Fitting Regression Models Research Articles

Enabling targeted mass drug administration for schistosomiasis in north-western Tanzania: Exploring the use of geostatistical modeling to inform planning at sub-district level.

Schistosomiasis is a parasitic disease in Tanzania affecting over 50% of the population. Current control strategies involve mass drug administration (MDA) campaigns at the district level, which have led to problems of over- and under-treatment in different areas. WHO guidelines have called for more targeted MDA to circumvent these problems, however a scarcity of prevalence data inhibits decision makers from prioritizing sub-district areas for MDA. This study demonstrated how geostatistics can be used to inform planning for targeted MDA. Geostatistical sub-district (ward-level) prevalence estimates were generated through combining a zero-inflated poisson model and kriging approach (regression kriging). To make predictions, the model used prevalence survey data collected in 2021 of 17,400 school children in six regions of Tanzania, along with several open source ecological and socio-demographic variables with known associations with schistosomiasis. The model results show that regression kriging can be used to effectively predict the ward level parasite prevalence of the two species of Schistosoma endemic to the study area. Kriging was found to further improve the regression model fit, with an adjusted R-squared value of 0.51 and 0.32 for intestinal and urogenital schistosomiasis, respectively. Targeted treatment based on model predictions would represent a shift in treatment away from 193 wards estimated to be over-treated to 149 wards that would have been omitted from the district level MDA. Geostatistical models can help to support NTD program efficiency and reduce disease transmission by facilitating WHO recommended targeted MDA treatment through provision of prevalence estimates where data is scarce.

The relationship between altitude and BMI varies across low- and middle-income countries.

Studies suggest that living at high altitude decreases obesity risk, but this research is limited to single-country analyses. We examine the relationship between altitude and body mass index (BMI) among women living in a diverse sample of low- and middle-income countries. Using Demographic and Health Survey data from 1 583 456 reproductive age women (20-49 years) in 54 countries, we fit regression models predicting BMI and obesity by altitude controlling for a range of demographic factors-age, parity, breastfeeding status, wealth, and education. A mixed-effects model with country-level random intercepts and slopes predicts an overall -0.162 kg/m2 (95% CI -0.220, -0.104) reduction in BMI and lower odds of obesity (OR 0.90, 95% CI 0.87, 0.95) for every 200 m increase in altitude. However, countries vary dramatically in whether they exhibit a negative or positive association between altitude and BMI (34 countries negative, 20 positive). Mixed findings also arise when examining odds of obesity. We show that past findings of declining obesity risk with altitude are not universal. Increasing altitude predicts slightly lower BMIs at the global level, but the relationship within individual countries varies in both strength and direction.

Assessment of the prevalence and risk factors for dry eye symptoms among Romanian medical students using the ocular surface disease index – a cross-sectional study

BackgroundThis study aims to assess how Romanian medical students suffer from dry eye disease symptoms, establish the prevalence and severity of dry eye (DE) symptoms and identify potential risk factors.MethodsAn analytical, cross-sectional study was conducted on students from “Carol Davila” University of Medicine, Romania, after the final examination period of July 2022. The OSDI score (Ocular Surface Disease Index©) was applied in an online survey. The study adopted the standards used by other authors, who defined symptomatic DED as an OSDI score greater than 12. The chi-square test was used to establish statistical significance at a cutoff value of p < 0.05. The predictive model was created using linear logistic regression analysis. The goodness of fit of the logistic regression model was assessed using the Hosmer-Lemeshow test. When the severity outcome had a nominal categorical form, multinomial regression analysis with normal subjects as a reference was performed. The distribution of the severe type of symptomatology across sex categories and years of study was analyzed using a nonparametric test (Independent-Samples Kruskal-Wallis Test).ResultsA total of 274 answers were received from 81.4% females and 18.6% males with a response rate of 35.58%. The mean age was 22.7 years ± 1.66 with an age range between 20 and 25 years old. Using the OSDI score, we established that the overall prevalence of DE symptoms was 83.6% (95%CI: 79.6%, 88%), with an 85.2% (95%CI: 80.5%, 89.8%) prevalence in females and 76.5% (95%CI: 65%,88%) in males. The severe form of DE was the most prevalent, regardless of the study year or sex. Increased screen time (p-value < 0.05) and non-smokers (p-value < 0.05) were proven risk factors. The predictive model which includes the explanatory variables (sex, contact lens wearers, smoking, oral contraceptives, screen time) proved an 84.7% predictability for symptomatic DE and was able to better predict the dependent variable than the intercept model only (p-value < 0.05). Smoking (p = 0.002) and screen time (p = 0.009) preserved their significance in the multinominal regression as well.ConclusionsThis is the first study to report the epidemiology of DE symptoms among Romanian medical students. OSDI revealed a high prevalence of symptomatic DE in medical students. Screen time, although not the only factor, likely plays a role in exacerbating the disease. This information can be used to inform healthcare policies, establish occupational health guidelines, and implement preventive measures for individuals in similar high-stress academic or professional environments.

Construction of a geographically weighted nonparametric regression model fit test

One of the approach of Geographically Weighted Regression (GWR) models is the Geographically Weighted Nonparametric Regression (GWNR) has more parameters than the GWR model. Models with more parameters usually have better match values, which is an advantage, while models with fewer parameters have the advantage of being easier to use and interpret. However, a model with more parameters should be used if it is proven to be significantly superior. Therefore, the purpose of this study was to develop a hypothesis test of goodness of fit test for GWNR model. The goodness of fit test was performed for the real data. We found that the GWNR model was more suitable than the mixed nonparametric regression model. Some highlights of the proposed method are:•A new model for GWR to overcome the unknown regression function by using mixed estimator spline truncated and fourier series at nonparametric regression•Goodness of fit for GWNR to testing the model fit between the mixed nonparametric regression model and GWNR•Applied goodness of fit test to poverty data in Sulawesi Island and infant mortality in East Java

PERFORMANCE OF MULTIPLE LINEAR REGRESSION ANALYSIS CONDUCTED UNDER RANDOMIZED COMPLETE BLOCK DESIGN

In factorial experiments conducted under randomized block design, the multiple linear regression model fitting can be performed under different combinations of the quantitative levels of the two factors and the number of replications. To determine the best combination, considering the same number of levels per factor and the same number of experimental units, it was concluded through a simulated data study that the quality of the fit increases when regression is performed in experiments with fewer combinations of levels (treatments) and more replications. Therefore, if linearity is expected, using four treatments evaluated in a 2 × 2 factorial design for model fitting is recommended. Otherwise, nine treatments evaluated in a 3 × 3 factorial design are recommended. All of this is for experiments with coefficients of variation of 20%.&#x0D; Keywords: Treatments, Replications, Experimental precision.

Degradation kinetics of organic matter in domestic sewage by sequencing batch reactor

The sequencing batch reactor (SBR) has become a popular sewage treatment process in various industries due to its strong impact load resistance and numerous other advantages. This study examines the variations of chemical oxygen demand chromium (CODcr), ammoniacal nitrogen (NH3-N), total bound nitrogen (TN) and total phosphorus (TP) with aeration time to investigate the degradation patterns of different pollutants during the SBR’s reaction stage. Additionally, an organic matter degradation kinetic model was developed. The experimental results showed that the CODcr and NH3-N in the reactor met the national discharge standards after two hours of influent. Furthermore, the removal rates of CODcr and NH3-N exceeded 85% and 99%, respectively, at the end of the final aeration. At the same time, the TN and TP removal rates remained stable at approximately 45% and 68%. The analysis of the kinetic model showed that the degradation process of organic matter followed a first-order kinetic equation: S=S0e−0.00012Xt. The regression model's fitting value exhibited a high degree of conformity with CODcr removal’s experimental results, with the average relative error of 5.52% falling within an acceptable range. Furthermore, the correlation coefficient (0.9966) indicated high accuracy and effectively represented the changes in organic matter throughout the SBR reaction stage. Consequently, this model can serve as a theoretical foundation for determining CODcr’s maximum treatment capacity in experimental wastewater.

Effect of testing criteria for infectious disease surveillance: The case of COVID-19 in Norway.

During the COVID-19 pandemic in Norway, the testing criteria and capacity changed numerous times. In this study, we aim to assess consequences of changes in testing criteria for infectious disease surveillance. We plotted the proportion of positive PCR tests and the total number of PCR tests for different periods of the pandemic in Norway. We fitted regression models for the total number of PCR tests and the probability of positive PCR tests, with time and weekday as explanatory variables. The regression analysis focuses on the time period until 2021, i.e. before Norway started vaccination. There were clear changes in testing criteria and capacity over time. In particular, there was a marked difference in the testing regime before and after the introduction of self-testing, with a drastic increase in the proportion of positive PCR tests after the introduction of self-tests. The probability of a PCR test being positive was higher for weekends and public holidays than for Mondays-Fridays. The probability for a positive PCR test was lowest on Mondays. This implies that there were different testing criteria and/or different test-seeking behaviour on different weekdays. Though the probability of testing positive clearly changed over time, we cannot in general conclude that this occurred as a direct consequence of changes in testing policies. It is natural for the testing criteria to change during a pandemic. Though smaller changes in testing criteria do not seem to have large, abrupt consequences for the disease surveillance, larger changes like the introduction and massive use of self-tests makes the test data less useful for surveillance.

Determinants of diabetic nephropathy among adult diabetic patients on follow-up at public hospitals in Addis Ababa, Ethiopia: A case-control study.

Diabetic nephropathy is defined as patients with diabetes mellitus who have persistent proteinuria for at least three consecutive measurements per year, a high blood creatinine level (>130 mol/l), or a decrease in glomerular filtration rate (<60 ml/min). Limited studies were done in Ethiopia on determinants of diabetic nephropathy among diabetic patients. Therefore, this study aimed to identify determinants of nephropathy among adult diabetic patients on follow-up at public hospitals in Addis Ababa, Ethiopia, 2022. A hospital-based unmatched case-control study design was conducted from 6 September to 9 November 2022, among diabetic patients on follow-up at public health hospitals in Addis Ababa. Using consecutive sampling techniques, a total of 442 (353 controls and 89 cases) were recruited, with a control-to-case ratio of 4:1. The data were collected using a structured and interview-administered questionnaire and variables like high-density lipoprotein, low-density lipoprotein, Glycated hemoglobin, and type of diabetes were extracted from the medical records of the patients using a checklist. The collected data were entered into Epidata 3.1 and analyzed by STATA version 15.0. Variables with a p-value < 0.25 in the bivariable logistic regression were selected for the final model. In multivariable logistic regression model fitting, variables with a p-value < 0.05 with 95% CI adjusted odds ratio have declared statistically significant risk factors of diabetic nephropathy. In this study, out of 442 study participants, 334 controls and 89 cases were included in the analysis, with a response rate of 94.6% and 100%, respectively. The majority of the study participants were 92.13% of cases and 84.13% of controls; 7.87% of cases, and 15.87% of controls were type 2 diabetes mellitus. Age 65 and above years old (AOR: 2.42; 95% CI: 1.28, 4.57); Smoking cigarette (AOR: 2.22; 95% CI: 1.18, 4.16); Non-adherent to diet (AOR: 2.11; 95% CI: 1.15, 3.84); Drinking alcohols (AOR: 1.95; 95% CI: 1.07, 3.52); Duration with diabetes more than 10 years (AOR: 3.39; 95% CI: 1.76, 6.54); Poor glycemic control (AOR: 2.19; 95% CI: 1.23, 4.28); and Low-density lipoprotein (AOR: 2.97; 95% CI: 1.69, 5.28) were found to be statistically significant risk factors of nephropathy among diabetic patients. This study found that old age, smoking cigarettes, non-adherence to diet, duration of diabetes, alcohol drinking, Glycated hemoglobin A1C, and high low-density lipoprotein were risk factors for nephropathy. Hence, continuous health education on lifestyle modifications and diabetic-related complications in each follow-up visit via front-line health professionals are very essential to avert the problem.

Talent Management Practices and Employee Job Satisfaction in Nepalese Commercial Banks

The primary objective of this study is to investigate the relationship between talent management practices and employee job satisfaction. The study employed a descriptive and analytical research design under the quantitative research method to achieve these objectives. The study targets all employees working in Nepalese commercial banks, including both headquarters and branch locations in Kathmandu, the capital city of Nepal, and a sample of 400 employees was selected. However, only 233 responses were received and used for further analysis. The selection of samples was done using a simple random sampling method. The study assessed the positions of different variables by calculating their mean values. The fitness of regression models was evaluated using F-values, and regression models were employed to examine the relationship between talent management practices and employee job satisfaction. The reliability of the data collection tool was tested using Cronbach's alpha value. Furthermore, regression assumptions were tested, including multicollinearity using tolerance and VIF value, and linearity using ANOVA. The results of the study indicate that talent management is a significant predictor of employee job satisfaction.

Effect of irrigation on farm efficiency in tribal villages of Eastern India

Irrigation is an important adaptation strategy to cope with climate change which reduces vulnerability to water stress and improves crop productivity to feed millions. There is evidence of crop yield stagnation in many developing countries, and irrigation efficiency is claimed to increase crop productivity. Therefore, this paper uses data envelopment analysis to evaluate the farmer’s productivity through technical efficiency (TE), i.e., the relationship between resource inputs and outputs of 513 paddy farmers in Eastern India. The results show that the farms are, on average operating at 14% TE, leaving a considerable scope to improve up to 86% to reach the optimal level. A significant difference is observed between irrigated and rain-fed paddy farmers, such that10% of the irrigated farms achieved efficiency scores over 40% and only 2% of rain-fed farms achieved the same. The tobit and beta fit regression models are estimated to find out the factors that influence the TE. Both surface water and groundwater sources of irrigation are used as predictors, along with other socio-demographic factors. Access to surface water irrigation is identified to be a significant determinant of farm efficiency, however, surface water irrigation, such as canal irrigation, is accessible only to farmers living on plain land. Farmers living on highlands need to explore other sources of irrigation practices, such as drip and sprinkler, that can increase TE and farm productivity. Therefore, this paper calls for government intervention to provide extensive training and facilities for these micro-irrigation practices.

Study of Modified Primary Compression Index Tests and a Regression Model for Municipal Solid Waste Considering the Temperature Effect

The modified primary compression index (MPCI) of municipal solid waste (MSW) is an important parameter for describing the primary consolidation settlement of MSW. It is beneficial to calculate the settlement and storage capacity of landfills accurately by calculating the MPCI of MSW correctly. This article studies the primary consolidation compression characteristics and influencing factors of MSW through a consolidation experiment considering temperature control. The experimental results show that the MPCI increases with an increase in temperature, decreases with an increase in dry weight, increases with an increase in water content, and increases with an increase in organic matter content. According to the above findings, an MPCI regression fitting model was established. The reliability and accuracy of the model was validated by the experimental results of the indoor bioreactor test considering temperature control.

COMPARISON OF CLINICAL BIOMARKER AND DNAM-DERIVED BIOLOGICAL AGE MEASURES IN AN ELECTIVE SURGERY COHORT

Abstract Older adults face greater post-operative morbidity due to decreasing biological aging (BA)-associated physical resilience. BA measures may quantify these vulnerabilities. Common BA quantification approaches include measures derived from clinical biomarkers and whole blood DNA methylation. The Duke Surgery One Thousand Patient Cohort is a prospective biospecimen and data repository of patients undergoing elective surgery. Using a subset of this cohort (n=119; 61±13 years of age; 50% female; 21% African American), we sought to determine: 1) the inter-relationship of BA measures; 2) associations of race, sex, BMI, and surgery type with BA; and 3) associations of BA with American Society of Anesthesia (ASA) Classification Score . Clinical biomarker BA measures included Klemera-Doubal method (KDM), homeostatic dysregulation (HD), and PhenoAge. DNAm measures included the DNAm PhenoAge, Hannum Age, and GrimAge. We calculated BA advancement (BAA) as BA minus Chronological Age (CA). We calculated Pearson correlation coefficients and fitted regression models, adjusting for CA, sex, race, and BMI. Overall, participants were biologically older than their CA. Male sex and African American race were associated with greater BAA by most measures . Associations of surgery type with BAA were consistent across measures . Greater BAA by most measures was associated with a higher ASA Score, reflecting higher baseline surgical risk. In conclusion, BA measures using different quantitative approaches showed anticipated associations with demographic factors, surgery type, and surgical risk. Ongoing work will identify BA measures that predict adverse surgical outcomes, and that could be used to identify patients for perioperative prehabilitation.

A comprehensive approach for calibrating anthropogenic effects on atmosphere degradation

Climate change is the most pervasive threat to the ecosystem, species survival, global economy, and urban lives. Global citizens and governments have perceived the alleviation of harmful anthropogenic effects as an urgent priority. However, conflicting interests between interest groups preclude achieving a common goal. The convincing solutions fundamentally proceed from the precise calibration. Considerable efforts have established calibration models where population density and road transportation have been investigated as major proxies. The model components generally involve spatiotemporal characteristics, but the consideration of time-varying, spatially correlated attributes is insufficient. This article presents a pilot approach in a comprehensive manner; it includes a spatial feature transformation procedure called Kriging for spatial consistency and applying model-fitting and explanation techniques. In the case study, the regression models fit by OLS, Ridge, and Lasso showed analogous coefficients for SO2, NO2, CO, O3, PM2.5, and PM10 emissions, whereas the magnitudes and directions extracted by classification techniques such as ANN and XGBoost vary with emission intensity. This quantitative interpretation based on coefficients or weights could be incompatible with qualitative aspects. As an alternative, this article applied SHAP technique to XGBoost so that the discovery of multidirectional relationships complemented this incongruity. In conclusion, the model design needs to encompass the whole process from recognizing data properties to eliciting high accuracy and scientific proof for efficacious policies and schemes. Environmental action building on valid models would promise to alleviate climate risks and sustain lives.

Improved Outcomes with Heavy Silicone Oil in Complex Primary Retinal Detachment: A Large Multicenter Matched Cohort Study

PurposeTo establish whether Densiron® 68, a heavier-than-water endotamponade agent, is an effective alternative to conventional light silicone oil in primary rhegmatogenous retinal detachment surgery for eyes with inferior breaks in the detached retina and severe proliferative vitreoretinopathy. DesignCohort study of routinely collected data from the European Society of Retina Specialists (EURETINA) and British and Eire Association of Vitreoretinal Surgeons (BEAVRS) vitreoretinal database between 2015 and 2022. ParticipantsAll consecutive eyes that underwent primary rhegmatogenous retinal detachment surgery using either Densiron® 68 or light silicone oil as an internal tamponade agent. MethodsTo minimize confounding bias, we undertook 2:1 nearest neighbor matching on inferior breaks, large inferior rhegmatogenous retinal detachments, proliferative vitreoretinopathy, and, for visual analyses, baseline visual acuity between treatment groups. We fit regression models including prognostically relevant covariates, treatment-covariate interactions, and matching weights. We used g-computation with cluster-robust methods to estimate marginal effects. For nonlinear models, we calculated confidence intervals using bias-corrected cluster bootstrapping with 9 999 replications. Main Outcome MeasuresPresence of a fully attached retina and visual acuity at least two months after oil removal. ResultsOf 1061 eyes enrolled, 426 and 239 were included in our matched samples for anatomical and visual outcome analyses, respectively. The primary success rate was higher in the Densiron® 68 group (113 of 142; 80%) compared to the light silicone oil group (180 of 284; 63%), with an adjusted odds ratio of 1.90 (95% CI 1.63 to 2.23, P < 0.001). We also observed a significant improvement favoring Densiron® 68 of -0.26 logMAR in postoperative visual acuity between the two groups (95% CI -0.43 to -0.10, P = 0.002). The anatomical benefit of using Densiron® 68 in eyes with inferior retinal breaks and large detachments was more pronounced among eyes with proliferative vitreoretinopathy Grade C. We found no evidence of visual effect moderation by anatomical outcome or foveal attachment. ConclusionsDensiron achieved higher anatomical success rates and improved visual outcomes compared to conventional light silicone oil in eyes with inferior retinal pathology and severe proliferative vitreoretinopathy.

Reconstruction of dissolved oxygen in the Indian Ocean from 1980 to 2019 based on machine learning techniques

Oceanic dissolved oxygen (DO) decline in the Indian Ocean has profound implications for Earth’s climate and human habitation in Eurasia and Africa. Owing to sparse observations, there is little research on DO variations, regional comparisons, and its relationship with marine environmental changes in the entire Indian Ocean. In this study, we applied different machine learning algorithms to fit regression models between measured DO, ocean reanalysis physical variables, and spatiotemporal variables. We utilized the Extremely Randomized Trees (ERT) model with the best performance, inputting complete reanalysis data and spatiotemporal information to reconstruct a four-dimensional DO dataset of the Indian Ocean during 1980–2019. The evaluation results showed that the ERT-based DO dataset was superior to the DO simulations in Earth System Models across different time and space. Furthermore, we assessed the spatiotemporal variations in reconstructed DO dataset. DO decline and oxygen-minimum zone (OMZ) expansion were prominent in the Arabian Sea, Bay of Bengal, and Equatorial Indian Ocean. Through correlation analysis, we found that temperature and salinity changes related to solubility primarily control the oxygen decrease in the middle and deep sea. However, the complicated factors with solubility change, vertical mixing, and circulation govern the oxygen increase in the upper and middle sea. Finally, we conducted a volume integral to estimate the oxygen content in the Indian Ocean. Overall, a deoxygenation trend of −141.5 ± 15.1 Tmol dec−1 was estimated over four decades, with a slowdown trend of −68.9 ± 31.3 Tmol dec−1 after 2000. Under global warming and climate change, OMZ expanding and deoxygenation in the Indian Ocean are gradually mitigating. This study enhances our understanding of DO dynamics of the Indian Ocean in response to deoxygenation.

A nonlinear data-driven approach to bias correction of XCO2 for NASA's OCO-2 ACOS version 10

Abstract. Measurements of column-averaged dry air mole fraction of CO2 (termed XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) contain systematic errors and regional-scale biases, often induced by forward model error or nonlinearity in the retrieval. Operationally, these biases are corrected for by a multiple linear regression model fit to co-retrieved variables that are highly correlated with XCO2 error. The operational bias correction is fit in tandem with a hand-tuned quality filter which limits error variance and reduces the regime of interaction between state variables and error to one that is largely linear. While the operational correction and filter are successful in reducing biases in retrievals, they do not allow for throughput or correction of data in which biases become nonlinear in predictors or features. In this paper, we demonstrate a clear improvement in the reduction in error variance over the operational correction by using a set of nonlinear machine learning models, one for land and one for ocean soundings. We further illustrate how the operational quality filter can be relaxed when used in conjunction with a nonlinear bias correction, which allows for an increase in sounding throughput by 14 % while maintaining the residual error in the operational correction. The method can readily be applied to future Atmospheric CO2 Observations from Space (ACOS) algorithm updates, to OCO-2's companion instrument OCO-3, and to other retrieved atmospheric state variables of interest.

Improved Risk Prediction in DLBCL By Combining Clinical and PET Features with Interim PET Assessment

Background: Accurate detection of patients at high risk of treatment failure following frontline immunochemotherapy in diffuse large B-cell lymphoma (DLBCL) is of paramount importance as these patients might benefit from early treatment escalation. Recently, we introduced the IMPI prognostic model based on metabolic tumor volume (MTV), age and stage that outperformed the international prognostic index (IPI). However, radiomic features such as Dmax bulk and SUV peak as well as an early treatment response at interim PET (i-PET) as a measure of chemosensitivity using ΔSUVmax may have additional predictive value. We tested different models for risk prediction aiming at a dynamic risk tool in the era of evolving radiomic features in functional imaging. Methods: All patients within the PETRA database with newly diagnosed DLBCL, who were treated with R-CHOP and had available clinical data, baseline PET and i-PET scans were included. The optimal transformation of Dmax bulk, SUV peak and ΔSUV max was determined by choosing the best fitting Cox regression model with 3-year PFS as outcome, with highest R2 and lowest Akaike Information Criterion (AIC), while the cross-validated c-index was obtained as a measure for discrimination. Subsequently, risk models were developed using clinical, baseline PET and i-PET data. The best risk model was compared to the IMPI model and our subsequent ClinicalPET model, also incorporating radiomic features (MTV, IPI, age, SUV peak and D maxbulk) by determination of risk re-classification rates and by generating kaplan-meier (KM)-curves based on 60-30-10 PFS risk groups. Results: 1014 patients were included in the analyses. Adding i-PET reponse (ΔSUV max) to the IMPI model markedly improved outcome prediction (AIC 3177.44, c-index 0.72) and was superior to IMPI model alone (AIC 3247.09, c-index 0.68). By adding D maxbulk outcome prediction was further improved (AIC 3143.23, c-index 0.74), while SUV peak did not show significant impact on outcome (p=0.07). Compared to the IMPI and the ClinicalPET model, the new model combining baseline features (MTV, age and D maxbulk) with i-PET reponse (ΔSUV max) led to a sharper segregation of KM-curves with an improved rate of correct progression risk classification (22%; 95% confidence interval 12.1-31.1%). Conclusions: Adding i-PET reponse to baseline clinical and PET parameters optimizes risk classification in DLBCL enabling individualized risk assessment in early phase of frontline treatment and outperforms our previous IMPI model and ClinicalPET models.

Using sulfur stable isotope ratios (δ34 S) for animal geolocation: Estimating the delay mechanisms between diet ingestion and isotope incorporation in tail hair.

Metabolism and diet quality play an important role in determining delay mechanisms between an animal ingesting an element and depositing the associated isotope signal in tissue. While many isotope mixing models assume instantaneous reflection of diet in an animal- tissue, this is rarely the case. Here we use data from wildebeest to measure the lag time between ingestion of 34 S and its detection in tail hair. We use time-lagged regression analysis of δ34 S data from GPS-collared blue wildebeest from the Serengeti ecosystem in combination with δ34 S isoscape data to estimate the lag time between an animal ingesting and depositing 34 S in tail hair. The best fitting regression model of δ34 S in tail hair and an individual- position on the δ34 S isoscape is generated assuming an average time delay of 78 days between ingestion and detection in tail hair. This suggests that sulfur may undergo multiple metabolic transitions before being deposited in tissue. Our findings help to unravel the underlying complexities associated with sulfur metabolism and are broadly consistent with results from other species. These findings will help to inform research aiming to apply the variation of δ34 S in inert biological material for geolocation or understanding dietary changes, especially for fast moving migratory ungulates such as wildebeest.

Mean value of pulse pressure: The key feature in ambulatory arterial stiffness index estimation using regression models

Objective: Ambulatory arterial stiffness index (AASI) is an index which indicates arterial stiffness. This work aims to explore the mathematical relationship between AASI and mean value of PP (PP‾), and reveal the importance of PP‾ during AASI estimating. Meanwhile, a well-performing AASI estimation model is presented.Methods: To evaluate AASI, electrocardiograph (ECG) signal, photoplethysmogram (PPG) signal and arterial blood pressure (ABP) are used as the source of AASI estimation. Features are extracted from the above three signals. Meanwhile, fitting curve analysis and regression models are implemented to describe the relationship between AASI and PP‾.Results: Among three fitting curves on AASI and PP‾, cubic polynomial curve performs best. The introduction of feature PP‾ in AASI estimation reduced LR's MAE from 0.0556 to 0.0372, SVMR's MAE from 0.0413 to 0.0343 and RFR's MAE from 0.0386 to 0.0256. All three estimation models obtain considerable improvement, especially on the previous worst-performing linear regression.Significance: This work presents the mathematical association between AASI and PP‾. AASI estimation using regression models can be significantly improved by involving PP‾ as its key feature, which is not only meaningful for exploring the connection between vascular elasticity function and pulse pressure, but also hold importance for the diagnosis of cardiovascular arteriosclerosis and atherosclerosis at the early stage.

Analysis of Educational and Institutional Security among Dairy Farmers in Bundelkhand Region, India

Education plays an important role in the modernized farm environment rather than the traditional one. The ability of farmers to deal with disequilibrium produced by the use of modern technologies is primarily determined by their education. Based on a thorough examination of the literature and discussions with extension professionals, an index was developed to assess the educational and institutional security of dairy farmers. Data were gathered through personal interview method with the help of the structured interview schedule. After the collection of data, the results were analyzed by using some statistical tools like percentage analysis, correlation and regression. The majority (&gt;75%) of the respondents among medium farmers had a low to medium level of institutional security, whereas among large farmers the majority (53.34%) of the respondents had a high level of institutional security. Education, land holding, annual income and mass media exposure had positive and highly significant relationship with educational security at the 1 percent level of significance. The beta coefficient and their corresponding value indicate varying level of contribution towards dependent variable under the study. When the data was put into a regression analysis for asserting the R2 value, then it was found that they were cumulatively responsible for accounting 53.90 percent variability towards dependent variable i.e. educational security. The fitted regression model was observed to be significant at 1 percent level of significance. The variables education, social participation and annual income were found to be highly significant (p&lt;0.01) while, experience in dairying, land holding and extension contact was found to be significant (p&lt;0.05).