamese private enterprises: A case study

This article investigates the effect of firm size on the performance of Vietnamese private enterprises. Based on the data from the Annual Enterprise Survey from 2009 to 2018, this study uses an ordinary least-squares regression model (OLS) to point out the effects of firm size (growth rate, total assets, and total labor) on the performance of Vietnamese private enterprises in both static and dynamic states. According to the results of the quantitative model, total assets are the biggest factor for determining firm performance, followed by total labor and growth rate. The results highlight the issue in Vietnamese private enterprises development in terms of scale, despite the fact that their number is growing, as the scale of enterprises decreases (the proportion of micro and small enterprises increases, but the proportion of medium and big enterprises decreases). Besides, the disadvantages of scale also negatively affect the development process of Vietnamese private enterprises, including accessing capital, increase in production or productivity, business expansion, and improving competitiveness. AcknowledgmentsThis research is supported by the National Science Project “Development of Private Enterprises in the Southwest Region in the new context” (KHCN-TNB/14-19/X15).

GenBio-MAPS as a Case Study to Understand and Address the Effects of Test-Taking Motivation in Low-Stakes Program Assessments.

The General Biology–Measuring Achievement and Progression in Science (GenBio-MAPS) assessment measures student understanding of the Vision and Change core concepts at the beginning, middle, and end of undergraduate biology degree programs. Assessment coordinators typically administer this instrument as a low-stakes assignment for which students receive participation credit. While these conditions can elicit high participation rates, it remains unclear how to best measure and account for potential variation in the amount of effort students give to the assessment. To better understand student test-taking motivation, we analyzed GenBio-MAPS data from more than 8000 students at 20 institutions. While the majority of students give acceptable effort, some students exhibited behaviors associated with low motivation, such as low self-reported effort, short test completion time, and high levels of rapid-selection behavior on test questions. Standard least-squares regression models revealed that students’ self-reported effort predicts their observable time-based behaviors and that these motivation indices predict students’ GenBio-MAPS scores. Furthermore, we observed that test-taking behaviors and performance change as students progress through the assessment. We provide recommendations for identifying and filtering out data from students with low test-taking motivation so that the filtered data set better represents student understanding.

Error Bound of Mode-Based Additive Models.

Due to their flexibility and interpretability, additive models are powerful tools for high-dimensional mean regression and variable selection. However, the least-squares loss-based mean regression models suffer from sensitivity to non-Gaussian noises, and there is also a need to improve the model’s robustness. This paper considers the estimation and variable selection via modal regression in reproducing kernel Hilbert spaces (RKHSs). Based on the mode-induced metric and two-fold Lasso-type regularizer, we proposed a sparse modal regression algorithm and gave the excess generalization error. The experimental results demonstrated the effectiveness of the proposed model.

Geographical disparities of incidence and prevalence of triple-negative breast cancers: Multistate study data analysis.

e12599 Background: Higher prevalence of triple negative breast cancer (TNBC) in black women with associated poor outcomes due to various disparities is well documented within a single state. We examine multiple states to better understand the state effect on such differences in incidence and prevalence of TNBC in black women. Methods: Female patients of ages 19 years old and above with breast cancer from the Surveillance, Epidemiology and End Results (SEER) Program across 13 states (608 counties) from 2015 (n = 66,444) and 2016 (n = 66,122) were examined. The relationships between the proportion of black and white women and the rate of patients with different tumor subtypes (luminal A, luminal B, HR-HER2+, and triple negative) were examined at the county level using ordinary least-square regression models. In parallel, due to consideration of various state-specific healthcare policies, socio-cultural norms, and socio-economic disparities, multi-level regression models were applied to examine the nested, random effect of each state on TNBC prevalence in each county. Bonferroni correction was applied to reduce the Type I error caused by repeated use of the same variables in multiple tests. Results: The baseline breast cancer rates between black and white women were similar in the population (0.171% for black and 0.168% for white). Consistent to previous studies, we demonstrate a significant positive correlation (p &lt; 0.001) in TNBC in black females in both years. Surprisingly, when accounted for the random effects on states, 38.2% (2015) and 34.3% (2016) increase in incidence of TNBC in black females were seen, suggestive of state-specific disparity affecting race-specific health. In 2015, other subtypes of breast cancer in both black and white females did not result in significant relationship. Interestingly, in 2016, there was a significant relationship seen between the TNBC rate in white females and the white female population rate only after adjusting for the state effect (p = 0.026). This indicates the impact of non-biological factors such as state-wide health policies. Additionally, HR-HER2+ black females had a significant relationship against respective population rate only after adjusting for the state effect as well (p = 0.0394). For luminal A white females, a 15% decrease in incidence was seen after adjusting for state effect (p = 0.0424). Conclusions: This is the first known across-state examination of breast cancer subtypes by race with random effects on state. This study shows the role of state-specific factors affecting incidence in black and white females and potentially indicates the importance of state-level management for breast cancer on health disparities in addition to race-driven effects. Further studies are needed to elucidate comparable differences between states affecting the rates of various subtypes of breast cancer and thus health outcomes.

Association between serum level of C-reactive protein and risk of cardiovascular events based on cohort studies

Although the association between serum level of C-reactive protein (CRP) and risk of cardiovascular events (CVEs) has been reported, the comprehensive assessment of the quantitative association of CRP level with risk of CVEs has not been reported. Our meta-analysis aims to quantitatively evaluate the association of CRP level and risk of CVEs. We searched PubMed and Embase databases for articles published up to December 6, 2019. Studies with data on men and women, different types of CVEs and multiple cohorts within a study were treated as independent studies. Generalized least-squares regression models were used to assess the quantitative association between CRP level and risk of CVEs. Restricted cubic splines were used to model the possible linear association between CRP and CVEs. We included 36 articles (60 studies; 227,715 participants) in the analysis. The pooled relative risks (RRs) of high versus low CRP level for cardiovascular disease (CVD), stroke and coronary heart disease (CHD) were 1.64 (95% confidence interval [CI], 1.49-1.82), 1.46 (95% CI, 1.35-1.58), and 1.55 (95% CI, 1.47-1.63), respectively. A linear association was found between CRP level and CVD (P = 0.429), stroke (P = 0.940), and CHD (P = 0.931); with each 1-mg/L increase in CRP level, the pooled RRs for CVD, stroke, and CHD were 1.18 (95% CI, 1.12-1.24), 1.07 (95% CI, 1.04-1.09), and 1.12 (95% CI, 1.08-1.16), respectively. This meta-analysis suggests that risk of CVEs increases with increasing serum CRP level.

Source:sink imbalance detected with leaf- and canopy-level spectroscopy in a field-grown crop.

The finely tuned balance between sources and sinks determines plant resource partitioning and regulates growth and development. Understanding and measuring metabolic indicators of source or sink limitation forms a vital part of global efforts to increase crop yield for future food security. We measured metabolic profiles of Cucurbita pepo (zucchini) grown in the field under carbon sink limitation and control conditions. We demonstrate that these profiles can be measured non-destructively using hyperspectral reflectance at both leaf and canopy scales. Total non-structural carbohydrates (TNC) increased 82% in sink-limited plants; leaf mass per unit area (LMA) increased 38% and free amino acids increased 22%. Partial least-squares regression (PLSR) models link these measured functional traits with reflectance data, enabling high-throughput estimation of traits comprising the sink limitation response. Leaf- and canopy-scale models for TNC had R2 values of 0.93 and 0.64 and %RMSE of 13 and 38%, respectively. For LMA, R2 values were 0.91 and 0.60 and %RMSE 7 and 14%; for free amino acids, R2 was 0.53 and 0.21 with %RMSE 20 and 26%. Remote sensing can enable accurate, rapid detection of sink limitation in the field at the leaf and canopy scale, greatly expanding our ability to understand and measure metabolic responses to stress.

Near-infrared spectroscopy: Alternative method for assessment of stable carbon isotopes in various soil profiles in Chile

The role of soil in the global carbon cycle and carbon–climate feedback mechanisms has attracted considerable interest in recent decades. Consequently, development of simple, rapid, and inexpensive methods to support the studies on carbon dynamics in soil is of interest. Near-infrared spectroscopy (NIRS) has emerged as a rapid and cost-effective method for measurements of soil properties. The aim of this study was to develop and validate a predictive model for δ13C values using NIRS in various soil profiles across Chile. Eleven sites were selected in the range of 30° to 50° S. These sites represent different soil moisture and soil temperature regimes, clay mineralogies, parent materials, and climates; in addition, they have prairie vegetation and contain C3-type vegetation. Air-dried soil samples were scanned in the NIR range at a resolution of 4 cm−1. The carbon isotopic composition, expressed as δ13C relative to the Vienna Pee Dee Belemnite standard, was analysed using an elemental analyser–isotope ratio mass spectrometer system. A prediction model for δ13C values based on NIRS data was developed through a partial least-squares regression (PLS) model using ten latent variables. A second model was generated using a random forest (RF) approach. The model performances were acceptable. The RF model provided the best results. The values of the root mean square error of prediction for the validation runs for δ13C obtained using the PLS and RF models were 1.38‰, and 1.15‰, respectively. These model performances indicate that NIRS can be used to predict δ13C for the selected dataset. The results of this study support the use of NIRS as a predictive method in soil analyses and as a nondestructive waste-free method for studies on carbon dynamics in soil.

Data-driven prognostic scheme for rolling-element bearings using a new health index and variants of least-square support vector machines

This paper presents a data-driven prognostic framework for rolling-element bearings (REBs). This framework infers a bearing’s health index by defining a degree-of-defectiveness (DD) metric in the frequency domain of bearing raw signal, named DD-based health index (DDHI). Then, we systematically apply least-square support vector machines (LSSVMs) in the forms of Bayesian inference-aided one-class LSSVM (Bayesian-OCLSSVM) for anomaly detection in order to define the time-to-start (TTS) point of RUL prediction and the recurrent least-square support vector regression (Recurrent-LSSVR) model for predicting future values of DDHI for calculating the RUL. In addition, this paper addresses several pertinent challenges, such as failure threshold determination during anomaly detection and RUL estimation, by developing adaptive thresholds. We conduct extensive experiments on publicly available two benchmark datasets using a run-to-failure experiment. The results demonstrate the efficacy of the proposed framework compared to state-of-the-art methods in terms of the accuracy and convergence of the RUL estimation of bearings.