Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

Cross-Sectional but Not Prospective Association of Accelerometry-Derived Physical Activity With Quality of Life in Children and Adolescents

Objectives: This study aims to quantify the cross-sectional and prospective associations between quality of life (QoL) and moderate-to-vigorous physical activity (MVPA). Methods: This study was based on the Swiss children's Objectively measured PHYsical Activity cohort. The primary endpoint is the overall QoL score and its six dimensions. The main predictor is the average time spent in MVPA per day. Linear mixed effects and linear regression models respectively were used to investigate the cross-sectional and prospective associations between MVPA and QoL. Results: There were 352 participants in the study with complete data from baseline (2013-2015) and follow-up (2019). MVPA was positively associated with overall QoL and physical wellbeing (p = 0.023 and 0.002 respectively). The between-subject MVPA was positively associated with the overall QoL, physical wellbeing, and social wellbeing (p = 0.030, 0.017, and 0.028 respectively). Within-subject MVPA was positively associated with physical wellbeing and functioning at school (p = 0.039 and 0.013 respectively). Baseline MVPA was not associated with QoL 5 years later. Conclusion: Future longitudinal studies should employ shorter follow-up times and repeat measurements to assess the PA and QoL association.

Association of intestinal bacteria with immune activation in a cohort of healthy adults

IMPORTANCE

Chronic inflammation may develop over time in healthy adults as a result of a variety of factors, such as poor diet directly affecting the composition of the intestinal microbiome, or by causing obesity, which may also affect the intestinal microbiome. These effects may trigger the activation of an immune response that could eventually lead to an inflammation-related disease, such as colon cancer. Before disease develops it may be possible to identify subclinical inflammation or immune activation attributable to specific intestinal bacteria normally found in the gut that could result in future adverse health impacts. In the present study, we examined a group of healthy men and women across a wide age range with and without obesity to determine which bacteria were associated with particular types of immune activation to identify potential preclinical markers of inflammatory disease risk. Several associations were found that may help develop dietary interventions to lower disease risk.

On-Site Sensor Calibration Procedure for Quality Assurance of Barometric Process Separation (BaPS) Measurements

Barometric process separation (BaPS) is an automated laboratory system for the simultaneous measurement of microbial respiration and gross nitrification rates in soil samples. To ensure optimal functioning, the sensor system, consisting of a pressure sensor, an O₂ sensor, a CO₂ concentration sensor, and two temperature probes, must be accurately calibrated. For the regular on-site quality control of the sensors, we developed easy, inexpensive, and flexible calibration procedures. The pressure sensor was calibrated by means of a differential manometer. The O₂ and CO₂ sensors were simultaneously calibrated through their exposure to a sequence of O₂ and CO₂ concentrations obtained by sequentially exchanging O₂/N₂ and CO₂/N₂ calibration gases. Linear regression models were best suited for describing the recorded calibration data. The accuracy of O₂ and CO₂ calibration was mainly affected by the accuracy of the utilized gas mixtures. Because the applied measuring method is based on the O₂ conductivity of ZrO₂, the O₂ sensor is particularly susceptible to aging and to consequent signal shifts. Sensor signals were characterized by high temporal stability over the years. Deviations in the calibration parameters affected the measured gross nitrification rate by up to 12.5% and affected the respiration rate by up to 5%. Overall, the proposed calibration procedures are valuable tools for ensuring the quality of BaPS measurements and for promptly identifying sensor malfunctions.

Investigation of heteroscedasticity in polygenic risk scores across 15 quantitative traits

The polygenic risk score (PRS) could be used to stratify individuals with high risk of diseases and predict complex trait of individual in a population. Previous studies developed a PRS-based prediction model using linear regression and evaluated the predictive performance of the model using the R ² value. One of the key assumptions of linear regression is that the variance of the residual should be constant at each level of the predictor variables, called homoscedasticity. However, some studies show that PRS models exhibit heteroscedasticity between PRS and traits. This study analyzes whether heteroscedasticity exists in PRS models of diverse disease-related traits and, if any, it affects the accuracy of PRS-based prediction in 354,761 Europeans from the UK Biobank. We constructed PRSs for 15 quantitative traits using LDpred2 and estimated the existence of heteroscedasticity between PRSs and 15 traits using three different tests of the Breusch-Pagan (BP) test, score test, and F test. Thirteen out of fifteen traits show significant heteroscedasticity. Further replication using new PRSs from the PGS catalog and independent samples (N = 23,620) from the UK Biobank confirmed the heteroscedasticity in ten traits. As a result, ten out of fifteen quantitative traits show statistically significant heteroscedasticity between the PRS and each trait. There was a greater variance of residuals as PRS increased, and the prediction accuracy at each level of PRS tended to decrease as the variance of residuals increased. In conclusion, heteroscedasticity was frequently observed in the PRS-based prediction models of quantitative traits, and the accuracy of the predictive model may differ according to PRS values. Therefore, prediction models using the PRS should be constructed by considering heteroscedasticity.

The Impact of Meteorological Parameters and Seasonal Changes on Reporting Patients with Selected Cardiovascular Diseases to Hospital Emergency Departments: A Pilot Study

(1) Background: So far, research results have confirmed the relationship between heat and cold stress, the fluctuations in atmospheric pressure and high relative humidity, and the vulnerability of patients with so-called "weather-dependent" diseases which could lead to death. This study aimed to determine the meteorological parameters, their interactions, and the seasonal changes of the most significant factors in predicting the number of patients reporting to the Emergency Departments (EDs) in Poznań (Poland) during 2019. (2) Methods: The analysis included the meteorological parameters and data of 3606 patients diagnosed with essential or complicated arterial hypertension, myocardial infarction, chronic ischemic heart disease, and ischemic or unspecified stroke by the International Classification of Diseases (ICD-10). The meteorological data (days per week and seasonal data) were used to build a linear regression model to assess the changes in the daily number of reporting patients. The input data for the final model were selected based on the principal component analysis (PCA), and built for each delay and acceleration (reporting up to 3 days before the change or up to 3 days after the change of the meteorological parameter). (3) Results: A significantly lower number of reports was observed during weekends compared to working days (standardised b = -0.254, p-value < 0.0001) and three days before the maximum daily air temperature in the spring and summer period (standardised b = -0.748, p-value < 0.0001), while two days after the increase in the daily amplitude of atmospheric pressure (standardised b = 0.116, p-value = 0.0267), and also on the day of occurrence of the unfavourable interdiurnal air temperature change, an increase in the number of patients was noted (standardised b = 0.115, p-value = 0.0186). The changes in the last two parameters were statistically insignificant. Based on the obtained results, the negative impact of the changes in the meteorological conditions on the number of reports to the EDs in Poznań was determined.

Photoluminescent Sensor of Scarification Efficiency of Fodder Plants' Seeds

Optoelectronic sensors open up new possibilities for predicting the yield for their possible correction, including increasing the seed germination of forage plants. The luminescent properties of unscarified and scarified seeds of various germination galega, clover and alfalfa are compared. The dependence of germination on the photoluminescence flux is approximated by linear equations with a determination coefficient R² = 0.932-0.999. A technological process for analyzing the scarification quality of forage seed plants is proposed, including sample preparation, photoluminescence excitation and registration, amplification of the received electrical signal and determination of germination based on calibration equations. This is followed by a decision on sowing, or re-scarification. The scheme of the scarification quality control device has been developed for which the LED, as well as the radiation receiver and other elements, has been selected according to the energy efficiency criterion. Mechanical scarification of the forage plants' seed surfaces has a significant effect on their photoluminescent properties. The flux increases by 1.5-1.7 times for galega, 2.0-3.0 times for clover and 2.3-3.9 times for alfalfa. Linear approximation of the flux dependence on germination with a high coefficient of determination allows us to obtain reliable linear calibration equations. Preliminary mock-up laboratory tests allow us to talk about the developed method's effectiveness and device.

Association between hemoglobin and chronic kidney disease progression: a secondary analysis of a prospective cohort study in Japanese patients

OBJECTIVE

Anemia has been reported as a risk factor for chronic kidney disease (CKD) progression. However, there are still few studies examining the relationship between specific hemoglobin (Hb) levels and renal prognosis and renal function decline simultaneously. Meanwhile, the possible non-linear relationship between Hb and CKD progression also deserves further exploration. On that account, our primary goal is to explore the link of Hb on renal prognosis and renal function decline in patients with CKD.

METHODS

This study was a secondary analysis of a prospective cohort study, which consecutively and non-selectively collected 962 participants from the research of CKD-ROUTE in Japan from November 2010 to December 2011. We used the Cox proportional-hazards and linear regression models to evaluate the independent association between baseline Hb and renal prognosis (renal composite endpoint, initiation of dialysis during follow-up or 50% decline in eGFR from baseline) and renal function decline(annual eGFR decline), respectively. A multivariate Cox proportional hazards regression analysis with cubic spline functions model and smooth curve fitting (penalized spline method) were conducted to address Hb and CKD prognosis's non-linearity. At the same time, a generalized additive model (GAM) and smooth curve fitting (penalized spline method) was conducted to explore the exact shape of the curve between Hb and renal function decline. Additionally, we did a series of sensitivity analyses to ensure the robustness of the results. Moreover, we conducted subgroup analyses.

RESULTS

The mean age of the included patients was 67.35 ± 13.56 years old, and 69.65% were male. The mean baseline Hb and estimated glomerular filtration rate (eGFR) was 12.06 ± 2.21 g/dL and 33.04 ± 18.01 ml/min per 1.73 m². The annual decline in eGFR was 2.09 mL/min/1.73 m²/year. During a median follow-up time of 33.5 months, 252(26.2%) people experienced renal composite endpoint. After adjusting covariates, the results showed that Hb was negatively associated with renal composite endpoint (HR = 0.836, 95%CI: 0.770, 0.907) and renal function decline (β = -0.436, 95%CI: -0.778, -0.093). There was also a non-linear relationship between Hb and renal composite endpoint, and the inflection point of Hb was 8.6 g/dL. The effect sizes(HR) on the left and right sides of the inflection point were 1.257 (0.841, 1.878) and 0.789 (0.715, 0.870), respectively. And the sensitive analysis demonstrated the robustness of the results. Subgroup analysis showed that Hb was more strongly associated with the renal composite endpoint in non-hypertensive, SBP < 140 mmHg, urine protein-to-creatinine ratio (UPCR) < 0.5 g/gCr, and diuretic use patients. In contrast, the weaker association was probed in hypertensive and non-diuretic use patients and the patients with SBP ≥ 140 mmHg, and UPCR ≥ 0.5 g/gCr.

CONCLUSION

This study demonstrates a negative and non-linear relationship between Hb and renal prognosis and renal function decline in Japanese CKD patients. Hb is strongly related to renal prognosis when Hb is above 8.6 g/dL.

Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research

In basic neuroscience research, data are often clustered or collected with repeated measures, hence correlated. The most widely used methods such as t test and ANOVA do not take data dependence into account and thus are often misused. This Primer introduces linear and generalized mixed-effects models that consider data dependence and provides clear instruction on how to recognize when they are needed and how to apply them. The appropriate use of mixed-effects models will help researchers improve their experimental design and will lead to data analyses with greater validity and higher reproducibility of the experimental findings.

What factors drive polyethylene wear in total knee arthroplasty? : results of a large retrieval series

AIMS

Wear of the polyethylene (PE) tibial insert of total knee arthroplasty (TKA) increases the risk of revision surgery with a significant cost burden on the healthcare system. This study quantifies wear performance of tibial inserts in a large and diverse series of retrieved TKAs to evaluate the effect of factors related to the patient, knee design, and bearing material on tibial insert wear performance.

METHODS

An institutional review board-approved retrieval archive was surveyed for modular PE tibial inserts over a range of in vivo duration (mean 58 months (0 to 290)). Five knee designs, totalling 1,585 devices, were studied. Insert wear was estimated from measured thickness change using a previously published method. Linear regression statistical analyses were used to test association of 12 patient and implant design variables with calculated wear rate.

RESULTS

Five patient-specific variables and seven implant-specific variables were evaluated for significant association with lower insert wear rate. Six were significant when controlling for other factors: greater patient age, female sex, shorter duration in vivo, polished tray, highly cross-linked PE (HXLPE), and constrained knee design.

CONCLUSION

This study confirmed that knee wear rate increased with duration in vivo. Older patients and females had significantly lower wear rates. Polished modular tibial tray surfaces, HXLPE, and constrained TKA designs were device design factors associated with significantly reduced wear rate. Cite this article: Bone Joint J 2021;103-B(11):1695-1701.

Establishing growth charts for proposed body shape and size index of the Pakistani population, using quantile regression approach

Background:

Obesity leads to other fatal diseases like diabetes, cardiovascular diseases, depression, and some forms of cancer. Still, the well-known tool to measure obesity is the body mass index. But it usually failed in the measurement of adipose tissues. So, we present a novel anthropometric measure, called body shape and size index which is developed by the combination of major anthropometric determinants: body surface area, body mass index, weight, and height.

Methods:

This study is based on cross-sectional data consisting of 7224 individuals that were taken from the city Multan, Punjab, Pakistan. All the individuals, both males, and females, of age 2 years and above were included in the study except the pregnant women. The variables included in this study are gender, area (urban and rural), age (years), weight (kg), and height (meters). Growth charts of quantile regression are used for the inferential analysis of data. Comparison of proposed body shape and size index at different obesity levels has also been made to access the relationship of proposed body shape and size index with obesity.

Results:

The results show that the proposed body shape and size index has a great association with body surface area, body mass index, weight, height, and age. The proposed body shape and size index has a high negative association with body surface area, moderate negative association with body mass index and weight, and low negative association with height and age. According to growth charts of body shape and size index, after the age of 25 years, body shape and size index curves go upward while it smoothly goes downward at the age of 50 years but decreases in earlier ages. Body shape and size index showed a significant association with body shape and body size (body development) at the same time.

Conclusion:

Body shape and size index is found, generally linear with age, and increased with decreasing body mass index and body surface area. The proposed index has an indirect relationship with obesity. Body shape and size index with low values indicates a high risk of obesity. While, however, body shape and size index with high values indicates a low risk of obesity. Applications of the proposed body shape and size index are also presented in statistical modeling.

Genomic breed composition of Ningxiang pig via different SNP panels

The genomic breed composition (GBC) reflects the genetic relationship between individual animal and ancestor breeds in composite or hybrid breeds. Also, it can estimate the genomic contribution of each breed (ancestor) to the genome of each individual animal. Using genomic SNP information to estimate Ningxiang pig GBC is of great significance. First of all, GBC was widely used in cattle and had significant effects, but there is almost no using experience in Chinese endemic pig breeds. Importantly, High-density SNPs are expensive but can be economized by deploying a relatively small number of highly informative SNP scattered evenly across the genome. Moreover, the impact of low-density SNPs selection strategy on estimating the GBC of individual animals has not been fully explained. Using SNP data from different databases and organizations, we established reference (N = 2015) and verification (N = 302) data sets. Twelve successively smaller SNP panels (500, 1K, 5K, 10K) were built from those SNP in the reference data by three selection methods (uniform, maximized the Euclidean distance (MED) and random distribution method). For each panel, the GBC of Ningxiang pigs in the reference dataset was estimated. Then combining Shannon entropy and the GBC results, the optimal panel (the 10K SNP panel constructed by MED method) was picked out to estimate the GBC of verification Ningxiang pig, which detected that 230 individuals were purebred Ningxiang pigs and the remaining 72 impure individuals contained 6.44% blood related with Rongchang pigs and 4.09% with Bamaxiang pigs in the verification Ningxiang population. Finally, the genetic structure analysis of verification population was performed combining with the results of GBC, multi-dimensional scaling (MDS) analysis and hierarchical cluster analysis. These results showed: (a) GBC could accurately identify purebred Ningxiang pigs and, scientifically, calculate the genomic contribution of each breed of each hybrid animal. (b) GBC could carry out population genetic structure and understand the genetic background of Ningxiang pigs. Such findings highlight a variety of opportunities to better protect and identify other endangered local breeds in China facing the same situation as Ningxiang pig and provide more accurate, economical and efficient new technical support in GBC estimation breeding work.

Chemical Oxygen Demand Can Be Converted to Gross Energy for Food Items Using a Linear Regression Model

BACKGROUND

Human and microbial metabolism are distinct disciplines. Terminology, metrics, and methodologies have been developed separately. Therefore, combining the 2 fields to study energetic processes simultaneously is difficult.

OBJECTIVES

When developing a mechanistic framework describing gut microbiome and human metabolism interactions, energy values of food and digestive materials that use consistent and compatible metrics are required. As an initial step toward this goal, we developed and validated a model to convert between chemical oxygen demand (COD) and gross energy (${E_g}$) for >100 food items and ingredients.

METHODS

We developed linear regression models to relate (and be able to convert between) theoretical gross energy (${E_g}^{\prime}$) and chemical oxygen demand (COD'); the latter is a measure of electron equivalents in the food's carbon. We developed an overall regression model for the food items as a whole and separate regression models for the carbohydrate, protein, and fat components. The models were validated using a sample set of computed ${E_g}^{\prime}$ and COD' values, an experimental sample set using measured ${E_g}$ and COD values, and robust statistical methods.

RESULTS

The overall linear regression model and the carbohydrate, protein, and fat regression models accurately converted between COD and ${E_g}$, and the component models had smaller error. Because the ratios of COD per gram dry weight were greatest for fats and smallest for carbohydrates, foods with a high fat content also had higher ${E_g}$ values in terms of kcal · g dry weight-1.

CONCLUSION

Our models make it possible to analyze human and microbial energetic processes in concert using a single unit of measure, which fills an important need in the food-nutrition-metabolism-microbiome field. In addition, measuring COD and using the regressions to calculate ${E_g}$ can be used instead of measuring ${E_g}$ directly using bomb calorimetry, which saves time and money.

Morphologic and radiologic parameters correlating to shoulder function at diagnosis for patients with rotator cuff tear

BACKGROUND

The magnetic resonance imaging (MRI) parameters used to diagnose rotator cuff tears are weakly correlated to shoulder function. Our hypothesis was that adding 3-dimensional morphologic parameters resulting from biplanar radiographs (3DXR parameters) to the MRI parameters would improve this correlation.

METHODS

We assessed 52 patients with rotator cuff tears with an EOS Imaging radiographic examination, MRI study, and clinical evaluation of the shoulder, as well as the Constant score. The bones of the 52 shoulders were reconstructed 3-dimensionally, and eleven 3DXR parameters were automatically extracted. First, the trueness and reliability of these parameters were evaluated. Then, bivariate correlations between each parameter and the Constant score were made. A linear regression model was subsequently built to correlate the 11 parameters and 5 MRI findings with shoulder function at diagnosis, as assessed by the Constant score.

RESULTS

The parameters showed good trueness and reliability of most 3DXR parameters. Supraspinatus tear extension, muscle atrophy, and the distance between the greater and deltoid tuberosities were the only parameters with a statistically significant correlation to a lower Constant score (P < .05) in the bivariate study. These correlations were either weak or negligible. A regression model was successfully built with one MRI parameter and four 3DXR parameters. Correlation to function increased from 16.7% to 43.3% with this model.

CONCLUSION

For patients with rotator cuff tears, the combination of MRI and 3DXR parameters of the shoulder in a linear regression model improves the correlation with the Constant score (shoulder function) at diagnosis.

Thorough evaluation of OECD principles in modelling of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine derivatives using QSARINS

The human immunodeficiency virus is a lethal pathology considered as a worldwide problem. The search for new strategies for the treatment of this disease continues to be a great challenge in the scientific community. In this study, a series of 107 derivatives of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine, previously evaluated experimentally against HIV-I reverse transcriptase, was used to model antiretroviral activity. A model of linear regression, implemented in the QSARINS software, was developed with a genetic algorithm for variable selection. The fit of its parameters was good and exhaustive validation, according to the OECD regulatory principles, was performed. Also, the applicability domain was established. In addition, its robustness (r ² = 0.84), stability (Q ² _LOO = 0.81; Q ² _LMO = 0.80) and good predictive power (r ² _EXT = 0.85) is proved. So, it was used to predict the antiretroviral activity of eight compounds obtained by rational drug design. Finally, it can be affirmed that the proposed tools allow the rapid and economic identification of potential antiretroviral drugs.

Psychosocial and Socio-Economic Crisis in Bangladesh Due to COVID-19 Pandemic: A Perception-Based Assessment

Background: The spread of the COVID-19 pandemic, the partial lockdown, the disease intensity, weak governance in the healthcare system, insufficient medical facilities, unawareness, and the sharing of misinformation in the mass media has led to people experiencing fear and anxiety. The present study intended to conduct a perception-based analysis to get an idea of people's psychosocial and socio-economic crisis, and the possible environmental crisis, amidst the COVID-19 pandemic in Bangladesh. Methods: A perception-based questionnaire was put online for Bangladeshi citizens of 18 years and/or older. The sample size was 1,066 respondents. Datasets were analyzed through a set of statistical techniques including principal component and hierarchical cluster analysis. Results: There was a positive significant association between fear of the COVID-19 outbreak with the struggling healthcare system (p < 0.05) of the country. Also, there was a negative association between the fragile health system of Bangladesh and the government's ability to deal with the pandemic (p < 0.05), revealing the poor governance in the healthcare system. A positive association of shutdown and social distancing with the fear of losing one's own or a family members' life, influenced by a lack of healthcare treatment (p < 0.05), reveals that, due to the decision of shutting down normal activities, people may be experiencing mental and economic stress. However, a positive association of the socio-economic impact of the shutdown with poor people's suffering, the price hike of basic essentials, the hindering of formal education (p < 0.05), and the possibility of a severe socio-economic and health crisis will be aggravated. Moreover, there is a possibility of a climate change-induced disaster and infectious diseases like dengue during/after the COVID-19 situation, which will create severe food insecurity (p < 0.01) and a further healthcare crisis. Conclusions: The partial lockdown in Bangladesh due to the COVID-19 pandemic increased community transmission and worsened the healthcare crisis, economic burden, and loss of GDP despite the resuming of industrial operations. In society, it has created psychosocial and socio-economic insecurity among people due to the loss of lives and livelihoods. The government should take proper inclusive steps for risk assessment, communications, and financial stimulus toward the public to alleviate their fear and anxiety, and to take proper action to boost mental health and well-being.

Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure

Due to the increased service life, environmental corrosion, unreasonable construction, and other issues, local defects inevitably exist in civil structures, which affect the structural performance and can lead to structural failure. However, research on grout defect identification of precast reinforced concrete frame structures with rebars spliced by sleeves faces great challenges owing to the complexity of the problem. This study presents a multiple-variable spatiotemporal regression model algorithm to identify local defects based on structural vibration responses collected using a sensor network. First, numerical simulations were carried out on precast beam–column connection models by comparing the identification results based on a single-variable regression model, two-variable spatial regression model, and two-variable spatiotemporal regression model; furthermore, a multiple-variable spatiotemporal regression model was proposed and robustness analysis of the damage indicator was carried out. Then, to explore the validity of the proposed method, a nondestructive vibration experiment was considered on a half-scaled, two-floor, precast concrete frame structure with column rebars spliced by defective grout sleeves. The results show that local defects were successfully identified based on a multiple-variable spatiotemporal regression model.

Quantitative evaluation of rejuvenation treatment of nasolabial fold wrinkles by regression model and 3D photography

OBJECTIVE

With the application of 3D photography, our study aimed to quantify parameters of static nasolabial fold wrinkles and establish mathematic regression model between parameters of wrinkles and age, further to quantitatively evaluate the effect of rejuvenation treatment in terms of age.

METHODS

From October 2016 to May 2018, 433 Chinese female volunteers, aged 25-60 years old, were enrolled in this study. Antera 3D camera was used to collect four parameters of static nasolabial fold wrinkles on the left and right sides of the volunteers, including overall size, average depth (mm), average width (mm), and maximum depth (mm). For those presented a linear relationship with age, univariate linear regression fitting was performed, followed by residual analysis, goodness of fit test, and significance test.

RESULTS

The results of univariate linear regression fitting showed there was a clear linear relationship between the maximum depth, average depth, overall size of nasolabial fold wrinkles and age, and the regression equations were established. The significance test of regression coefficients showed P values were less than .0001.

CONCLUSIONS

With application of the regression model between parameters of nasolabial fold wrinkles and age, the effect of rejuvenation treatment can be quantitatively evaluated in terms of age, which has certain reference and promotion value.

Hierarchical Variance Analysis: A Quantitative Approach for Relevant Factor Exploration and Confirmation of Perceived Tourism Impacts

The issue of tourism impacts is one that has plagued the tourism industry. This study develops a quantitative approach using hierarchical variance analysis, which deals with the exploration of the relevant factors and the confirmation of their significant contribution to analyze the residents’ perception of tourism impacts. Hierarchical variance analysis includes three mathematical procedures: Cronbach’s alpha tests, the exploration of relevant factors, and a hierarchical factor confirmation. Data are collected using a structured questionnaire completed by 452 surveyed residents living in Ly Son Island, Vietnam. The significant effects of socio-demographic variables on the overall impact assessment are observed. The bilateral and simultaneous relationships are analyzed using a one-factor ANOVA. A two-factor ANOVA shows the significant contribution of each socio-demographic variable on the economic, socio-cultural, and environmental impacts. Interaction between factors such as “Education level”, “Type of work”, etc. are hierarchically confirmed. The findings allow a better understanding of the residents’ perception of the effects of tourism on society, the economy, and the environment. This provides a scientific basis to help define problems and promote legal regulations for community participation in tourism planning in a small island destination.

Brine-Oil Interfacial Tension Modeling: Assessment of Machine Learning Techniques Combined with Molecular Dynamics

The physical chemistry mechanisms behind the oil-brine interface phenomena are not yet fully clarified. The knowledge of the relation between brine composition and concentration for a given oil may lead to the ionic tuning of the injected solution on geochemical and enhanced oil recovery processes. Thus, it is worth examining the parameters influencing the interfacial properties. In this context, we have combined machine learning (ML) techniques with classical molecular dynamics simulations (MD) to predict oil/brine interfacial tensions (IFT) effectively and compared this process to a linear regression (LR) method. To diversify our data set, we have introduced a new atomistic crude oil model (medium) with 36 different types of hydrocarbon molecules. The MD simulations were performed for mono- and multicomponent (toluene, heptane, Heptol, light, and medium) oil systems interfaced with sulfate and chloride brines with varying cations (Na⁺, K⁺, Ca²⁺, and Mg²⁺) and salinity concentration. Thus, a consistent IFT data set was built for the ML training and LR fitting at room temperature and pressure conditions, over the feature space considering oil density, oil composition, salinity, and ionic concentrations. On the basis of gradient boosted (GB) algorithms, we have observed that the dominant quantities affecting the IFT are related to the oil attributes and the salinity concentration, and no specific ion dominates the IFT changes. When the obtained LR model was validated against MD and experimental data from the literature, the error varied up to 2% and 9%, respectively, showing a robust and consistent transferability. The combination of MD simulations and ML techniques may provide a fast and cost-effective IFT determination over multiple and complex fluid-fluid and fluid-solid interfaces.

Psychosocial and Socio-Economic Crisis in Bangladesh Due to COVID-19 Pandemic: A Perception-Based Assessment

Background: The spread of the COVID-19 pandemic, the partial lockdown, the disease intensity, weak governance in the healthcare system, insufficient medical facilities, unawareness, and the sharing of misinformation in the mass media has led to people experiencing fear and anxiety. The present study intended to conduct a perception-based analysis to get an idea of people's psychosocial and socio-economic crisis, and the possible environmental crisis, amidst the COVID-19 pandemic in Bangladesh. Methods: A perception-based questionnaire was put online for Bangladeshi citizens of 18 years and/or older. The sample size was 1,066 respondents. Datasets were analyzed through a set of statistical techniques including principal component and hierarchical cluster analysis. Results: There was a positive significant association between fear of the COVID-19 outbreak with the struggling healthcare system (p < 0.05) of the country. Also, there was a negative association between the fragile health system of Bangladesh and the government's ability to deal with the pandemic (p < 0.05), revealing the poor governance in the healthcare system. A positive association of shutdown and social distancing with the fear of losing one's own or a family members' life, influenced by a lack of healthcare treatment (p < 0.05), reveals that, due to the decision of shutting down normal activities, people may be experiencing mental and economic stress. However, a positive association of the socio-economic impact of the shutdown with poor people's suffering, the price hike of basic essentials, the hindering of formal education (p < 0.05), and the possibility of a severe socio-economic and health crisis will be aggravated. Moreover, there is a possibility of a climate change-induced disaster and infectious diseases like dengue during/after the COVID-19 situation, which will create severe food insecurity (p < 0.01) and a further healthcare crisis. Conclusions: The partial lockdown in Bangladesh due to the COVID-19 pandemic increased community transmission and worsened the healthcare crisis, economic burden, and loss of GDP despite the resuming of industrial operations. In society, it has created psychosocial and socio-economic insecurity among people due to the loss of lives and livelihoods. The government should take proper inclusive steps for risk assessment, communications, and financial stimulus toward the public to alleviate their fear and anxiety, and to take proper action to boost mental health and well-being.

[Study on quality of standard decoction of Coptidis Rhizoma based on traditional decoction process]

In order to determine the quality evaluation method for standard decoction of Coptidis Rhizoma,15 batches of standard decoction of Coptidis Rhizoma were prepared by using standardized process. Parameters such as traits,p H value,indicative component content,fingerprint similarity,composition transfer rate and dry extract rate were selected as the indexes for quality evaluation. Similarity evaluation and cluster analysis were performed for HPLC fingerprint of standard decoction,and mathematical model was used to study the correlation between dry extract rate,berberine content,berberine transfer rate in standard decoction and berberine content in decoction pieces. The results showed that the similarity of fingerprints was greater than 0. 99 for these 15 batches of standard decoctions of Coptidis Rhizoma. In cluster analysis,the standard decoctions of Coptidis Rhizoma from 4 producing areas were classified into 3 categories,consistent with the content determination results,indicating that there were quality differences among different producing areas.R2 in three linear regression mathematical models established was all greater than 0. 9,with significant difference. The validation of three batches of data showed that the models had good accuracy. Therefore,this model can be used to predict the quality of standard decoction prepared from different Coptidis Rhizoma pieces. In the standard decoction process established in this study,the integrity of the traditional process was greatly preserved,and the established quality evaluation method could be used to comprehensively examine the quality of the standard decoction,which can provide a demonstration for the related research of water extraction preparation containing Coptidis Rhizoma pieces.

Development of QSAAR and QAAR models for predicting fish early-life stage toxicity with a focus on industrial chemicals

We developed models for predicting fish early-life stage (ELS) toxicities oriented to industrial chemicals. The training set was constructed without data from the Office of Pesticide Programs Pesticide Ecotoxicity Database, the main source for the pesticide-biased training set used in our previous work (SAR QSAR Environ. Res. 29:9, 725-742). In addition to the descriptors from the previous study, we also used water solubility to develop the new models, which were evaluated against the test set used in our previous study so that we could focus on the effects of the different training set and the additional descriptor. The statistics for the new models were hardly better than those for the previous models, which suggests, contrary to our expectations, that pesticide-biased data can successfully be used to develop models for predicting the fish ELS toxicities oriented to industrial chemicals. Acute Daphnia magna toxicity was important for the predictive QSAARs in both studies. A distance-based method for defining the applicability domains indicated that water solubility was a key indicator for detecting underestimated chemicals. The comparison of fish ELS toxicities for chemicals presented in different literatures revealed the uncertainty of the experimental data, which may lead to the low predictivity.

Fibrinogen measurement in liver disease: validation of the functional fibrinogen thromboelastography assay and a novel mathematical predictive model

BACKGROUND

Fibrinogen is produced in the liver and tends to be reduced in liver cirrhosis. Quantitative and qualitative tests exist to measure fibrinogen. We aimed to validate the functional fibrinogen thromboelastography assay (FF-TEG) and propose a new model to estimate fibrinogen levels via the Clauss method (Clauss) using data from a prothrombin time-derived fibrinogen assay (PT-Fg) in patients with liver cirrhosis.

MATERIALS AND METHODS

Clauss, PT-Fg, fibrinogen antigen (Fib-Ag) and FF-TEG were studied in 55 patients with liver cirrhosis (26 with Child-Turcotte-Pugh [CTP]-A disease, 14 with CTP-B and 15 CTP-C) and 20 healthy individuals.

RESULTS

The results of all four assays correlated strongly with each other, but gave significantly different mean levels in all cohorts. PT-Fg gave the highest levels whereas the Clauss gave the lowest levels. The FF-TEG performed well with results which were in between the Clauss and the PT-Fg. Significant differences were only observed between CTP-A and CTP-C for the Clauss, PT-Fg and Fib-Ag but not functional fibrinogen level. We devised a simple linear regression model in order to estimate Clauss from the PT-Fg.

DISCUSSION

The results of the FF-TEG correlate well with those of routine fibrinogen assays in patients with liver cirrhosis. However, the FF-TEG assay does not discriminate between early and late stages of disease, pointing to a preserved fibrin clot strength in cirrhosis. Through linear regression models, fibrinogen levels can be accurately estimated using the Clauss method based on fibrinogen levels obtained in the cheaper PT-Fg.

Robust regression: Testing global hypotheses about the slopes when there is multicollinearity or heteroscedasticity

A well-known concern regarding the usual linear regression model is multicollinearity. As the strength of the association among the independent variables increases, the squared standard error of regression estimators tends to increase, which can seriously impact power. This paper examines heteroscedastic methods for dealing with this issue when testing the hypothesis that all of the slope parameters are equal to zero via a robust ridge estimator that guards against outliers among the dependent variable. Included are results related to leverage points, meaning outliers among the independent variables. In various situations, the proposed method increases power substantially.

Paced Breathing Increases the Redundancy of Cardiorespiratory Control in Healthy Individuals and Chronic Heart Failure Patients

Synergy and redundancy are concepts that suggest, respectively, adaptability and fault tolerance of systems with complex behavior. This study computes redundancy/synergy in bivariate systems formed by a target X and a driver Y according to the predictive information decomposition approach and partial information decomposition framework based on the minimal mutual information principle. The two approaches assess the redundancy/synergy of past of X and Y in reducing the uncertainty of the current state of X. The methods were applied to evaluate the interactions between heart and respiration in healthy young subjects (n = 19) during controlled breathing at 10, 15 and 20 breaths/minute and in two groups of chronic heart failure patients during paced respiration at 6 (n = 9) and 15 (n = 20) breaths/minutes from spontaneous beat-to-beat fluctuations of heart period and respiratory signal. Both methods suggested that slowing respiratory rate below the spontaneous frequency increases redundancy of cardiorespiratory control in both healthy and pathological groups, thus possibly improving fault tolerance of the cardiorespiratory control. The two methods provide markers complementary to respiratory sinus arrhythmia and the strength of the linear coupling between heart period variability and respiration in describing the physiology of the cardiorespiratory reflex suitable to be exploited in various pathophysiological settings.

Detection of ethyl carbamate in liquors using surface-enhanced Raman spectroscopy

Ethyl carbamate (EC), a potential carcinogen, can be formed during the fermentation and storage of alcoholic beverages. In this work, quantitative detection of EC in alcoholic beverages by using surface-enhanced Raman spectroscopy (SERS) is reported. Flower-shaped silver nanostructure substrates and silver nanocube substrates were prepared and employed as SERS platform. Flower-like silver substrates had better Raman enhancement effect on EC and were selected for further EC detection. In EC SERS spectra based on flower-shaped silver substrates, the strongest and reproducible characteristic band at 857 cm^-1 was chosen for establishing a linear regression model in the concentrations ranging from 10^-5 to 10^-9 M, which effectively extended the application scope of the quantitative model for determination EC. Furthermore, a real alcoholic beverage was tested to verify the feasibility and reliability of the method.

Google timeline accuracy assessment and error prediction

Google Location Timeline, once activated, allows to track devices and save their locations. This feature might be useful in the future as available data for evidence in investigations. For that, the court would be interested in the reliability of this data. The position is presented in the form of a pair of coordinates and a radius, hence the estimated area for tracked device is enclosed by a circle. This research focuses on the assessment of the accuracy of the locations given by Google Location History Timeline, which variables affect this accuracy and the initial steps to develop a linear multivariate model that can potentially predict the actual error with respect to the true location considering environmental variables. The determination of the potential influential variables (configuration of mobile device connectivity, speed of movement and environment) was set through a series of experiments in which the true position of the device was recorded with a reference Global Positioning System (GPS) device with a superior order of accuracy. The accuracy was assessed measuring the distance between the Google provided position and the de facto one, later referred to as Google error. If this Google error distance is less than the radius provided, we define it as a hit. The configuration that has the largest hit rate is when the mobile device has GPS available, with a 52% success. Then the use of 3G and 2G connection go with 38% and 33% respectively. The Wi-Fi connection only has a hit rate of 7%. Regarding the means of transport, when the connection is 2G or 3G, the worst results are in Still with a hit rate of 9% and the best in Car with 57%. Regarding the prediction model, the distances and angles from the position of the device to the three nearest cell towers, and the categorical (non-numerical) variables of Environment and means of transport were taking as input variables in this initial study. To evaluate the usability of a model, a Model hit is defined when the actual observation is within the 95% confidence interval provided by the model. Out of the models developed, the one that shows the best results was the one that predicted the accuracy when the used network is 2G, with 76% of Model hits. The second model with best performance had only a 23% success (with the mobile network set to 3G).

Positive Aspects of Caregiving and Caregiver Burden: A Study of Caregivers of Patients With Dementia

BACKGROUND

Now positive aspect of caregiving (PAC) is well-defined as caregiver gains, satisfaction, meaningful life, and enhanced family relationship. The adjusted association of PAC and caregiver burden is not well acknowledged. This study investigated the association of caregiver burden and PAC adjusting for potential confounders.

METHODS

This was a cross-sectional study that recruited 132 caregivers. A linear regression model with PAC was used to estimate the adjusted associations.

RESULTS

The caregiver burden was negatively associated with PAC (mean difference in PAC per a 1-unit increase in caregiver burden = -0.12, 95% confidence interval: -0.18 to -0.056; P < .001). This association remained after adjustment for caregivers' age and marital status as well as patients' dependency level.

CONCLUSION

The negative significant association of caregiver burden with PAC reinforces the need for interventional and/or educational programs aiming at decreasing the overall imposed burden. This can play an important role in improving caregivers' general health and quality of life.

Predictive Relation between Early Numerical Competencies and Mathematics Achievement in First Grade Portuguese Children

Early numerical competencies (ENC) (counting, number relations, and basic arithmetic operations) have a central position in the initial learning of mathematics, and their assessment is useful for predicting later mathematics achievement. Using a regression model, this study aims to analyze the correlational and predictive evidence between ENC and mathematics achievement in first grade Portuguese children (n = 123). The children's ENC were examined at the point of school entry. Three criterion groups (low, moderate, and high ENC) were formed based on the results of the early numerical brief screener and mathematics achievement measured at the end of first grade. The following hypotheses were tested: children who started first grade with low numerical competencies remained low mathematics achievement at the end of first grade; and children who started with high numerical competencies, finished the first grade with high mathematics achievement. The results showed that ENC contributed to a significant amount of explained variance in mathematics achievement at the end of the first grade. Children with low numerical competencies performed lower than children with moderate and high numerical competencies. Findings suggest that ENC are meaningful for predicting first-grade mathematics difficulties.

Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models

Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the homoscedasticity assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated assumptions is discussed.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow's Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow's milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R² (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R² (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

Quantitative Ethylene Measurements with MOx Chemiresistive Sensors at Different Relative Air Humidities

The sensitivity of two commercial metal oxide (MOx) sensors to ethylene is tested at different relative humidities. One sensor (MiCS-5914) is based on tungsten oxide, the other (MQ-3) on tin oxide. Both sensors were found to be sensitive to ethylene concentrations down to 10 ppm. Both sensors have significant response times; however, the tungsten sensor is the faster one. Sensor models are developed that predict the concentration of ethylene given the sensor output and the relative humidity. The MQ-3 sensor model achieves an accuracy of ±9.2 ppm and the MiCS-5914 sensor model predicts concentration to ±7.0 ppm. Both sensors are more accurate for concentrations below 50 ppm, achieving ±6.7 ppm (MQ-3) and 5.7 ppm (MiCS-5914).

An optimal proportion of mixing broad-leaved forest for enhancing the effective productivity of moso bamboo

Moso bamboos (Phyllostachys edulis) are important forestry plants in southern China, with substantial roles to play in regional economic and ecological systems. Mixing broad-leaved forests and moso bamboos is a common management practice in China, and it is fundamental to elucidate the interactions between broad-leaved trees and moso bamboos for ensuring the sustainable provision of ecosystem services. We examine how the proportion of broad-leaved forest in a mixed managed zone, topology, and soil profile affects the effective productivity of moso bamboos (i.e., those with significant economic value), using linear regression and generalized additive models. Bamboo's diameter at breast height follows a Weibull distribution. The importance of these variables to bamboo productivity is, respectively, slope (25.9%), the proportion of broad-leaved forest (24.8%), elevation (23.3%), gravel content by volume (16.6%), slope location (8.3%), and soil layer thickness (1.2%). Highest productivity is found on the 25° slope, with a 600-m elevation, and 30% broad-leaved forest. As such, broad-leaved forest in the upper slope can have a strong influence on the effective productivity of moso bamboo, ranking only after slope and before elevation. These factors can be considered in future management practice.

Evaluation of accuracy of linear regression models in predicting urban stormwater discharge characteristics

Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days (ADD), have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency's Storm Water Management Model (SWMM). The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid (TSS) discharge loads and event mean concentrations (EMC) were extracted. From these data, linear regression models were developed. R(2) and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data.

Infant mortality rates regressed against number of vaccine doses routinely given: is there a biochemical or synergistic toxicity?

The infant mortality rate (IMR) is one of the most important indicators of the socio-economic well-being and public health conditions of a country. The US childhood immunization schedule specifies 26 vaccine doses for infants aged less than 1 year--the most in the world--yet 33 nations have lower IMRs. Using linear regression, the immunization schedules of these 34 nations were examined and a correlation coefficient of r = 0.70 (p < 0.0001) was found between IMRs and the number of vaccine doses routinely given to infants. Nations were also grouped into five different vaccine dose ranges: 12-14, 15-17, 18-20, 21-23, and 24-26. The mean IMRs of all nations within each group were then calculated. Linear regression analysis of unweighted mean IMRs showed a high statistically significant correlation between increasing number of vaccine doses and increasing infant mortality rates, with r = 0.992 (p = 0.0009). Using the Tukey-Kramer test, statistically significant differences in mean IMRs were found between nations giving 12-14 vaccine doses and those giving 21-23, and 24-26 doses. A closer inspection of correlations between vaccine doses, biochemical or synergistic toxicity, and IMRs is essential.

Statistical Inference for Regression Models with Covariate Measurement Error and Auxiliary Information

We consider statistical inference on a regression model in which some covariables are measured with errors together with an auxiliary variable. The proposed estimation for the regression coefficients is based on some estimating equations. This new method alleates some drawbacks of previously proposed estimations. This includes the requirment of undersmoothing the regressor functions over the auxiliary variable, the restriction on other covariables which can be observed exactly, among others. The large sample properties of the proposed estimator are established. We further propose a jackknife estimation, which consists of deleting one estimating equation (instead of one obervation) at a time. We show that the jackknife estimator of the regression coefficients and the estimating equations based estimator are asymptotically equivalent. Simulations show that the jackknife estimator has smaller biases when sample size is small or moderate. In addition, the jackknife estimation can also provide a consistent estimator of the asymptotic covariance matrix, which is robust to the heteroscedasticity. We illustrate these methods by applying them to a real data set from marketing science.