Pairwise Regression Research Articles

Profitability of Energy Sector Companies in Poland: Do Internal Factors Matter?

The aim of this article is to examine which selected internal factors influence the profitability (ROA) of companies in the energy sector in Poland and how they do so, over the period 2018–2021, taking into account two groups: all types of activities (984 companies) and electricity production (508 companies). This study uses Pearson correlation analysis, Wilcoxon pairwise rank test, descriptive statistics and multiple linear regression to build eight ROA econometric models, four for each group. The research shows that in the energy sector, in particular, variables relating to the capital structure (total equity/total assets, long-term liabilities/total assets, short-term liabilities/total assets and long-term liabilities/short-term liabilities ratios) have a statistically significant impact (positive or negative) on the profitability (ROA). The aforementioned ratios appear in various combinations in all eight ROA models. The use of equity to finance the activities of companies in this sector seems to be particularly beneficial, as the total equity/total assets ratio occurs in as many as seven out of eight models and, moreover, it always has a positive impact on the ROA. The remaining analyzed variables relating to the structure of assets (fixed assets/total assets ratio), financial liquidity (current ratio) and the age of the company appear in the models as statistically significant quite rarely, having a different impact on the ROA (positively or negatively). However, variables such as the fixed assets/current assets and total liabilities/total equity ratios do not have a statistically significant impact on the ROA at all in any of the studied groups of enterprises. The research results suggest that managers, in order to shape profitability (measured by ROA), should pay special attention to the capital structure, i.e., the proportions of the use of equity, long-term liabilities and short-term liabilities to finance the operations of energy companies as these independent variables appear most often in ROA models. Other analyzed factors, such as the assets structure (the share of fixed assets in total assets) or financial liquidity, also have an impact on the return on assets; therefore, their use in energy companies should also be considered. Moreover, the research shows a large diversity of factors shaping ROA in econometric models, the way they affect the dependent variable (positive or negative) and the degree of model fit (R2), both in individual years and in the two groups of companies studied. This proves that it is not possible to clearly and finally determine which factors and how (positive or negative) they affect the profitability. This influence can change over time depending on the circumstances, which indicates the need for the continuous involvement of decision makers in the management process and making decisions based on reliable and appropriate-to-the-situation analyses.

Sleep patterns in adolescents and associations with substance use

ObjectivesGood sleep during adolescence is crucial for maintaining physical and psychological health; however, sleep disturbance during this period may contribute to health risks, such as substance use. This study aimed to identify the latent sleep patterns across male and female adolescents, and their association with drug use. MethodA cross-sectional study was conducted involving 1391 high school students (aged 15-17; 56.4% female). Participants completed the brief Pittsburgh Sleep Quality Index alongside other sleep measures, and the Timeline Follow-Back and Drug Use History Questionnaire to measure substance use. A multiple-group latent class analysis was used to identify sleep patterns across sexes, and pairwise Logistic Regression models to compare their association with substance use. ResultsFour sleep patterns were identified with varying degrees of sleep difficulties: "Good Sleep" (43.3%), "Night Awakenings" (31.8%), "Poor Efficiency and Sleep Onset" (9.4%), and "Poor Sleep" (15.5%). Female adolescents were more likely to belong to Poor Sleep and Poor Efficiency and Sleep Onset patterns, and male adolescents to Good Sleep. Likewise, binge drinking and using alcohol for a longer period were associated with experiencing Poor Efficiency and Sleep Onset (OR=1.03 and 2.3, respectively); smoking tobacco within the past month was linked to Night Awakenings (OR=2.2); and using cannabis or illegal drugs to the Poor Sleep pattern (OR=2.4 and 2.6, respectively). ConclusionsVaried sleep difficulties exist among adolescents that significantly correlate with different aspects of drug use. Targeted interventions that address both sleep and drug prevention are recommended.

Impact of bank financial technology on the performance of banks in the European Union

This article examines how financial technology determines bank performance in different EU countries. The answer to that question would allow banks to choose their development policy. The paper focuses on the main and most popular bank services that are linked to financial technology. A SWOT analysis of FinTech is also presented to show the benefits and drawbacks of FinTech. FinTech-based services are very diverse and are provided by financial firms and banks alike. This paper looks at the financial technology provided by banks: internet usage (internet banking), number of ATMs, credit transfers in a country, percentage of the population in a country holding a debit or credit card and whether that population has received or made a digital payment. Using the multi-criteria assessment methods of CRITIC and EDAS, the authors analysed and compared the countries of the European Union and the financial technology used in them. As a result of the application of these methods, the EU countries under consideration were ranked in terms of the use of financial technology. Subsequently, three banks from different countries with different levels of the use of financial technology were selected for the study. For these banks, financial ratios of profitability were calculated to characterise their performance. Correlation and pairwise regression analyses between the banks’ profitability ratios and financial technology were used to assess the relationship and influence between these ratios. The main conclusion of the study focuses on the extent to which financial technology influences the performance of banks in the selected countries. It is likely that further research will try to take into account the size of the country’s population when analysing all financial technologies. Researchers also needed to find out what influence financial technologies have on the such financial indicators as operational efficiency (costs), financial stability, and capital adequacy.

Application of a Robust Maximum Diversified Portfolio to a Small Economy’s Stock Market: An Application to Fiji’s South Pacific Stock Exchange

In this study, we apply a novel approach of portfolio diversification—the robust maximum diversified (RMD)—to a small and developing economy’s stock market. Using monthly returns data from August 2019 to May 2024 of 18/19 stocks listed on Fiji’s South Pacific Stock Exchange (SPX), we construct the RMD portfolio and simulate with additional constraints. To implement the RMD portfolio, we replace the covariance matrix with a matrix comprising unexplained variations. The RMD procedure diversifies weights, and not risks, hence we need to run a pairwise regression between two assets (stocks) and extract the R-square to create a P-matrix. We compute each asset’s beta using the market-weighted price index, and the CAPM to calculate market-adjusted returns. Next, together with other benchmark portfolios (1/N, minimum variance, market portfolio, semi-variance, maximum skewness, and the most diversified portfolio), we examine the expected returns against the risk-free (RF) rate. From the simulations, in terms of expected return, we note that eight portfolios perform up to the RF rate. Specifically, for returns between 4 and 5%, we find that max. RMD with positive Sharpe and Sortino (as constraints) and the most diversified portfolio offer comparable returns, although the latter has slightly lower standard deviation and downside volatility and contains 94% of all the stocks. Portfolios with returns between 5% and the RF rate are the minimum-variance, the semi-variance, and the max. RMD with positive Sharpe; the latter coincides with the RF rate and contains the most (94%) stocks compared to the other two. An investor with a diversification objective, some risk tolerance and return preference up to the RF rate can consider the max. RMD with positive Sharpe. However, depending on the level of risk-averseness, the minimum-variance or the semi-variance portfolio can be considered, with the latter having lower downside volatility. Two portfolios offer returns above the RF rate—the market portfolio (max. Sharpe) and the maximum Sortino. Although the latter has the highest return, this portfolio is the least diversified and has the largest standard deviation and downside volatility. To achieve diversification and returns above the RF rate, the market portfolio should be considered.

Genetic relations between type 1 diabetes, coronary artery disease and leukocyte counts.

Type 1 diabetes is associated with excess coronary artery disease (CAD) risk even when known cardiovascular risk factors are accounted for. Genetic perturbation of haematopoiesis that alters leukocyte production is a novel independent modifier of CAD risk. We examined whether there are shared genetic determinants and causal relationships between type 1 diabetes, CAD and leukocyte counts. Genome-wide association study summary statistics were used to perform pairwise linkage disequilibrium score regression and heritability estimation from summary statistics (ρ-HESS) to respectively estimate the genome-wide and local genetic correlations, and two-sample Mendelian randomisation to estimate the causal relationships between leukocyte counts (335,855 healthy individuals), type 1 diabetes (18,942 cases, 501,638 control individuals) and CAD (122,733 cases, 424,528 control individuals). A latent causal variable (LCV) model was performed to estimate the genetic causality proportion of the genetic correlation between type 1 diabetes and CAD. There was significant genome-wide genetic correlation (rg) between type 1 diabetes and CAD (rg=0.088, p=8.60 × 10-3) and both diseases shared significant genome-wide genetic determinants with eosinophil count (rg for type 1 diabetes [rg(T1D)]=0.093, p=7.20 × 10-3, rg for CAD [rg(CAD)]=0.092, p=3.68 × 10-6) and lymphocyte count (rg(T1D)=-0.052, p=2.76 × 10-2, rg(CAD)=0.176, p=1.82 × 10-15). Sixteen independent loci showed stringent Bonferroni significant local genetic correlations between leukocyte counts, type 1 diabetes and/or CAD. Cis-genetic regulation of the expression levels of genes within shared loci between type 1 diabetes and CAD was associated with both diseases as well as leukocyte counts, including SH2B3, CTSH, MORF4L1, CTRB1, CTRB2, CFDP1 and IFIH1. Genetically predicted lymphocyte, neutrophil and eosinophil counts were associated with type 1 diabetes and CAD (lymphocyte OR for type 1 diabetes [ORT1D]=0.67, p=2.02-19, ORCAD=1.09, p=2.67 × 10-6; neutrophil ORT1D=0.82, p=5.63 × 10-5, ORCAD=1.17, p=5.02 × 10-14; and eosinophil ORT1D=1.67, p=5.45 × 10-25, ORCAD=1.07, p=2.03 × 10-4. The genetic causality proportion between type 1 diabetes and CAD was 0.36 ± 0.16 (pLCV=1.30 × 10-2), suggesting a possible intermediary causal variable. This study sheds light on shared genetic mechanisms underlying type 1 diabetes and CAD, which may contribute to their co-occurrence through regulation of gene expression and leukocyte counts and identifies cellular and molecular targets for further investigation for disease prediction and potential drug discovery.

Improving the predictive accuracy of production frontier models for efficiency measurement using machine learning: The LSB-MAFS method

Making accurate predictions of the true production frontier is critical for reliable efficiency analysis. However, canonical deterministic methods like Data Envelopment Analysis (DEA) provide approximations of the production frontier that cannot accommodate noise satisfactorily and suffer from overfitting. This study combines machine learning techniques known as Least Squares Boosting (LSB) and Multivariate Adaptive Regression Splines (MARS), to introduce a new methodology that improves the accuracy of production frontiers predictions and overcomes previous limitations. The new method fits pairwise regression splines to the data while ensuring that the predicted production frontiers satisfy certain the required regularity conditions: envelopmentness, monotonicity, and concavity. The method, termed LSB-MAFS, is implemented through computational algorithms, and we illustrate its applicability by performing simulations with several data generating processes. We also compare its performance against the most popular alternatives, considering both deterministic and stochastic scenarios: DEA, bootstrapped DEA, Corrected Concave Non-Parametric Least Squares (C2NLS) and Stochastic Frontier Analysis (SFA). The new method outperforms these alternatives in the most complex scenarios, including stochastic settings where parametric methods like SFA should perform better in principle. We conclude that our approach to production frontier prediction is a valid and competitive alternative for dependable efficiency analysis.

Weakly Supervised anomaly detection with privacy preservation under a Bi-Level Federated learning framework

Training a Machine Learning (ML) model in the industrial field faces special challenges, such as data privacy, lack of data, data imbalance, and unlabeled data. Therefore, it is not realistic to gather production data directly from various companies and use them to train a machine learning model. In this paper, we proposed a novel framework named Bi-level Federated Learning (BFL) to tackle the above challenges. In the first level, a Weakly Supervised Anomaly Detection method named Pairwise Relation prediction-based Ordinal regression Network (PRO) is utilized for training a Deep Anomaly Detection (DAD) model under Federated Learning (FL) mechanism. According to the DAD model, anomalies are identified and labeled with ‘anomaly’ tags. In order to train a multi-classification model that can identify different types of defects not just anomalies, the anomalies are manually labeled according to their respective defect types. In the second level, labeled anomaly and normal data are applied to train a multi-classification model under the FL mechanism. A framework is proposed to classify defects in pre-baked carbon anodes. Its performance is verified through a real case study. The results show that the DAD model achieved an acceptable performance with a 0.988 recall and 0.866 F1 score. The multi-classification model has shown good performance on 0.942 accuracy, 0.913 precision, 0.903 recall, and 0.906 F1. The results of the BFL exhibit improved performance compared to our previous work and classical ML methods.

Synergies and trade‐offs quantification from regional waste policy to sustainable development goals: The case of Kyoto City

AbstractWaste management policies introduced at regional levels can have a bearing effect on sustainable development goal (SDG) achievement at such levels, promoting certain SDG targets, while hindering other SDG targets. This work introduces an empirical methodology for estimating the synergies and trade‐offs for a city‐level waste management policy with SDG achievements, taking the case of Kyoto City's extensive waste management policy package and its interactions with renewable energy (RE) development (SDG 7), decent economic growth (SDG 9), reduced inequalities (SDG 10), and sustainable consumption (SDG 12). Similar interactions at the national‐level are also enumerated with the justified indicators at the city‐level being extended to the national‐level. Through pairwise correlation, multiple correlation, and regression, four hypotheses for circular economy compatibility with SDG achievement were tested. It was found that manufacturing value and small business growth is stunted by circular economy‐based waste policies, while resource efficiency is promoted. Cultural and social structures have trade‐offs with stringent waste management at city‐levels, but is neutral at aggregated levels. RE development is also synergetic with circular economy‐based waste management. This methodology shows a pathway as to how national‐ and city‐level policies can be integrated with each other in order to optimize waste management and overall sustainability.

Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials

High energy and low sensitivity have been the focus of developing new energetic materials (EMs). However, there has been a lack of a quick and accurate method for evaluating the stability of diverse EMs. Here, we develop a machine learning prediction model with high accuracy for bond dissociation energy (BDE) of EMs. A reliable and representative BDE dataset of EMs is constructed by collecting 778 experimental energetic compounds and quantum mechanics calculation. To sufficiently characterize the BDE of EMs, a hybrid feature representation is proposed by coupling the local target bond into the global structure characteristics. To alleviate the limitation of the low dataset, pairwise difference regression is utilized as a data augmentation with the advantage of reducing systematic errors and improving diversity. Benefiting from these improvements, the XGBoost model achieves the best prediction accuracy with R2 of 0.98 and MAE of 8.8kJ mol-1, significantly outperforming competitive models.

Hemifield-based analysis of pattern electroretinography in normal subjects and patients with preperimetric glaucoma

This prospective cross-sectional study investigated the visual function of preperimetric glaucoma (PPG) patients based on hemifield (HF) pattern electroretinogram (PERG) amplitudes. Thirty-two (32) normal subjects and 33 PPG patients were enrolled in control and PPG groups, respectively. All of the participants had undergone full ophthalmic examinations, including spectral-domain optical coherence tomography (SD-OCT), visual field (VF) examination and pattern electroretinography (PERG). The PERG parameters along with the HF ratios of SD-OCT and PERG were compared between the control and PPG groups. Pairwise Pearson's correlation coefficients and linear regression models were fitted to investigate the correlations. The PERG N95 amplitudes were significantly lower in the PPG group (P < 0.001). The smaller/larger HF N95 amplitude ratio of the PPG group was found to be smaller than that of the control group (0.73 ± 0.20 vs. 0.86 ± 0.12; P = 0.003) and showed positive correlations with affected HF average ganglion cell-inner plexiform layer (GCIPL) thickness (r = 0.377, P = 0.034) and with average GCIPL thickness (r = 0.341, P = 0.005). The smaller/larger HF N95 amplitude ratio did not significantly change with age (β = − 0.005, P = 0.195), whereas the full-field N95 amplitude showed a negative correlation with age (β = − 0.081, P < 0.001). HF analysis of PERG N95 amplitudes might be particularly useful for patients with early glaucoma.

Forecasting the Impact of the Structure of Financing Sources on the Results of Production Activity in Agriculture

The study conducted to assess the impact of the source of financing on the results of production activities of small and medium-sized businesses in agriculture in Kazakhstan is presented in the article. When building the model, the authors used the method of correlation and regression analysis, including calculations of pairwise regression, as well as the method of building statistical equations. Statistical data on the volume of financing of SMEs in agriculture by way of own funds, government resources (concessional lending, subsidies, and leasing), and non-state sources of financing such as borrowed funds from banks, microfinance organizations, credit partnerships, and leasing companies were used as the factors. Firstly, the results of the study showed that such factors as bank lending and leasing financing by private companies do not have a significant impact on production volumes. Secondly, of all the factors analyzed, the greatest impact on gross output was made by financing by microfinance organizations and credit partnerships, which determines and indicates the need for the development of non-state sources of financing. Thirdly, a comprehensive approach combining state financial support as well as available non-state financial resources is needed to achieve the strategic objectives of economic development and ensure the required level of food security.

Corpora amylacea negatively correlate with hippocampal tau pathology in Alzheimer's disease.

Severity and distribution of aggregated tau and neurofibrillary tangles (NFT) are strongly correlated with the clinical presentation of Alzheimer's disease (AD). Clearance of aggregated tau could decrease the rate of NFT formation and delay AD onset. Recent studies implicate corpora amylacea (CA) as a regulator of onset or accumulation of tau pathology. Normally, CA clear brain waste products by amassing cellular debris, which are then extruded into the cerebrospinal fluid to be phagocytosed. The proper functioning of CA may slow progression of AD-associated NFT pathology, and this relationship may be influenced by amount and distribution of phospho-tau (pTau) produced, age, sex, and genetic risk. The goal of this study was to determine if CA size and number are associated with hippocampal location and local pTau severity while accounting for variations in age, sex, and genetic risk. Postmortem brain hippocampal tissue sections from 40 AD and 38 unaffected donors were immunohistochemically stained with AT8 (pTau) and counter stained with periodic acid Schiff (PAS). Stained sections of the CA1 and CA3 regions of the hippocampus were analyzed. The percent area occupied (%AO) of CA, pTau, and NFT was calculated. Pairwise comparisons and regression modeling were used to analyze the influence of age, pTau %AO, and genetic risk on %AO by CA in each region, separately in donors with AD and unaffected donors. CA %AO was significantly higher in the CA3 region compared to CA1 in both groups. A significant negative correlation of CA %AO with both pTau %AO and neurofibrillary tangle %AO in the CA3 region of AD brain donors was found. Regression analysis in the CA3 region revealed a significant negative association between CA with both pTau and age. We found an increase of CA in the CA3 region, compared to CA1 region, in AD and unaffected donors. This may suggest that the CA3 region is a hub for waste removal. Additionally, the negative correlation between %AO by CA and NFT in the CA3 region of the hippocampus in donors with AD suggests CA could play a role in AD pathologic progression by influencing tau clearance.

Genetic Susceptibility Profile of the Southern Indian Population of Hyderabad for Coronary Artery Disease

In order to portray the genetic susceptibility profile of the population of Hyderabad a prioritized set of 192 SNPs belonging to the 11q23.3, 9p21.3 and other CAD specific genomic regions were genotyped in a sample of ~500 cases and 500 controls. After data pruning, pair-wise logistic regression analysis of 148 CAD specific SNPs revealed significant association of 18 SNPs. The risk score analysis based on 18 SNPs and the ROC curve yielded an AUC of 0.822 suggesting substantial power of these SNPs to serve as predictive markers for CAD. Comparative analysis revealed certain SNPs belonging to APOA4-APOC3, APOA5-APOA4, BUD13, ZPR1, LPA and SMEK1 genes were associated significantly with the three phenotypic categories of CAD. The current study identified a unique set of SNPs to be associated with CAD and also portrays the distinct patterns of association of certain SNPs with the three major phenotypic sub categories of CAD in the population of Hyderabad.

Statistical Modeling of Implicit Functional Relations

This study considers the statistical estimation of relations presented by implicit functions. Such structures define mutual interconnections of variables rather than outcome variable dependence by predictor variables considered in regular regression analysis. For a simple case of two variables, pairwise regression modeling produces two different lines of each variable dependence using another variable, but building an implicit relation yields one invertible model composed of two simple regressions. Modeling an implicit linear relation for multiple variables can be expressed as a generalized eigenproblem of the covariance matrix of the variables in the metric of the covariance matrix of their errors. For unknown errors, this work describes their estimation by the residual errors of each variable in its regression by the other predictors. Then, the generalized eigenproblem can be reduced to the diagonalization of a special matrix built from the variables’ covariance matrix and its inversion. Numerical examples demonstrate the eigenvector solution’s good properties for building a unique equation of the relations between all variables. The proposed approach can be useful in practical regression modeling with all variables containing unobserved errors, which is a common situation for the applied problems.

Factor Model for The Migration Processes Development in Kazakhstan

Migration, being a reflection of socio-economic processes, processes in society, highlights issues related to the identification of the phenomena that cause it. The purpose of the article is to build a multifactorial model for the development of infectious processes. In the course of the study, the main factors affecting the interaction of processes in Kazakhstan in 1991-2021 were identified, a theoretical and methodological justification was given for the dependence (independence) of the regulation of processes on wages, formal and informal employment, assessment of the standard of living of the population, cultural and educational level development of the Kazakhstan population in their morbidity-temporal aspect. Correlation coefficients were calculated and the degree of analysis of indicators was estimated based on calculations of pairwise regression between variables using the SPSS 23 program.

Factor Model for The Migration Processes Development in Kazakhstan

Migration, being a reflection of socio-economic processes, processes in society, highlights issues related to the identification of the phenomena that cause it. The purpose of the article is to build a multifactorial model for the development of infectious processes. In the course of the study, the main factors affecting the interaction of processes in Kazakhstan in 1991-2021 were identified, a theoretical and methodological justification was given for the dependence (independence) of the regulation of processes on wages, formal and informal employment, assessment of the standard of living of the population, cultural and educational level development of the Kazakhstan population in their morbidity-temporal aspect. Correlation coefficients were calculated and the degree of analysis of indicators was estimated based on calculations of pairwise regression between variables using the SPSS 23 program.

Joint Local-Global Discriminative Subspace Transfer Learning for Facial Expression Recognition

Traditional facial expression recognition (FER) has achieved satisfactory results to some extent, and most of the current methods are trained and evaluated on a single database. However, in real applications, the training and testing images are often collected in different scenarios, which will lead to performance degeneration. To tackle this problem, in this paper, we propose a novel transfer learning approach, named joint local-global discriminative subspace transfer learning (LGDSTL), for cross-database FER. In LGDSTL, firstly, we develop a joint local-global graph as the distance metric, in which we not only consider the local discriminative geometric structure for each database, but also consider a global graph to transfer knowledge. In this way, the discrepancy between the two databases will be significantly reduced. Then, we present a pairwise regression function to guide the discriminative subspace transfer learning. Additionally, a data reconstruction constraint is introduced to preserve the main discriminative information. Finally, comparative studies on four popular benchmarks demonstrate the effectiveness of the proposed approach.

Psychological stress is associated with arterial inflammation in people living with treated HIV infection

Stress and depression are increasingly recognized as cerebrovascular risk factors, including among high stress populations such as people living with HIV infection (PLWH). Stress may contribute to stroke risk through activation of neural inflammatory pathways. In this cross-sectional study, we examined the relationships between stress, systemic and arterial inflammation, and metabolic activity in stress-related brain regions on 18F-fluorodeoxyglucose (FDG)-PET in PLWH. Participants were recruited from a parent trial evaluating the impact of alirocumab on radiologic markers of cardiovascular risk in people with treated HIV infection. We administered a stress battery to assess different forms of psychological stress, specifying the Perceived Stress Scale as the primary stress measure, and quantified plasma markers of inflammation and immune activation. Participants underwent FDG-PET of the brain, neck, and chest. Age- and sex-matched control participants without HIV infection were selected for brain FDG-PET comparisons. Among PLWH, we used nonparametric pairwise correlations, partial correlations, and linear regression to investigate the association between stress and 1) systemic inflammation; 2) atherosclerotic inflammation on FDG-PET; and metabolic activity in 3) brain regions in which glucose metabolism differed significantly by HIV serostatus; and 4) in a priori defined stress-responsive regions of interest (ROI) and stress-related neural network activity (i.e., ratio of amygdala to ventromedial prefrontal cortex or temporal lobe activity). We studied 37 PLWH (mean age 60 years, 97% men) and 29 control participants without HIV (mean age 62 years, 97% men). Among PLWH, stress was significantly correlated with systemic inflammation (r = 0.33, p = 0.041) and arterial inflammation in the carotid (r = 0.41, p = 0.023) independent of age, race/ethnicity, traditional vascular risk factors and health-related behaviors. In voxel-wise analyses, metabolic activity in a cluster corresponding to the anterior medial temporal lobes, including the bilateral amygdalae, was significantly lower in PLWH compared with controls. However, we did not find a significant positive relationship between stress and this cluster of decreased metabolic activity in PLWH, a priori defined stress-responsive ROI, or stress-related neural network activity. In conclusion, psychological stress was associated with systemic and carotid arterial inflammation in this group of PLWH with treated infection. These data provide preliminary evidence for a link between psychological stress, inflammation, and atherosclerosis as potential drivers of excess cerebrovascular risk among PLWH.

Assessments of the impacts of environmental factors on vegetation cover at gas flaring sites in the Niger Delta, Nigeria

This study aims at the assessment of the effects of environmental factors on vegetation cover at the flaring sites in Rivers State, Nigeria. Twenty one Landsat 7 Enhanced Thematic Mapper Plus (ETM) data, and four Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI-TIRS) data dated from 21/04/2000 to 05/02/2022 with 3 cloud cover were employed to study eleven gas flaring sites. Matrix Laboratory MATLAB code was developed for data processing and analysis. Normalized Differential Vegetation Index (NDVI) was computed from the atmospherically corrected multispectral bands (1-4) for Landsat 7, and bands (2-5) for Landsat 8. Change in NDVI was computed as NDVI450-60)m in the N, E, S and W directions which is the difference between NDVI at 450 m and 60 m distance from the flare stack. The available environmental factors (Facility size, flare stack height and time i.e. year, month and day) were applied to the (NDVI450-60m) in the 4 cardinal directions. Pairwise linear and multiple regression statistical analyses were adopted to investigate the relationships between each of the (NDVI450-60)mN, (NDVI450-60)mE, (NDVI450-60)mS and (NDVI450-60)mW and facility size, stack height and time. The results show that only 12 of the variance in (NDVI450-60)mN is explained by the available data. Therefore, it can be concluded that the combination of the facility size, flare stack height and time accounted for only 12 of the results. Further researches using the rate and volume of flared gas data, and the vegetation species is required to achieve better results.

U-shaped association of serum uric acid with all-cause mortality in patients with hyperlipidemia in the United States: a cohort study.

Serum uric acid (SUA) interferes with lipid metabolism and is considered an independent risk factor for atherosclerosis, a major complication in patients with hyperlipidemia. However, the effects of uric acid levels on mortality in hyperlipidemic patients has yet to be sufficiently determined. In this study, we aimed to assess the association between all-cause mortality and SUA in a hyperlipidemic population. To determine mortality rates, we obtained data for 20,038 hyperlipidemia patients from the U.S. National Health and Nutrition Examination Surveys (NHANES) 2001-2018 and National Death Index. To examine the all-cause mortality effect of SUA, multivariable Cox regression models, restricted cubic spline models, and two pairwise Cox regression models were used. Over a median follow-up of 9.4 years, a total of 2079 deaths occurred. Mortality was examined according to SUA level quintiles: <4.2, 4.3-4.9, 5.0-5.7, 5.8-6.5, and >6.6 mg/dl. In multivariable analysis using 5.8-6.5 mg/dl SUA as a reference, the hazard ratios (95% confidence interval) of all-cause mortality across the five groups were 1.24 (1.06-1.45), 1.19 (1.03-1.38), 1.07 (0.94-1.23), 1.00 (reference), and 1.29 (1.13-1.48), respectively. According to a restricted cubic spline, we noted a U-shaped relationship between SUA and all-cause mortality. The inflection point was approximately 6.30 mg/dl, with hazard ratios of 0.91 (0.85-0.97) and 1.22 (1.10-1.35) to the left and right of the inflection point, respectively. In both sexes, SUA was characterized by a U-shaped association, with inflection points at 6.5 and 6.0 mg/dl for males and females, respectively. Using nationally representative NHANES data, we identified a U-shaped association between SUA and all-cause mortality in participants with hyperlipidemia.