Regression Coefficients Research Articles

Performances of different yield-detrending methods in assessing the impacts of agricultural drought and flooding: A case study in the middle-and-lower reach of the Yangtze River, China

In evaluating the impacts of drought and flooding disasters on crop yields, accurately calculating meteorological yield (i.e., detrended yield) is an important procedure. The present work aimed to compare various yield-detrending methods in terms of characterizing the regression relationships between meteorological yield and the intensities of drought and flooding. Taking the middle-and-lower reach of the Yangtze River (MLRYR) as the study region, the intensities of drought and flooding during the growing seasons of four study crops (cotton, oilseed rape, wheat, and maize) were quantified using the standardized precipitation evapotranspiration index. Nine popular yield-detrending methods and the first-difference method were employed to determine meteorological yields. The results indicated that the examined methods consistently identified the cases with very significant yield-reducing impacts of drought and flooding. The first-difference method identified 58 significant regression relationships, outperforming the yield-detrending methods (identifying 39–44 significant regression relationships). The 20-yr moving average and the linear fitting methods performed best at the provincial level, while the cubic smoothing spline and the quadratic polynomial fitting methods performed best at the district level. Based on these best-performing methods, the average method was proposed and exhibited wider applicability and better performance than the individual methods. In terms of meteorological yield losses, cotton was the most affected crop (35% of all districts experienced severe cotton yield loss) and Anhui was the most affected region (with an average crop yield loss of 14.06%). The yield-reducing impact of flooding was significant in 41 districts; in comparison, the impact of drought was significant only in 7 districts. Additionally, oilseed rape was the crop most affected by drought and flooding. These results can provide guidance for assessing agricultural drought and flooding disasters under climate change.

Non-Volatile metabolome of different cultivars of Prunus mume blossoms and screening of the antidepressant molecules targeting monoamine oxidase A

Prunus mume is a traditional Chinese plant with high ornamental and medicinal values. Although there has been some research on the antidepressant effect of green calyx Mume Flos through animal tests, there is limited information on the potential antidepressant effects of different cultivars of P. mume and their relevance with intrinsic non-volatile metabolites. In this work, the non-volatile metabolites of P. mume blossom from seven cultivars were analyzed using UPLC-Q-TOF/MS, and their antidepressant activities were examined by targeting monoamine oxidase A (MAO-A). The cultivars included Gulihong (GLH), Yudie (YD), LvE (LE), Zhusha (ZS), Jiangmei (JM), Gongfen (GF) and Meirenmei (MRM). The untargeted analysis identified 148 compounds, including flavonoids, acylated sucroses, phenolic acids etc., out of 1120 compound ions. The Blireiana Prunus MRM displayed a significant difference in both metabolites and activity, compared to the other six true Prunus cultivars, with the lowest inhibitory effect on MAO-A. The correlation analysis between the spectrum and effect of the six true Prunus cultivars was conducted by partial least squares regression (PLSR) and grey correlation (GRA) and screened 45 common active compounds that are likely responsible for the antidepressant activity. A joint analysis with the 50 untargeted differential compounds resulted in 15 compounds finally, which are believed to contribute to the differences in activity between the six cultivars. All of them have a low binding energy with MAO-A in molecular docking. According to their regression coefficients, the plentiful polyacetyl coumaroylsucroses identified in blossom are believed to be the main effective substances for the inhibition activity of MAO-A instead of the flavonoids. The results also suggest that JM may be better than LE as a potential antidepressant herb. However, experiments are required for further validation.

Influence of organizational factor on change productivity and work intensity (based on the example of US economy sectors)

Based on the results of existing research and the empirical data obtained by the author, a number of conclusions have been made about the nature and degree of influence of the labor organization factor (labor force, actually living labor) on productivity and labor intensity in industries/sectors of the US economy. This analysis covers the period from 2010 to 2022, when the American economy, subject to an extremely high degree of servization, entered the stage of “re-industrial turn”, formed on the basis of a new sixth technological structure, characterized, in addition to technological innovations, by a significant increase in the role of a person - a qualified worker with creative content of work. Based on the results of regression analysis, the grouping of industries/sectors of the US economy according to the elasticity of productivity and intensity depending on the indicators of labor organization in American companies, expressed through the corresponding regression coefficients, allowed us to draw conclusions about the ambiguity in the nature and significance of the influence of the sought factor on the explained variables. Currently, a number of American industries are forced to use extensive forms of labor organization, in particular to increase the actual hours worked, in order to maintain profitability and competitiveness, despite increasing social costs. The identification of four groups of sectors of the American economy according to this criterion indicates a change in the structural policy of the United States, characterized by a sectoral focus in the field of stabilization and increasing productivity growth rates due to the organizational factor.

Influence of Technological Capability on the Performance of Pharmaceutical Companies in Kenya

Aim: The pharmaceutical companies operating in Kenya have not been spared by the environmental turbulence and increased competition since liberalization of the industry. The main objective of the study was to assess the effects of technological capability on the performance of pharmaceutical companies in Kenya.
 Methods: The population comprised the 415 pharmaceutical companies that are operating in Kenya. The study used stratified random sampling with sample size of 103 respondents. The study targeted both manufacturers and distributors. Primary and secondary data was collected using a questionnaire covering the five variables and background information. The data was analysed using SPSS version 23.0, descriptive statistics was computed using mean, frequencies, percentages and inferential statistics to test the relationship between the study variables.
 Results: The correlation analysis revealed that there was a positive and significant association between technological capability and firm performance (r = 0.685, p = 0.000). Regression of coefficients results revealed that technological capability and firm performance are positively and significantly related (β =0.603, p=0.000). This implies that a unit increase in technological capability would lead to increase in firm performance by 0.603 units.
 Conclusion: Technological capability of a firm stands to be one of the main determinants of how well the organization performs in terms of efficiency and effectiveness.
 Recommendations: Pharmaceutical companies should embrace information technology by bringing modern and automated means of production as well as information sharing platforms that create the uniqueness, efficiency and effectiveness of their firms.

Evaluation of Using Odua Weston Jambi Hotel Services: The Influence of Internship Student Service Quality on Consumer Satisfaction

Purpose of the study: The aim of this research is to analyze the influence of student interns on service quality and consumer satisfaction in using the services of the O2 Weston Hotel in Jambi, which is an important factor in improving the guests' stay experience.
 Methodology: This research method uses a quantitative approach by randomly sampling 60 consumers of the Odua Weston Hotel in Jambi. Data was collected through a survey using a questionnaire compiled based on variables related to service quality and consumer satisfaction. Data analysis was carried out using a regression test to measure the relationship between the variables involved in this research.
 Main Findings: The research results show that there is a significant relationship between service quality and consumer satisfaction in using the services of the Odua Weston Hotel in Jambi. Regression analysis shows that service quality has a strong positive influence on consumer satisfaction, with a high and statistically significant regression coefficient value. This shows that the higher the quality of service provided by the hotel, the higher the level of consumer satisfaction.
 Novelty/Originality of this study: The novelty of this research lies in the approach of using regression methods to explore in depth the influence between service quality and consumer satisfaction at the Odua Weston Hotel in Jambi, which provides a valuable contribution in strengthening understanding of the factors that influence consumer satisfaction in the context of the hotel industry.

Adaptive orthogonal semi-supervised feature selection with reliable label matrix learning

Feature selection (FS) can select features with high value from high-dimensional data as much as possible and reduce the dimensionality of the data to improve the performance of the machine learning model and enhance the generalization ability. In semi-supervised FS (SSFS), regression-based methods utilize the label matrix, and the quality of the label matrix can directly affect the performance of FS. Hence, the importance of the reliable label matrix is obvious. Orthogonal regression (OR) can retain more information in the subspace than least squares regression (LSR). Therefore, this paper introduces OR into SSFS, and then the label scaling technique is used to learn a reliable label matrix. Also, this study utilizes adaptive graph learning to exploit more structural information about the data. Two constraints, Frobenius-norm or maximum information entropy imposed on the similarity matrix and two adaptive orthogonal SSFS (AGLSOFS) methods with reliable label matrix learning are constructed. The impact of these two constraints on the construction of dynamic similarity graphs and FS results is discussed. Effective optimization algorithms for these two methods are based on the Alternating Direction Method of Multipliers (ADMM) and Generalized Power Iteration (GPI). Experiments are conducted on 15 benchmark datasets, and the results show that: (1) similarity graphs constructed using both original and projected data are more accurate; (2) both constraints are valid; (3) both of the methods of this paper perform well on most datasets; (4) OR performs better than LSR for FS; and (5) the scaling factor affects the convergence speed of the model.

Associations between breakfast skipping and outcomes in neuropsychiatric disorders, cognitive performance, and frailty: a Mendelian randomization study

BackgroundPrior studies have identified a correlation between breakfast skipping and a heightened risk of mental health issues. This investigation aimed to employ a Two-Sample Mendelian Randomization (MR) approach to explore the potential causal links between breakfast skipping and various psychiatric, neurological disorders, cognitive performance, and frailty.MethodsUtilizing data from genome-wide association studies within European demographics, this research scrutinized the association between breakfast habits and several neuropsychiatric conditions and physical health outcomes, including Alzheimer’s disease (AD), Attention Deficit Hyperactivity Disorder (ADHD), Bipolar Disorder (BD), Major Depressive Disorder (MDD), Narcolepsy, Insomnia, cognitive performance, and frailty. In this MR analysis, the Inverse Variance Weighted (IVW) method was primarily utilized for evaluation. Outcomes were reported as Odds Ratios (OR) and regression coefficients (β), and underwent validation through False Discovery Rate (FDR) corrections, thereby offering a rigorous evaluation of the effects of breakfast habits on both mental and physical health dimensions.ResultsFindings demonstrate a significant causal link between skipping breakfast and an increased risk of ADHD (OR = 2.74, 95%CI: 1.54–4.88, PFDR = 0.003) and MDD (OR = 1.7, 95%CI: 1.22–2.37, PFDR = 0.005). Conversely, no substantial causal associations were identified between breakfast skipping and AD, BD, narcolepsy, or insomnia (PFDR > 0.05). Moreover, a notable causal relationship was established between skipping breakfast and a reduction in cognitive performance (β = -0.16, 95%CI: -0.29–0.04, PFDR = 0.024) and an increase in frailty (β = 0.29, 95%CI: 0.12–0.45, PFDR = 0.003).ConclusionThe MR analysis reveals that skipping breakfast is associated with an increased risk of ADHD, MDD, decreased cognitive performance, and greater frailty, while showing no associations were found with AD, BD, narcolepsy, or insomnia. These findings warrant further investigation into the underlying mechanisms and emphasize the importance of regular breakfast consumption for mental and physical well-being.

PENGARUH PEMASARAN WORD OF MOUTH DAN BRAND TRUST TERHADAP KEPUTUSAN PEMBELIAN KONSUMEN DI COFFEE SHOP SIMALIS

This study aims to determine the influence of word of mouth and brand trust on consumers' purchasing decisions at Simalis coffee shop. This type of research is quantitative. The population in this study was 500 simalis coffee shop consumers using the slovin formula technique, so the sample needed was 222 respondents. The data collection techniques used in this study were questionnaires, and interviews. This study uses Validity Test tools, Reliability Test, Classical Assumption Test, Multiple Linear Regression Analysis, Partial Test, Simultaneous Test and Coefficient of Determination Test. The results of this study show that word of mouth affects consumers' purchasing decisions, brand trust affects consumers' purchasing decisions, and word of mouth and brand trust affect consumers' purchasing decisions simultaneously at simalis coffee shop

A novel green synthesis of MnO2-coal composite for rapid removal of silver and lead from wastewater

The presence of Ag(I) and Pb(II) ions in wastewater poses a significant threat to human health in contemporary times. This study aims to explore the development of a novel and economical adsorbent by grafting MnO2 particles onto low-rank coal, providing an innovative solution for the remediation of water contaminated with silver and lead. The synthesized nanocomposites, referred to as MnO2-coal, underwent thorough characterization using FTIR, XRD, BET, SEM, and TEM to highlight the feasibility of in-situ surface modification of coal with MnO2 nanoparticles. The adsorption of Ag(I) and Pb(II) from their respective aqueous solution onto MnO2-coal was systematically investigated, with optimization of key parameters such as pH, temperature, initial concentration, contact time, ionic strength, and competing ions. Remarkably adsorption equilibrium was achieved within a 10 min, resulting in impressive removal rates of 80–90% for both Ag(I) and Pb(II) at pH 6. The experimental data were evaluated using Langmuir, Freundlich, and Temkin isotherm models. The Langmuir isotherm model proved to be more accurate in representing the adsorption of Ag(I) and Pb(II) ions onto MnO2-coal, exhibiting high regression coefficients (R2 = 0.99) and maximum adsorption capacities of 93.57 and 61.98 mg/g, along with partition coefficients of 4.53 and 71.92 L/g for Ag(I) and Pb(II), respectively, at 293 K. Kinetic assessments employing PFO, PSO, Elovich, and IPD models indicated that the PFO and PSO models were most suitable for adsorption mechanism of Pb(II) and Ag(I) on MnO2-coal composites, respectively. Moreover, thermodynamic evaluation revealed the spontaneous and endothermic adsorption process for Ag(I), while exothermic behavior for adsorption of Pb(II). Importantly, this approach not only demonstrates cost-effectiveness but also environmental friendliness in treating heavy metal-contamination in water. The research suggests the potential of MnO2-coal composites as efficient and sustainable adsorbents for water purification applications.

The derivative-based approach to nonlinear mediation models: insights and applications

AbstractTraditional mediation analysis has been developed in the context of linear models, enabling the estimation of indirect effects through the product of regression coefficients. However, in the presence of nonlinearities, defining and estimating indirect effects becomes more challenging. While nonlinear mediation models are relatively easy to address in the counterfactual-based framework, very few generalizations to nonlinear associational settings have been proposed. One of the most intuitive is the derivative-based approach that, however, seems not to be widely spread among scholars. In this paper, we deepen such an approach to nonlinear mediation models, clarifying and proposing solutions to some issues which have not been addressed by the previous literature. Specifically, we discussed discrete exposures, binary mediators and extensions of this approach to more complex settings like the multilevel one. We also propose to estimate confidence intervals for the indirect effect within a Bayesian framework and compare its performance to that of other approaches in the literature through a simulation study. Finally, a real data application is presented.

The Mediating Effect of Taxpayer Perception in the Relationship between Tax Education and Income Tax Filing Compliance among Small Taxpayers in Mbarara City

Purpose: This study is about establishing the mediating role of taxpayer perception in the relationship between tax education and income tax filing compliance among small taxpayers in Mbarara city.
 Materials and Methods: The study used cross-sectional research design on a study population of 33,979 small taxpayers with a sample size of 380 respondents in Mbarara city. Systematic random sampling was used to select respondents while data was collected using a questionnaire tool whose validity (Content Validity Index) and reliability (Cronbach’s Alpha) were ascertained before data collection and found to be above 0.70. The collected data was analyzed with the aid of SPSS version 26.0 to generate regression coefficients which were exported to an Online Medgraph Calculator to determine mediation effect, significance and path analysis. 
 Findings: The regression analysis results revealed that taxpayer perception had a significant partial mediating effect (βmd=.366, p<0.05) on the relationship between tax education and income tax filing compliance among small taxpayers in Mbarara city. Based on the findings, the study concludes that efforts aimed at improving taxpayer perceptions play a significant role in strengthening the relationship between tax and income tax filing compliance.
 Implications to Theory, Practice and Policy: The study recommends a need for the government and tax authorities, in this case, Uganda Revenue Authority to put in place strategies and programs aimed at changing individual perceptions, social understanding and personal motivations regarding income tax filing. Some of these strategies include intensifying tax education and outreach services of the mobile tax office ‘Tujenge Bus Services’ to all locations; introducing basic tax education at earlier stages of formal education such that citizens are informed about their future obligations to file income tax returns; translating all information about income tax filing in different local languages and publishing the same in print format for public access at no cost; and recruiting more tax agents within Mbarara city who can assist small taxpayers in income tax filing at a reasonable fee. The assumptions of the Theory of Reasoned Action, Social Influence Theory and Motivational Posturing Theory could also be relevant in assisting the tax authorities to change the personal and societal perceptions about income tax filing and motivate them to willingly comply with their filing obligations. The study has significant implications for policy making as it identifies a need for government and tax authorities to establish policies and offer incentives and motivational postures that prioritize voluntary compliance rather than introducing sanctions to enforce income tax filing compliance.

On the accuracy of sequence methods for baroreflex sensitivity estimation.

In the absence of a true gold standard for non-invasive baroreflex sensitivity estimation, it is difficult to quantify the accuracy of the variety of techniques used. A popular family of methods, usually entitled 'sequence methods' involves the extraction of (apparently) correlated sequences from blood pressure and RR-interval data and the subsequent fitting of a regression line to the data. This paper discusses the accuracy of sequence methods from a system identification perspective, using both data generated from a known mathematical model and spontaneous baroreflex data. It is shown that sequence methods can introduce significant bias in the baroreflex sensitivity estimate, even when great care is taken in sequence selection.

On the Equivalence of Linear Discriminant Analysis and Least Squares Regression.

Studying the relationship between linear discriminant analysis (LDA) and least squares regression (LSR) is of great theoretical and practical significance. It is well-known that the two-class LDA is equivalent to an LSR problem, and directly casting multiclass LDA as an LSR problem, however, becomes more challenging. Recent study reveals that the equivalence between multiclass LDA and LSR can be established based on a special class indicator matrix, but under a mild condition which may not hold under the scenarios with low-dimensional or oversampled data. In this article, we show that the equivalence between multiclass LDA and LSR can be established based on arbitrary linearly independent class indicator vectors and without any condition. In addition, we show that LDA is also equivalent to a constrained LSR based on the data-dependent indicator vectors. It can be concluded that under exactly the same mild condition, such two regressions are both equivalent to the null space LDA method. Illuminated by the equivalence of LDA and LSR, we propose a direct LDA classifier to replace the conventional framework of LDA plus extra classifier. Extensive experiments well validate the above theoretic analysis.

Factors affecting the use of a three-dimensional model during robot-assisted partial nephrectomy.

This study aimed to identify cases that require a three-dimensional-printed kidney model in robot-assisted partial nephrectomy. We enrolled 93 patients undergoing robot-assisted partial nephrectomy for renal tumors at a single institution between November 2018 and May 2021. The endpoints were how often and how long the surgeon consulted the three-dimensional-printed model, determined using intraoperative video. Multivariate analyses of the endpoints were adjusted by preoperative patient and kidney characteristics, including renal vascular complexity that was defined as the number of vascular branches penetrating the surface tangential to the ventral side of the kidney. Of the 93 cases, the median frequency and duration of intraoperative three-dimensional-printed model consultation were four times and 39 s, respectively. The multivariate linear regression analyses showed that the frequency of intraoperative three-dimensional-printed model consultation by the surgeon was significantly related to the complexity of the arterial structure (≥4 branches), presence of hilar tumor, and high Mayo Adhesive Probability score; the regression coefficients were 1.81, 2.79, and 1.34, respectively. All p-values were ≤.03. The duration of the three-dimensional-printed model consultation was significantly related to the complexity of the arterial structure (≥4 branches) and the presence of hilar tumor; the regression coefficients were 21.6, and 29.0 s, respectively. All p-values were <.01. During robot-assisted partial nephrectomy, a three-dimensional-printed model would be helpful in cases with a complex arterial structure or hilar tumor.

480 Effect of Chronic DBS on Ventral Capsule Ventral Striatum-Specific Circadian Rhythm in Refractory Obsessive-Compulsive Disorder

INTRODUCTION: Deep brain stimulation (DBS) of the ventral capsule/ventral striatum (VC/VS) improves symptoms in ∼66% of patients with treatment-resistant obsessive-compulsive disorder (OCD). However, a common adverse effect of VC/VS DBS is hypomania, marked by decreased need for sleep and increased risk-taking behaviors. Modern DBS devices have enabled 24-hour collection of neural data, allowing for tracking of circadian rhythms reflected by VC/VS local field potentials (LFPs). METHODS: We implanted six patients with treatment-resistant OCD with a DBS device configured for constant VC/VS stimulation and recording. Spectral power in the 9 ± 2.5 Hz band was estimated onboard the device every 10 minutes for a median of 249 days (range 77-608). We assessed the rhythmicity of power in this spectral band by fitting 7-day retrospective rolling windows of per-day normalized LFP power to cosine functions using least-squared regression. Output metrics included acrophase (time of peak activity), amplitude, and fit significance. We quantified trends in acrophase over time using Pearson correlation. RESULTS: All six patients demonstrated consistent 24-hour cyclic LFP activity bilaterally. Two of the six had diurnal peaks which shifted significantly earlier across the study period. The remaining four patients had nocturnal peaks, all shifting significantly later over time. Notably, a patient in the latter group experienced three days of hypomania after DBS initiation associated with circadian disruptions even after clinical symptom resolution. Only after clinical DBS adjustment 2 weeks later did circadian rhythm rapidly return to pre-hypomanic levels. CONCLUSIONS: Study patients demonstrated evolving circadian VC/VS LFP activity across the course of DBS therapy, usually independent of cyclic whole-brain activity (i.e. sleep). Conversely, disruptions to these circadian patterns, as observed here during hypomania, may serve as biomarkers of region-specific stimulation side effects.

Estimation of Lexisinatide in Bulk and Tablets by RP‐UPLC Method Developed by Quality by Design Concept

AbstractThe primary goal of the proposed study is to establish and validate a low‐cost, sensitive, reliable, and trouble‐free RP‐UPLC process with improved performance for the direct estimation of Lixisenatide in pure powder and fixed‐dose tablet form. The proposed method is optimized by implementing quality by design studies. The method is optimized using quadratic model of central composite design (CCD) of randomized response surface study. Successful separation of Lixisenatide is obtained by the use of Phenyl‐XBD (1.7 µm, 2.1 mm × 100 mm) column and a mobile phase of acetonitrile: 0.1% w/v OPA (60:40) with 0.5 mL min−1 flow rate. The separated Lixisenatide and its degradants are explorated with PDA detector which has the ability to provide comprehensive spectral information, rapid data acquisition, quantitative analysis, and peak purity analysis of all compounds simultaneously. The RT of Lixisenatide is found to be 1.34 min. The lowest concentration of Lixisenatide's limit of detection and limit of quantification makes guarantees regarding the method's sensitivity. For the provided range of linear concentrations (1.25–7.5 µg mL−1), the regression coefficient is found to be 0.999. The computed average percentage recoveries of Lixisenatide in spiked solutions ranged from 98.9% to 101.2%. The % degradation of Lixisenatide in the presence of a variety of forced conditions confirms the existing method's stability. Lixisenatide is significantly photo sensitive when compared to other stressful conditions such as acid, base hydrolysis, and oxidation. The proposed UPLC approach separates Lixisenatide with high sensitivity, a short retention time, and an affordable solvent system.

Development and validation of a trauma frailty scale in severely injured patients: the Nottingham Trauma Frailty Index.

Frailty greatly increases the risk of adverse outcome of trauma in older people. Frailty detection tools appear to be unsuitable for use in traumatically injured older patients. We therefore aimed to develop a method for detecting frailty in older people sustaining trauma using routinely collected clinical data. We analyzed prospectively collected registry data from 2,108 patients aged ≥ 65 years who were admitted to a single major trauma centre over five years (1 October 2015 to 31 July 2020). We divided the sample equally into two, creating derivation and validation samples. In the derivation sample, we performed univariate analyses followed by multivariate regression, starting with 27 clinical variables in the registry to predict Clinical Frailty Scale (CFS; range 1 to 9) scores. Bland-Altman analyses were performed in the validation cohort to evaluate any biases between the Nottingham Trauma Frailty Index (NTFI) and the CFS. In the derivation cohort, five of the 27 variables were strongly predictive of the CFS (regression coefficient B = 6.383 (95% confidence interval 5.03 to 7.74), p < 0.001): age, Abbreviated Mental Test score, admission haemoglobin concentration (g/l), pre-admission mobility (needs assistance or not), and mechanism of injury (falls from standing height). In the validation cohort, there was strong agreement between the NTFI and the CFS (mean difference 0.02) with no apparent systematic bias. We have developed a clinically applicable tool using easily and routinely measured physiological and functional parameters, which clinicians and researchers can use to guide patient care and to stratify the analysis of quality improvement and research projects.

Predicting the properties of deep eutectic solvents based on choline chloride and carboxylic acids and their mixtures with water using QSPR approach

Deep eutectic solvents (DES) are increasingly popular materials in various fields of chemistry. The set of properties of a particular DES determines the range of its applicability for a certain task. Tailoring these properties to meet the needs of various applications can be tedious, as it may require a large amount of experimental work. Here we propose the use of quantitative structure-property relationship (QSPR) approach to model DES properties. Using choline chloride and six carboxylic acids as a case study we attempted to construct regression models relating molecular descriptors of carboxylic acids and the properties of corresponding solvents. Statistically meaningful correlations were established between the descriptors of the acids and density, viscosity and conductivity of DES. The impact of different descriptors on these properties was analyzed based on the regression coefficients. Taking into account rather limited dataset a rigorous validation of the regression models was performed using double loop cross-validation.

Associations between dietary potassium intake from different food sources and hyperkalemia in patients with chronic kidney disease

ObjectivePrevious studies reported mixed results on associations between dietary potassium intake and hyperkalemia in patients with chronic kidney disease (CKD). This study investigated the association between potassium intake from different food sources and hyperkalemia in patients with non-dialysis-dependent CKD. MethodsA total of 285 patients were recruited at a university hospital and two city hospitals in Tokyo. Dietary potassium intake was estimated by a validated diet history questionnaire. Associations of potassium intake from all foods and individual food groups with serum potassium were examined by multivariable linear regression among potassium binder non-users. An association between tertile groups of potassium intake and hyperkalemia, defined as serum potassium ≥5.0 mEq/L, was evaluated by multivariable logistic regression. ResultsAmong 245 potassium binder non-users, total potassium intake was weakly associated with serum potassium (regression coefficient = 0.147, 95% confidence interval (CI): 0.018–0.277), while an association with hyperkalemia was not observed (first vs third tertile: adjusted odds ratio (aOR) = 0.98, 95% CI: 0.29–3.26). As for food groups, potassium intakes from potatoes, pulses, and green/yellow vegetables were positively associated with serum potassium. Patients in the highest tertile of potassium intake from potatoes had higher odds of hyperkalemia as compared to those in the lowest tertile (aOR = 4.12, 95% CI: 1.19–14.34). ConclusionTotal potassium intake was weakly associated with serum potassium, but not with hyperkalemia. Potassium intake from potatoes was associated with hyperkalemia. These findings highlight the importance of considering food sources of potassium in the management of hyperkalemia in CKD.

Transfer learning under the Cox model with interval‐censored data

AbstractTransfer learning, focusing on information borrowing to address limited sample size issues, has gained increasing attention in recent years. Our method aims to utilize data from other population groups as a complement to enhance risk factor discernment and failure time prediction among underrepresented subgroups. However, a literature gap exists in effective knowledge transfer from the source to the target for risk assessment with interval‐censored data while accommodating population incomparability and privacy constraints. Our objective is to bridge this gap by developing a transfer learning approach under the Cox proportional hazards model. We introduce the tuning‐free Trans‐Cox‐MIC algorithm, enabling adaptable information sharing in regression coefficients and baseline hazards, while ensuring computational efficiency. Our approach accommodates covariate distribution shifts, coefficient variations, and baseline hazard discrepancies. Extensive simulations showcase the method's accuracy, robustness, and efficiency. Application to the prostate cancer screening data demonstrates enhanced risk estimation precision and predictive performance in the African American population.