Least-squares Linear Regression Research Articles

Machine learning approaches for simultaneous spectrophotometric determination of heavy metal ions in water samples

In this study, the simultaneous determination of Co, Cd, Ni, Cu, and Pb was carried out as a color complex with 4-(2-pyridylazo) resorcinol in an aqueous solution under the assesting of machine learning. A partial least-squares multivariate linear regression and artificial neuron network for the analysis of mixtures of metals were developed. MATLAB is a powerful software machine learning program that was used to support matrix calculations and displays. The benefit of MATLAB in the construction of the machine learning model allows the development of a rapid and highly effective analysis of multiple components in the mixtures without separation and enrichment. For individual determinations, the working ranges were discovered as the important information for choosing the initial concentration of each heavy metal in a mixture, r. The results of analysis of Ni2+, Pb2+, and Cd2+ by two methods Partial Least Squares - PLS and Artificial Neural Networks - ANN are sensitive and accurate for simultaneous determination of the concentration of these ions in the synthesis mixture with a high regression coefficient of 0.993, respectively, 0.997, 0.997 for Ni2+, Pb2+ and Cd2+. As for Cu2+ and Co2+, the accuracy is higher when using the ANN method.

Estimating the Aboveground Fresh Weight of Sugarcane Using Multispectral Images and Light Detection and Ranging (LiDAR)

This study aimed to develop a remote sensing method for estimating the aboveground fresh weight (AGFW) of sugarcane using multispectral images and light detection and ranging (LiDAR). Remotely sensed data were acquired from an unmanned aerial vehicle (drone). Sample plots were harvested and the AGFW of each plot was measured. Sugarcane crown heights and volumes were obtained by isolating individual tree crowns using a LiDAR-derived digital surface model of the area. Multiple linear regression (MLR) and partial least-squares regression (PLSR) models were tested for the field-sampled AGFWs (dependent variable) and individual canopy heights and volumes, and spectral indices were used as independent variables or predictors. The PLSR model showed more promising results than the MLR model when predicting the AGFW over the study area. Although PLSR is well-suited to a large number of collinear predictor variables and a limited number of field samples, this study showed moderate results (R2 = 0.5). The visual appearance of the spatial distribution of the AGFW map is satisfactory. The limited no. of field samples overfitted the MLR prediction results. Overall, this research highlights the potential of integrating remote sensing technologies in the sugarcane industry, thereby improving yield estimation and effective crop management.

Association of Fructosamine Levels With Glycemic Control in Children With Type 1 Diabetes as Determined by Continuous Glucose Monitoring: Results From the CGM TIME Trial

ObjectiveOur aim in this study was to determine the correlation between serum fructosamine and average blood glucose, as measured by continuous glucose monitoring (CGM) in children with type 1 diabetes. MethodsNinety-seven blood samples were collected from 70 participants in the Timing of Initiation of continuous glucose Monitoring in Established pediatric diabetes (CGM TIME) Trial. Each eligible participant had 3 weeks of CGM data with at least 60% CGM adherence before blood collection. Ordinary least-squares linear regression incorporating restricted cubic splines was used to determine the association between fructosamine levels and mean blood glucose. ResultsAn association was found between fructosamine and mean blood glucose, with an F statistic of 9.543 (p<0.001). Data were used to create a formula and conversion chart for calculating mean blood glucose from fructosamine levels for clinical use. ConclusionsThere is a complex relationship between average blood glucose, as determined by CGM and fructosamine. Fructosamine levels may be clinically useful for assessing short-term glycemic control when CGM is not available.

Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia

Accurately simulating glacier mass balance (GMB) data is crucial for assessing the impacts of climate change on glacier dynamics. Since physical models often face challenges in comprehensively accounting for factors influencing glacial melt and uncertainties in inputs, machine learning (ML) offers a viable alternative due to its robust flexibility and nonlinear fitting capability. However, the effectiveness of ML in modeling GMB data across diverse glacier types within High Mountain Asia has not yet been thoroughly explored. This study addresses this research gap by evaluating ML models used for the simulation of annual glacier-wide GMB data, with a specific focus on comparing maritime glaciers in the Niyang River basin and continental glaciers in the Manas River basin. For this purpose, meteorological predictive factors derived from monthly ERA5-Land datasets, and topographical predictive factors obtained from the Randolph Glacier Inventory, along with target GMB data rooted in geodetic mass balance observations, were employed to drive four selective ML models: the random forest model, the gradient boosting decision tree (GBDT) model, the deep neural network model, and the ordinary least-square linear regression model. The results highlighted that ML models generally exhibit superior performance in the simulation of GMB data for continental glaciers compared to maritime ones. Moreover, among the four ML models, the GBDT model was found to consistently exhibit superior performance with coefficient of determination (R2) values of 0.72 and 0.67 and root mean squared error (RMSE) values of 0.21 m w.e. and 0.30 m w.e. for glaciers within Manas and Niyang river basins, respectively. Furthermore, this study reveals that topographical and climatic factors differentially influence GMB simulations in maritime and continental glaciers, providing key insights into glacier dynamics in response to climate change. In summary, ML, particularly the GBDT model, demonstrates significant potential in GMB simulation. Moreover, the application of ML can enhance the accuracy of GMB modeling, providing a promising approach to assess the impacts of climate change on glacier dynamics.

Self-identified barriers to health services among migrants 50 years of age or older: population-based survey study of Russian speakers in Finland

BackgroundThe compounded effect of a migratory background and ageing increases the risk of unequal medical treatment opportunities. The aim of this article is to investigate the social determinants of barriers to health services.MethodsThe study uses population-based survey data of Russian-speaking migrants (50 + years) residing in Finland (n = 1082, 57% of men, mean age 63 years). Multiple correspondence analysis was performed as a dimension reduction procedure on six barriers to health services. Multiple ordinary least-squares linear regression was used for the predicted score of the barriers as an outcome variable.ResultsMost of the sociodemographic characteristics were not associated with barriers to health services, except gender, as women tended to face more disadvantages. Migration-related factors, such as the need for interpreters for health services and experienced discrimination, were associated with an increased likelihood of reporting barriers to health services. Using the internet as a primary source of health information was associated with more access barriers to health services.ConclusionsMigrants 50 years of age or older face multiple barriers to health services. Given that the healthcare needs increase with age, addressing this issue becomes crucial, necessitating improved access to health services for older migrants.

IDaPS — Improved data-locality aware data placement strategy based on Markov clustering to enhance MapReduce performance on Hadoop

The execution of Map-Reduce applications on the Hadoop cluster poses significant challenges due to the non-consideration of data locality, i.e., assigning tasks to compute nodes where input data sets are located. Due to such non-consideration, high data transfer overheads are caused. Further, it increases latency, which may arise if input data needs to be transferred across the network, thereby significantly increasing execution time. To address this issue, an Improved DAta Placement Strategy IDaPS based on the intra-dependency among the data is proposed. IDaPS re-organizes the default data layouts in HDFS to ensure higher degree of parallelism. The efficiency of IDaPS is demonstrated in Hadoop clusters (10 and 15 nodes) by executing Hadoop Benchmark performance tests viz. WordCount, Grep on Project-Gutenberg book dataset (50 GB) and Least Square Linear Regression (LSLR) on weather dataset (10.67 GB). The results were compared with state-of-the-art algorithms viz. Hadoop Default Data Placement (HDDP), Load-Balancer and literary work RENDA. The results demonstrate that IDaPS significantly reduces execution time by 28.2% and 38.4% in 10-node and 15-node clusters while executing WordCount, and 35% and 38.1% in 10-node and 15-node clusters for Grep. Similarly, for LSLR, it reduces execution time by 32.7%.

Is the Bland-Altman plot method useful without inferences for accuracy, precision, and agreement?

This study aims to propose a comprehensive alternative to the Bland-Altman plot method, addressing its limitations and providing a statistical framework for evaluating the equivalences of measurement techniques. This involves introducing an innovative three-step approach for assessing accuracy, precision, and agreement between techniques, which enhances objectivity in equivalence assessment. Additionally, the development of an R package that is easy to use enables researchers to efficiently analyze and interpret technique equivalences. Inferential statistics support for equivalence between measurement techniques was proposed in three nested tests. These were based on structural regressions with the goal to assess the equivalence of structural means (accuracy), the equivalence of structural variances (precision), and concordance with the structural bisector line (agreement in measurements obtained from the same subject), using analytical methods and robust approach by bootstrapping. To promote better understanding, graphical outputs following Bland and Altman's principles were also implemented. The performance of this method was shown and confronted by five data sets from previously published articles that used Bland and Altman's method. One case demonstrated strict equivalence, three cases showed partial equivalence, and one showed poor equivalence. The developed R package containing open codes and data are available for free and with installation instructions at Harvard Dataverse at https://doi.org/10.7910/DVN/AGJPZH. Although easy to communicate, the widely cited and applied Bland and Altman plot method is often misinterpreted, since it lacks suitable inferential statistical support. Common alternatives, such as Pearson's correlation or ordinal least-square linear regression, also fail to locate the weakness of each measurement technique. It may be possible to test whether two techniques have full equivalence by preserving graphical communication, in accordance with Bland and Altman's principles, but also adding robust and suitable inferential statistics. Decomposing equivalence into three features (accuracy, precision, and agreement) helps to locate the sources of the problem when fixing a new technique.

Mapping and Assessing Riparian Vegetation Response to Drought along the Buffalo River Catchment in the Eastern Cape Province, South Africa

The increasing drought frequency poses a significant threat to global and regional river systems and ecosystem functioning, especially in the complex topographical Buffalo River catchment area of the Eastern Cape Province, South Africa. This study explored the impact of drought on riparian vegetation dynamics using the Normalize Difference Vegetation Index (NDVI), Transformed Difference Vegetation Index (TDVI) and Modified Normalized Difference Water Index (MNDWI) from satellite-derived Landsat data from 1990 to 2020. The least-squares linear regression and Pearson’s correlation coefficient were used to evaluate the long-term drought in riparian vegetation cover and the role of precipitation and streamflow. The correlation results revealed a moderate positive correlation (r = 0.77) between precipitation and streamflow with a significant p-value of 0.04 suggesting consequences on riparian vegetation health. Concurrent with the precipitation, the vegetation trends showed that precipitation increased insignificantly with less of an influence while the reverse was the case with the streamflow in the long term. The results show that the NDVI and TDVI were significant indices for detecting water-stressed vegetation in river catchment dynamics. Much of these changes were reflected for MNDWI in dry areas with a higher accuracy (87.47%) and dense vegetation in the upper catchment areas. The standardized precipitation index (SPI) revealed the inter-annual and inter-seasonal variations in drought-stressed years between 1991–1996, 2000–2004, 2009–2010, 2015, and 2018–2019, while 2020 exhibited slight sensitivity to drought. The findings of this study underscore the need for heightened efforts on catchment-scale drought awareness for policy development, programs, and practices towards ecosystem-based adaptation.

Hyperspectral (VNIR-SWIR) analysis of roll front uranium host rocks and industrial minerals from Karnes and Live Oak Counties, Texas Coastal Plain

VNIR-SWIR (400–2500 nm) reflectance measurements were made on the surfaces of various cores, cuttings and sample splits of sedimentary rocks from the Tertiary Jackson Group, and Catahoula, Oakville and Goliad Formations. These rocks vary in composition and texture from mudstone and claystone to sandstone and are known host rocks for roll front uranium occurrences in Karnes and Live Oak Counties, Texas. Spectral reflectance profiles, 569 in total, were reduced to 125 representative spectral signatures, which were analyzed using the U.S. Geological Survey's (USGS) Material Identification and Characterization Algorithm (MICA). MICA uses an automated continuum-removal procedure together with a least-squares linear regression to determine the fit of observed sample spectral absorption features to those of reference mineral standards in a spectral library. The reference minerals include various clay, mica, carbonate, ferric and ferrous iron minerals and their mixtures. In addition, absorption feature band-depth analysis was done to identify rock surfaces exhibiting absorption features related to uranium and zeolite minerals, which were not included in the command files used to execute MICA.Rocks from each of the four geologic units produced broadly similar spectral signatures as a result of comparable mineral compositions, but there were some notable differences. For example, Ca- and Na-montmorillonite was matched most frequently to the spectral absorption features in 2-μm (∼2000–2500 nm) wavelengths, while goethite occurred often at 1-μm (∼400–1000 nm) wavelengths. The latter is related to limonitic iron-staining in and around oxidized zones of the uranium roll front as described in previous papers. Rocks of the Jackson Group differed from those of the Catahoula, Oakville and Goliad units in that the former exhibited spectral features we interpret as being due to the presence of lignite-bearing mudstone layers. Goliad rocks exhibit spectral features related to dolomite, gypsum, anhydrite, and an unidentified green clay mineral that is possibly glauconite. Jackson Group rocks also exhibit weak but well-resolved absorption features at 964 and 1157 nm related to either or both zeolite minerals clinoptilolite and heulandite. These zeolite minerals and a few spectra exhibiting hydrous silica absorption features are indicative of alteration of volcanic glass in tuffaceous mudstone and claystone layers. A few sample spectra exhibited strong absorption features at around 1135 nm related to the uranium mineral coffinite. Both the 1135 nm coffinite and 1157 nm zeolite absorption features overlap somewhat, potentially making them difficult to distinguish without additional hyperspectral field, laboratory or remote sensing data.The results of this study were compared to mixtures of minerals described for ore, gangue and alteration minerals in deposit models for sandstone-hosted uranium, sedimentary bentonite and sedimentary zeolite. Use of these spectra can help facilitate mapping of both waste materials from the legacy mining of the above commodities, as well as future exploration and resource assessment activities.

Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data.

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier's performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier's performance emphasizes the role of feature selection methods.

Harnessing dual-energy CT for glycogen quantification: a phantom analysis.

Non-invasive glycogen quantification in vivo could provide crucial information on biological processes for glycogen storage disorder. Using dual-energy computed tomography (DECT), this study aimed to assess the viability of quantifying glycogen content in vitro. A fast kilovolt-peak switching DECT was used to scan a phantom containing 33 cylinders with different proportions of glycogen and iodine mixture at varying doses. The virtual glycogen concentration (VGC) was then measured using material composition images. Additionally, the correlations between VGC and nominal glycogen concentration (NGC) were evaluated using least-square linear regression, then the calibration curve was constructed. Quantitative estimation was performed by calculating the linearity, conversion factor (inverse of curve slope), stability, sensitivity (limit of detection/limit of quantification), repeatability (inter-class correlation coefficient), and variability (coefficient of variation). In all conditions, excellent linear relationship between VGC and NGC were observed (P<0.001, coefficient of determination: 0.989-0.997; residual root-mean-square error of glycogen: 1.862-3.267 mg/mL). The estimated conversion factor from VGC to NGC was 3.068-3.222. In addition, no significant differences in curve slope were observed among different dose levels and iodine densities. The limit of detection and limit of quantification had respective ranges of 6.421-15.315 and 10.95-16.46 mg/mL. The data demonstrated excellent scan-repeat scan agreement (inter-class correlation coefficient, 0.977-0.991) and small variation (coefficient of variation, 0.1-0.2%). The pilot phantom analysis demonstrated the feasibility and efficacy of detecting and quantifying glycogen using DECT and provided good quantitative performance with significant stability and reproducibility/variability. Thus, in the future, DECT could be used as a convenient method for glycogen quantification to provide more reliable information for clinical decision-making.

6:2 Chlorinated Polyfluoroalkyl Ether Sulfonates Exert Stronger Thyroid Homeostasis Disruptive Effects in Newborns than Perfluorooctanesulfonate: Evidence Based on Bayesian Benchmark Dose Values from a Population Study.

Growing toxicologic evidence suggests that emerging perfluoroalkyl substances (PFASs), like chlorinated polyfluoroalkyl ether sulfonate (Cl-PFESA), may be as toxic or more toxic than perfluorooctanesulfonate (PFOS) and perfluorooctanoic acid (PFOA). However, further investigations are needed in terms of the human health risk assessment. This study examined the effects of emerging and legacy PFAS exposure on newborn thyroid homeostasis and compared the thyroid disruption caused by 6:2 Cl-PFESA and PFOS using a benchmark dose approach. The health effects of mixture and individual exposure were estimated using the partial least-squares (PLS) model and linear regression, respectively. A Bayesian benchmark dose (BMD) analysis determined the BMD value for adverse effect comparison between 6:2 Cl-PFESA and PFOS. The median (interquartile range) concentrations of 6:2 Cl-PFESA (0.573 [0.351-0.872] ng/mL), PFOS (0.674 [0.462-1.007] ng/mL), and PFOA (1.457 [1.034, 2.405] ng/mL) were found to be similar. The PLS model ranked the PFAS variables' importance in projection (VIP) scores as follows: 6:2 Cl-PFESA > PFOS > PFOA. Linear regression showed that 6:2 Cl-PFESA had a positive association with free triiodothyronine (FT3, P = 0.006) and triiodothyronine (T3, P = 0.014), while PFOS had a marginally significant positive association with FT3 alone (P = 0.042). The BMD analysis indicated that the estimated BMD10 for 6:2 Cl-PFESA (1.01 ng/mL) was lower than that for PFOS (1.66 ng/mL) in relation to a 10% increase in FT3. These findings suggest that 6:2 Cl-PFESA, an alternative to PFOS, has a more pronounced impact on newborns' thyroid homeostasis compared to PFOS and other legacy PFASs.

Preclinical pharmacokinetic exploration of a novel osteoporotic quinazolinone-benzopyran-indole hybrid (S019-0385) using LC-MS/MS

1. The current investigation was to develop and validate the LC-MS/MS method in order to analyse the various pharmacokinetic parameters of S019-0385. A sensitive, selective, and robust LC-MS/MS approach was established and validated for measuring S019-0385 in female mice plasma and tissue, using optimal multiple reaction monitoring (MRM) transition m/z 488.25/329.12 on positive mode. On a Waters Symmetry Shield C18 column, the analyte was separated using acetonitrile and deionised water with formic acid within 6 min at 0.7 mL/min. Linearity (R2 ≥ 0.99) was observed across 0.195–100 ng/mL concentration range using linear least-squares regression. 2. Blood-to-plasma ratio and plasma protein drug binding (%) in mice and human was assessed and found to be less than 1 and >83%, respectively. Absolute bioavailability (%F) of S019-0385 in female Swiss mice was exhibited to be 6.90%. Percent dose excreted S019-0385 in unchanged form through urine and faecal was found to be less than 2% and 0.5%, respectively. 3. Following oral administration at 5 mg/kg, the concentration of S019-0385 in tissue distribution was found to be in the order of C small intestine > C bone > C lung > C spleen > C kidney > C liver > C heart > C brain.

Longitudinal machine learning model for predicting systolic blood pressure in patients with heart failure.

Systolic blood pressure (SBP) strongly indicates the prognosis of heart failure (HF) patients, as it is closely linked to the risk of death and readmission. Hence, maintaining control over blood pressure is a vital factor in the management of these patients. In order to determine significant variables associated with changes in SBP over time and assess the effectiveness of classical and machine learning models in predicting SBP, this study aimed to conduct a comparative analysis between the two. This retrospective cohort study involved the analysis of data from 483 patients with HF who were admitted to Farshchian Heart Center located in Hamadan in the west of Iran, and hospitalized at least two times between October 2015 and July 2019. To predict SBP, we utilized a linear mixed-effects model (LMM) and mixed-effects least-square support vector regression (MLS-SVR). The effectiveness of both models was evaluated based on the mean absolute error and root mean squared error. The LMM analysis revealed that changes in SBP over time were significantly associated with sex, body mass index (BMI), sodium, time, and history of hypertension (P-value < 0.05). Furthermore, according to the MLS-SVR analysis, the four most important variables in predicting SBP were identified as history of hypertension, sodium, BMI, and triglyceride. In both the training and testing datasets, MLS-SVR outperformed LMM in terms of performance. Based on our results, it appears that MLS-SVR has the potential to serve as a viable alternative to classical longitudinal models for predicting SBP in patients with HF.

Thermal Stability of Cellulose Nanomaterials.

Thermal stability is a crucial property of materials, especially when they have a wide range of thermally sensitive applications. Cellulose nanomaterials (CNMs) extracted from cellulosic biomass have garnered significant attention due to their abundance, biodegradability, sustainability, production scalability, and industrial versatility. To explore the correlation between the structure, chemistry, and morphology of CNMs and their thermal stability, we present a comprehensive literature review. We identify five major factors affecting CNMs' thermal stability, namely type, source, reaction conditions, post-treatment, and drying method, and analyze their impact on CNMs' thermal stability using several case studies from the literature. Using multiple linear least-squares regression (MLR), we establish a quantitative relationship between thermal stability and seven variables: crystallinity index of the source, dissociation constant of the reactant used, reactant concentration, reaction temperature, reaction time, evaporation rate, and post-treatment presence. By understanding these interdependencies, our statistical analysis enables the design of CNMs with predictable thermal properties and identification of optimal conditions for achieving high thermal stability. The results of our study provide crucial insights that can guide the development of CNMs with enhanced thermal stability for use in a variety of industrial applications.

PD40-05 RECOVERY OF RENAL FUNCTION FOLLOWING PN IN SOLITARY KIDNEY: IMPACT OF PRE-OPERATIVE EGFR AND INTRAOPERATIVE CHARACTERISTICS ON RECOVERY OF RENAL FUNCTION

You have accessJournal of UrologyCME1 Apr 2023PD40-05 RECOVERY OF RENAL FUNCTION FOLLOWING PN IN SOLITARY KIDNEY: IMPACT OF PRE-OPERATIVE EGFR AND INTRAOPERATIVE CHARACTERISTICS ON RECOVERY OF RENAL FUNCTION Arnold Oparanozie, Stephen W. Reese, Stanley Weng, Lennert Eismann, Katiana Vasquez-Rivera, Jonathan Coleman, A. Ari Hakimi, and Paul Russo Arnold OparanozieArnold Oparanozie More articles by this author , Stephen W. ReeseStephen W. Reese More articles by this author , Stanley WengStanley Weng More articles by this author , Lennert EismannLennert Eismann More articles by this author , Katiana Vasquez-RiveraKatiana Vasquez-Rivera More articles by this author , Jonathan ColemanJonathan Coleman More articles by this author , A. Ari HakimiA. Ari Hakimi More articles by this author , and Paul RussoPaul Russo More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003345.05AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: In patients undergoing surgery for a renal mass in a solitary kidney, it remains unknown the extent of acute kidney injury (AKI), post-operative renal recovery, and role for large renal tumors (>T2b) in patients undergoing surgery for renal masses in a solitary kidney. Thus, we explored the clinical outcomes and post-operative renal function in a cohort of patients undergoing renal-sparing surgery. METHODS: This study was a single-center retrospective analysis of patients undergoing renal sparing surgery in a solitary kidney at a high-volume academic institution. Descriptive statistics were captured and compared using a Chi-squared test and Wilcoxon Rank Test for categorical and continuous variables, respectively. Multivariable regression was performed using a least-square estimates linear regression model. Survival analysis was performed using the Kaplan-Meier method. RESULTS: In total, 148 patients underwent a partial nephrectomy in a solitary kidney. Most patients were male [110 (74.3%)], 20 (13.5%) had diabetes and 96 (64.9%) had hypertension. The cause of a solitary kidney was a prior history of nephrectomy for carcinoma (n=137, 92.6%). The time from prior nephrectomy to secondary nephrectomy was 77.9 mo (IQR: 14.1-157.2) and median pre-operative eGFR was 52.1 (IQR: 43.7 – 64.7). Most patients had a complex renal mass with 77 (52.0%) having a RENAL score greater than 7. Most patients were clinical stage T1/2 (n=119, 80.4%), however 27 (18.2%) had a T3 tumor. Most patients underwent an open procedure (n=132, 89.2%), had an on-clamp partial (n=92, 62.2%) and had cold ischemia (n=68, 73.9%). The median clamp time was 36 min (IQR: 26-47). eGFR was measured in the 12 months after surgery and after an initial decline in eGFR, there was noted to be recovery of renal function. In our multi-variable model, there was no differences in change from baseline eGFR at 12 months, including starting eGFR, cold vs. warm ischemia time and on-clamp vs. off-clamp partial nephrectomy. Dialysis was required in 14 (9.5%) patients and of those who required dialysis, 8 (57.1%) only had temporary dialysis, while in 6 (42.9%) dialysis was permanent. Median OS was 118.3 mo (95% CI: 138.8 -106.2). At the end of follow-up, 69 (46.6%) had died and of those 27 (39.1%) died of kidney cancer. Of those who died of kidney cancer, 7 (25.9%) had clinical stage T3 at time of surgery. CONCLUSIONS: In this selected cohort of renal masses in solitary kidneys, nephron-sparing surgery was feasible with most patients recovering to baseline GFR regardless of approach. Source of Funding: MSK P30 grant © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e1053 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Arnold Oparanozie More articles by this author Stephen W. Reese More articles by this author Stanley Weng More articles by this author Lennert Eismann More articles by this author Katiana Vasquez-Rivera More articles by this author Jonathan Coleman More articles by this author A. Ari Hakimi More articles by this author Paul Russo More articles by this author Expand All Advertisement PDF downloadLoading ...

Lack of structural brain alterations associated with insomnia: findings from the ENIGMA-Sleep Working Group.

Existing neuroimaging studies have reported divergent structural alterations in insomnia disorder (ID). In the present study, we performed a large-scale coordinated meta-analysis by pooling structural brain measures from 1085 subjects (mean [SD] age 50.5[13.9]years, 50.2% female, 17.4% with insomnia) across three international Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA)-Sleep cohorts. Two sites recruited patients with ID/controls: Freiburg (University of Freiburg Medical Center, Freiburg, Germany) 42/43 and KUMS (Kermanshah University of Medical Sciences, Kermanshah, Iran) 42/49, while the Study of Health in Pomerania (SHIP-Trend,University MedicineGreifswald,Greifswald,Germany) recruited population-based individuals with/without insomnia symptoms 75/662. The influence of insomnia on magnetic resonance imaging-based brain morphometry using an insomnia brain score was then assessed. Within each cohort, we used an ordinary least-squares linear regression to investigate the link between the individual regional cortical and subcortical volumes and the presence of insomnia symptoms. Then, we performed a fixed-effects meta-analysis across cohorts based on the first-level results. For the insomnia brain score, weighted logistic ridge regression was performed on one sample (Freiburg), which separated patients with ID from controls to train a model based on the segmentation measurements. Afterward, the insomnia brain scores were validated using the other two samples. The model was used to predict the log-odds of the subjects with insomnia given individual insomnia-related brain atrophy. After adjusting for multiple comparisons, we did not detect any significant associations between insomnia symptoms and cortical or subcortical volumes, nor could we identify a global insomnia-related brain atrophy pattern. Thus, we observed inconsistent brain morphology differences between individuals with and without insomnia across three independent cohorts. Further large-scale cross-sectional/longitudinal studies using both structural and functional neuroimaging are warranted to decipher the neurobiology of insomnia.

Predicting the shape, size, and placement of adult human pubic symphyses.

When reconstructing fossil pelves, the articulation of the pelvic bones largely relies on subjective decisions by researchers. Different positionings at the pubic symphysis can affect the overall morphology of the pelvis and the subsequent biological interpretation associated with that individual or species. This study aims to reduce this subjectivity using quantitative models to predict pubic symphysis morphology. We collected 3D landmarks and semilandmarks on the pubic symphysis and adjacent aspects on the CT scans of 103 adults. Using geometric morphometrics we, (1) quantified pubic symphysis morphology, (2) trained simple and two-stage least-squares linear regression models to predict pubic symphysis shape, and (3) assessed the shape variation in the sample. The model with the lowest prediction error was identified as the best model. Principal components analysis was used to explore the effects of each variable on shape and hypothetical shapes were generated from the model to illustrate these effects. The best model is a two-stage least-squares model that predicts pubic symphysis size at the first stage using additive effects of sex and age, then subsequently interacts pubic symphysis size with sex and age at the second stage to predict pubic symphysis shape. Other models with low prediction errors included variables reflecting pelvic size and breadth. Linear regression modeling can be used to systematically predict pubic symphysis morphology. This method can be used in addition to other techniques to improve fossil reconstructions by more accurately estimating the morphology of this region of the pelvis.

Approximating the span of principal components via iterative least-squares

In the course of the last century, Principal Component Analysis (PCA) have become one of the pillars of modern scientific methods. Although PCA is normally addressed as a statistical tool aiming at finding orthogonal directions on which the variance is maximized, its first introduction by Pearson at 1901 was done through defining a non-linear least-squares minimization problem of fitting a plane to scattered data points. Thus, it seems natural that PCA and linear least-squares regression are somewhat related, as they both aim at fitting planes to data points. In this paper, we present a connection between the two approaches. Specifically, we present an iterated linear least-squares approach, yielding a sequence of subspaces, which converges to the space spanned by the leading principal components (i.e., principal space).

Abstract P273: A Mediterranean Diet Nutrition Intervention Increases Adherence in Feeding America's Bravest : A Stepped-Wedge Cluster-Randomized Controlled Trial

Background: United States (US) career firefighters represent a working population at high risk for cardiovascular disease that may benefit from a Mediterranean diet (MedDiet) nutrition intervention (MDNI). The Feeding America’s Bravest trial was the first workplace randomized controlled trial with nutrition education, behavior change, and incentives strategies promoting adherence to the MedDiet within US firehouses and homes. We assessed the hypothesis that a 12-month MDNI with a novel worksite behavior change approach would increase adherence to the MedDiet. Methods: 486 US career firefighters within the Indianapolis and Fishers Fire Departments were enrolled and cluster randomized by fire station to either a MDNI or control group and completed 6-month (n=336) and 12-month follow-up (n=260). Stepwise-selection regression analyses and nested least-squares linear regression models determined the contribution of each item to the between person variability of a modified Mediterranean Diet Score (mMDS). To assess the intervention effect, an intention-to-treat analysis used multilevel mixed-effects linear regression models for repeated measures to evaluate between-group and within-group mean differences in mMDS from baseline to 6-months and baseline to 12-months adjusting for age, sex, physical activity, race, and waist circumference with clusters as the random effects parameter. Results: Among 484 US career firefighters (95% male with mean age of 47 years (SD=7.5; range: 26-68) observed mean scores (SD) in the MDNI group were 23.41 (6.34) at baseline, 25.26 (5.73) at 6-months, and 24.79 (6.15) at 12-months, and in the control group; 22.87 (6.78), 26.20 (6.15), and 26.68 (6.42), respectively. Between person variability of mMDS scores was largely due to the cooking oil used at home, fried foods, beverages at home, vegetables, and quantity of alcohol. Adjusted mean changes in mMDS showed statistically significant improvements among the MDNI group at 6-months by 1.28 points (pts.) (95%CI: -0.24 to 2.81) and 1.93 pts. (95%CI: 0.39 to 3.46) at 12 months compared to the control group. Multiple imputation methods supported these findings showing 6-month improvements of 1.23 pts. (95%CI: 0.22 to 2.25) and 1.63 pts. (95%CI: 0.58 to 2.67) at 12-months in the MDNI group compared to the control group. Conclusions: In conclusion, firefighters’ increased mMDS scores evidenced the effect of a multicomponent MDNI in a non-Mediterranean working population at high cardiovascular risk. Although more research is warranted, these findings support MedDiet promotion among the US fire service and other similar workforces. Furthermore, the intervention strategies used in this trial; cooking demonstrations, positive reinforcement, family involvement, and discounted access to staple MedDiet foods, may serve as effective nutrition worksite approaches for reducing the burden of chronic disease.