Partial Linear Regression Research Articles

Asymmetric effects of diurnal warming on carbon allocation to leaves of marsh wetlands on the Tibetan Plateau

ABSTRACT Using MODIS leaf area index and meteorological data from 2000 to 2020, this study analyzed the variations in carbon allocation to leaves (C leaf) of marsh during the leaf green-up period and their response to climate change across the TP based on partial correlation and linear regression analysis methods. The regionally averaged C leaf of marsh showed an increasing trend during the leaf green-up period from 2000 to 2020. Diurnal warming has asymmetric effects on C leaf of marsh during different stages of the leaf green-up period. During the early leaf green-up period, warming preseason daytime temperature significantly reduced C leaf in the southwestern region, while warming preseason daytime and night-time temperatures increased C leaf in the central region. During the late leaf green-up period, preseason temperatures exerted significantly positive and negative effects on C leaf in the southwestern and central regions, respectively. During the early leaf green-up period, preseason precipitation increases significantly enhanced C leaf in the southwestern region, but decreased C leaf in the central region. This study highlights the distinct impacts of climatic change on C leaf during different stages of the leaf green-up period and indicates that the asymmetric effects of diurnal warming should be considered in simulations of marsh carbon allocation in the future.

Contamination concerns and face consciousness in fashion-sharing services

PurposeThe commercial sharing service (CSS) represents an emerging business model in which users pay a minor fee to rent a product for a short period of time. Fashion CSSs enable individuals to rent various garments and accessories with the goal of enhancing one’s public image while saving money. Marketers have strived to popularize fashion CSSs, but concerns related to contamination have thwarted their efforts. Based on face consciousness theory, this research examines how consumers’ desire to enhance their public image (i.e. to “gain face”) can attenuate the negative impacts of contamination concerns and thus facilitate fashion CSS usage.Design/methodology/approachTwo scenario-based studies were conducted to collect data. Participants were recruited via online survey platforms in mainland China. The hypotheses were tested by partial least squares (PLS) path modeling and linear regression analysis.FindingsThe analysis results revealed a two-stage mediation model. Contamination concerns were found to inhibit consumers’ participation in fashion-sharing by increasing their perceived risk, which further decreased the perceived value of the CSS. However, consumers’ desire to gain face can mitigate the negative (direct and indirect) effects of contamination concerns on CSS usage, facilitating CSS adoption.Originality/valueOur findings suggest that eliciting consumers’ desire to gain face can promote fashion CSS usage and attenuate the negative impacts of contamination concerns. Moreover, consumers are less risk-averse and less concerned about shared pieces being contaminated when they seek to enhance their face through fashion products. Practical implications for fashion marketers are discussed.

Ecotoxicological risk assessment of active pharmaceutical ingredients (APIs) against different aquatic species leveraging intelligent consensus prediction and i-QSTTR modeling

The increasing presence of active pharmaceutical ingredients (APIs) in aquatic ecosystems, driven by widespread human use, poses significant risks, including acute and chronic toxicity to aquatic species. However, the scarcity of experimental toxicity data on APIs and related compounds due to the high costs, time requirements, and ethical concerns associated with animal testing hinders comprehensive risk assessment. In response, we developed quantitative structure-toxicity relationship (QSTR) and interspecies quantitative structure toxicity-toxicity relationship (i-QSTTR) models for three key aquatic species: zebrafish, water fleas, and green algae, using NOEC as an endpoint, following OECD guidelines. Algae, daphnia, and fish, recognized as standard organisms in toxicity testing, are crucial bio-indicators due to their size, transparency, adaptability, and regulatory acceptance. We used partial least squares (PLS) and multiple linear regression (MLR) methods for model development alongside machine learning techniques such as Random Forest (RF), Support Vector Machines (SVM), K-nearest Neighbor (kNN), and Neural Networks (NN) to enhance the predictivity. Lipophilicity, electronegativity, unsaturation, a molecular cyclized degree in molecular structure, large fragments, aliphatic secondary C(sp2), and R–CR–R groups were identified as critical biomarkers for API toxicity. Screening of the PPDB (pesticide properties databases) and DrugBank validated the practical application of these models, offering valuable tools for regulatory decisions, safer API design, and the preservation of aquatic biodiversity.

Genome-wide profiling of long non-coding RNA following ozone exposure: A randomized, controlled exposure trial

BackgroundExposure to ambient ozone has been associated with extrapulmonary health, but the underlying mechanisms remain to be understood. LncRNAs are involved in the regulation of gene expression, but their regulatory mechanisms in ozone-related health effects are scarcely explored. ObjectiveTo investigate genome-wide lncRNA changes after short-term ozone exposure and their regulatory roles in ozone exposure and gene expression. MethodWe conducted a randomized, crossover, controlled exposure trial in 32 healthy college students in Shanghai, China. Each participant received both 200-ppb ozone exposure and filtered air exposure for 2 h in a random order with a 14-day washout period. Blood samples were collected after each exposure and used for lncRNA sequencing. Differentially expressed lncRNAs between the two exposures were identified using orthogonal partial least squares discriminant analysis and linear regression analysis. LncRNAs-targeted mRNAs were mapped and subjected to enrichment analyses. We also constructed lncRNA-miRNA-mRNA networks. ResultsA total of 90 lncRNAs were differentially expressed after exposure to ozone, with 49 up-regulated and 41 down-regulated. Enrichment analyses suggested that these dysregulated lncRNAs were involved in a variety of biological processes, including those related to oxidative stress, inflammation response, and cell proliferation, development, and differentiation. Multiple pathways such as IL-17 signaling, NF-kB signaling, and Rho GTPases signaling were also enriched. Furthermore, the lncRNA-miRNA-mRNA network revealed that specific lncRNAs may regulate the expression of inflammation- and angiogenesis-related genes by interacting with miRNAs, such as NEAT1/hsa-miR-500a-3p/SIGLEC8, NEAT1/hsa-miR-6835–3p/SLC16A14, OIP5-AS1/miR-183–5p/EGR1, and SNHG25/hsa-miR-663a/FOSB axes. ConclusionThis study characterized a thorough profile of human lncRNAs following short-term ozone exposure and suggested the regulatory roles of these lncRNAs in ozone-induced inflammatory responses and angiogenesis, providing novel epigenetic insights into the mechanisms of the health effects of ozone exposure.

Full connected layer model with self-attention to hourly predict heparin dosage for perioperative cardiac surgery patients

BackgroundAnticoagulant therapy for patients who underwent cardiac surgery is a challenge. Both inadequate and excessive anticoagulation can cause fatal complications. Previous studies failed to provide real-time guidance for heparin pump speed adjustment. This study intended to provide a quantitative prediction model to optimize heparin dosage for cardiac surgery patients based on machine learning method. MethodsPatients who underwent cardiac surgery and admitted to intensive care unit in Peking Union Medical College Hospital (PUMCH) from January 2013 to December 2023 were retrospectively analyzed. In order to reach target activated partial thromboplastin time (aPTT), linear regression, SVM, XGBoost, LSTM, GRU, FC (Full Connected Layer) and FC + self-attention models were used to make hourly adjustment recommendation for administrations of heparin pump speed. Mean absolute square, and absolute percentage errors were used to evaluate the reliability of the models. SHAP method and feature cumulative effect were used to interpret the features of the FC + self-attention model. Safety and economic evaluation based on clinical compliance of this real-world data-oriented model was further analyzed. ResultsA total of 1080 patients including 112,554 heparin pump administrations were included in this study. Among seven candidate models, FC + self-attention model yielded the lowest mean absolute error of 0.9388 and 1.1325 in test and validation cohort. Gap to target aPTT, thrombin time, history of coronary heart disease, previous duration of arterial fibrillation and prothrombin activity were identified as important features affecting heparin adjustment. High compliance to FC + self-attention model may increase percentage of normal therapeutic time and decrease supratherapeutic therapeutic time and reducing blood draw until two consecutive normal therapeutic stabilization of aPTT. ConclusionsThis FC + self-attention model is potentially applicable for giving recommendation for healthcare providers to optimize heparin dosage for cardiac surgery patients.

Glymphatic function assessment with diffusion tensor imaging along the perivascular space in patients with major depressive disorder and its relation to cerebral white-matter alteration.

The link between glymphatic system function in the brain and alterations in white-matter microstructure among individuals with major depressive disorder (MDD) remains unclear. This study aimed to examine the assessment of glymphatic system function in patients with MDD using the diffusion tensor imaging along the perivascular space (DTI-ALPS) index and to evaluate its association with cerebral-white-matter abnormalities and neuropsychological scores. From February 2023 to November 2023, this cross-sectional study recruited 35 patients with MDD from the Psychosomatic Diseases Department of the First Affiliated Hospital of Dalian Medical University. In this time period, 23 healthy controls (HCs) were enlisted from the community and matched with the MDD cohort in terms of years of education, gender, and age. All participants underwent magnetic resonance imaging, depression, anxiety, and cognitive assessments. The tract-based spatial statistics (TBSS) analyzed DTI parameters and identified significant clusters. Automated fiber quantification (AFQ) was used to automatically identify fiber bundles with statistical differences. Mann-Whitney tests or two-sample t-tests were used for comparisons. Interobserver consistency of the DTI-ALPS measurements was evaluated using the interclass correlation coefficient (ICC). Partial correlation analyses and linear regression analyses were used to examine relationships. A comparison of the DTI-ALPS index was made between the two groups. Correlations among diffusion characteristics, neuropsychological scores, and the DTI-ALPS index were analyzed. Compared to HCs, patients with MDD exhibited a lower DTI-ALPS score (P=0.001). According to using linear regression analysis, the ALPS index was found to be an independent predictor of the Hamilton Depression Rating Scale [B=-25.32; P=0.001; 95% confidence interval (CI): -40.35 to -11.55], Hamilton Anxiety Rating Scale (B=-33.48; P=0.003; 95% CI: -55.38 to -11.24), and Montreal Cognitive Assessment total score (B=8.59; P=0.008; 95% CI: 2.38 to 14.79). According to the TBSS analysis, there were clusters of increased axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) in patients with MDD as compared to HCs (all P values <0.05). A lower DTI-ALPS score was correlated with higher AD (r=-0.592; P<0.001), MD (cluster 1: r=-0.567, P=0.001; cluster 2: r=-0.581, P<0.001), and RD (r=-0.491; P=0.004) values. AFQ analysis identified the significantly different diffusion indicators in the left cingulum bundle (CB_L), left inferior longitudinal fasciculus (ILF_L), and left uncinate fasciculus (UF_L) between the two groups (all false discovery rate P values <0.05). DTI-ALPS score was negatively correlated with the AD value of CB_L (r=-0.304; P=0.024), ILF_L (r=-0.35; P=0.008), and UF_L (r=-0.354; P=0.008) in AFQ tract-level analysis. In point-wise analysis, the MD value of CB_L at nodes 33 to 36 was negatively correlated with DTI-ALPS score (r ranging from -0.504 to -0.535; P<0.01). Our results indicated a decrease in DTI-ALPS index score in patients with MDD. DTI-ALPS score was associated with depression, anxiety, declined cognitive ability, and white-matter microstructural abnormalities and may thus be a promising biomarker for the partial evaluation of glymphatic system function in patients with MDD.

High-precision estimation of plant alpha diversity in different ecosystems based on Sentinel-2 data

At present, the accuracy of remote sensing estimation models of plant alpha diversity is generally low, and high-precision estimation models in deciduous broadleaved forest (DBF), deciduous coniferous forest (DCF) and evergreen coniferous forest (ECF) are still lacking. The main purpose of this study is to construct high-precision remote sensing models for plant alpha diversity in multiple ecosystems at global scale. Normalized Difference Vegetation Index (NDVI) were derived from Sentinel-2 data. NDVI and NDVI based spectral diversity/heterogeneity indices were selected as predictive variables, and alpha diversity indices were selected as response variables. Simple linear regression (SLR), partial linear regression (PLSR), and random forest (RF) models were used to evaluate the predictive ability of the predictive variables against the response variables under six ecosystems (evergreen broadleaved forest (EBF), DBF, ECF, DCF, shrub, and grassland), and to compare the estimated robustness of various spectral diversity indices. In terms of prediction accuracy, the SLR models were the worst, and the PLSR model were average. RF performed best, outperforming most current models. Especially in DBF, ECF, shrub and grassland, the determination coefficient R2 of RF models can be as high as 0.9. In terms of the prediction of α-diversity, the prediction effect of species richness was better than that of Shannon index, Simpson index and Pielou index. The higher the vegetation complexity, the more accurate the assessment of vegetation α-diversity tends to be, especially in DBF, shrub and grassland. According to the importance of predictive variables and model stability evaluation results, NDVI, standard deviation of NDVI (SD), and NDVI derived Shannon’s diversity index (Sha), Cumulative Residual Entropy (CRE), Pielou’s evenness index (Pie), Hill’s numbers (Hill), Berger-Parker’s diversity index (Ber), Parametric Rao’s index of quadratic entropy(paRao) are all powerful indicators for predicting plant alpha diversity. Among them, the prediction performance of NDVI and SD is better. This study is not only an exploration of the practicability of R package “rasterdiv”, but also an attempt to construct high-precision remote sensing estimation models of plant alpha diversity at global scale.

Spatial and Temporal Dynamics in Vegetation Greenness and Its Response to Climate Change in the Tarim River Basin, China

Vegetation in ecologically sensitive regions has experienced significant alterations due to global climate change. The underlying mechanisms remain somewhat obscure owing to the spatial heterogeneity of influencing factors, particularly in the Tarim River Basin (TRB) in China. Therefore, this study targets the TRB, analyzing the spatial and temporal dynamics of vegetation greenness and its climatic determinants across multiple spatial scales. Utilizing Normalized Difference Vegetation Index (NDVI) data, vegetation greenness trends over the past 23 years were assessed, with future projections based on the Hurst exponent. Partial correlation and multiple linear regression analyses were employed to correlate NDVI with temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET), elucidating NDVI’s response to climatic variations. Results revealed that from 2000 to 2022, 90.1% of the TRB exhibited an increase in NDVI, with a significant overall trend of 0.032/decade (p &lt; 0.01). The difference in NDVI change across sub-basins and vegetation types highlighted the spatial disparity in greening. Notable greening predominantly occurred near rivers at lower elevations and in extensive cropland areas, with projections indicating continued greening in some regions. Conversely, future trends mainly suggested a shift towards browning, particularly in higher-elevation areas with minimal human influence. From 2000 to 2022, the TRB experienced a gradual increase in TMP, PRE, and PET. The latter two factors were significantly correlated with NDVI, indicating their substantial role in greening. However, vegetation sensitivity to climate change varied across sub-basins, vegetation types, and elevations, likely due to differences in plant characteristics, hydrothermal conditions, and human disturbances. Despite climate change influencing vegetation dynamics in 51.5% of the TRB, its impact accounted for only 25% of the total NDVI trend. These findings enhance the understanding of vegetation ecosystems in arid regions and provide a scientific basis for developing ecological protection strategies in the TRB.

Duration mismatch negativity under varying deviant conditions in individuals with high schizotypal traits.

Although impaired auditory mismatch negativity (MMN) has consistently been found in individuals with schizophrenia, there are few and inconsistent reports on nonclinical individuals with schizotypy. To date, no studies have thoroughly assessed MMN with different degrees of deviant oddballs in nonclinical schizotypal samples. The aim of this study was to examine the extent of duration MMN (dMMN) amplitudes under two deviant duration conditions (large and small) in nonclinical participants with high schizotypal traits. An extreme-group design was utilized, in which 63 participants from the schizotypy and control groups were selected from a pool of 1519 young adults using the Schizotypal Personality Questionnaire (SPQ). MMN was measured using passive duration oddball paradigms. Basic demographic information and musical backgrounds were assessed and matched, while depression and anxiety were evaluated and controlled for. The repeated measures analysis of covariance was utilized to evaluate differences in dMMN between groups. The Bonferroni correction was applied for multiple comparisons. Partial correlation and multiple linear regression analyses were conducted to investigate the association between dMMN amplitudes and SPQ scores. The amplitudes of dMMN at Cz were significantly increased under the large deviance condition in nonclinical schizotypal individuals (F = 4.36, p = .04). Large-deviance dMMN amplitudes at Fz were positively correlated with mild cognitive-perceptual symptoms in the control group (rp = .42, p = .03). However, as schizophrenia-like symptoms worsened and approached the clinical threshold for schizophrenia, small-deviance dMMN amplitudes at Cz showed negative associations with the cognitive-perceptual factor in the schizotypy group (rp = -.40, p = .04). These results suggest the importance of considering the degree of deviation in duration when implementing the auditory oddball paradigm among nonclinical participants with schizotypal traits. In addition, our findings reveal a potential non-linear relationship between bottom-up auditory processing and the positive dimension of the schizophrenia spectrum.

Comparison of Some Robust Methods of Difference Based Ridge Estimator in Semiparametric Regression Model

The semiparametric regression models have received a lot of attention from researchers recently because it combines parametric and nonparametric methods, it is one of the advanced topics in data analysis for various studies, which aims to find the best capabilities and a high level of efficiency. One of the most important semiparametric regression models is the partial linear regression model (PLM), which consists of a parametric component and a nonparametric component, for the purpose of estimating the parametric component, the difference method will be used to remove the nonparametric component. When the analysis hypotheses of the parametric component are not fulfilled, it will suffer from several problems, the most important of which is the problem of complete multicollinearity, besides the multicollinearity, there are also outliers in the data. In this research, the problems of multicollinearity and outliers of the semiparametric regression model were addressed, where simulation was used to generate data with different sample sizes and for different correlations and outlier ratios and for different methods such as [Difference Ridge based M robust with Nadaraya – Watson (DRMNW), Difference Ridge based S robust with Nadaraya – Watson (DRSNW), Difference Ridge based M robust with Smoothing spline (DRMSP), Difference Ridge based S robust with Smoothing spline (DRSSP)], the results showed that method Difference Ridge based M robust with Smoothing spline (DRMSP) is the best estimator.

Revealing the effects and mechanism of wine processing on Corni Fructus using chemical characterization integrated with multi-dimensional analyses

Corni fructus (CF) is always subjected to wine processing before prescription in clinic, for an enhancing effect of nourishing liver and kidney. While, the underlying mechanism for this processing on CF remains obscure. In this study, a sensitive ultra-high-performance liquid chromatography mass spectrometry (UPLC-MS/MS) method combined multi-dimensional analyses was established to monitor chemical characterizations of raw and wine-processed CF (WCF) and hence reveal the effects and underlying mechanism of wine processing on CF. As indicated, a total of 216 compounds were tentatively identified, including 98 structurally complex and variable home/hetero-polymers, that were composed of iridoid glucosides, gallic acids, caffeic acid and/or 5-HMF. Interestingly, 53 of these compounds probably characterized potential novel, including 35 iridoid glucosides or their dimers, 9 iridoid glucoside-gallic acid dimers, 7 gallic acids derivatives and 2 gallic acid-caffeic acid dimers, which provides ideas for natural product researchers. Meanwhile, the multi-dimensional analyses including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and linear regression analysis were used to explore the differences between CF and WCF. The results showed that 23 compounds as chemical markers greatly contributing to the distinction were screened out, and 3 of which (7α/β-O-ethyl-morroniside, gallic acid and 5-HMF) in WCF indicated an increasing trend in intensities in relative to those in CF. Additionally, linear regression analysis showed that in WCF 53 compounds exhibited an increasing in intensities, while 132 ones did a decreasing trend, compared with those in CF. As our investigation demonstrated, acetal reaction of morroniside, ester hydrolysis in different organic acid derivatives as well as glycoside bond cleavage during wine processing probably resulted in the distinctions. The findings of this study provide a further understanding of the effect and mechanism of wine processing on CF.

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.

THE INFLUENCE OF BRAND AWARENESS, PERCEIVED QUALITY, BRAND ASSOCIATIONS, AND BRAND LOYALTY ON THE BRAND EQUITY OF OPPO SMARTPHONES

Brand equity is a set of assets and values associated with a brand. Several factors influence it, namely brand awareness, perceived quality, brand associations, and brand loyalty. This research is expected to provide insight to understand the influence of brand awareness, perceived quality, brand associations, and brand loyalty on the brand equity of Oppo smartphones. This research used purposive sampling as the sampling method, the questionnaire as the data collection method, and distributed to 145 students of the Faculty of Business, PGRI University of Yogyakarta. The data were evaluated using validity and reliability tests and analyzed with partial, simultaneous, and multiple linear regression tests. Partial positive impact is shown by brand awareness, brand association, and brand loyalty on brand equity. However, perceived quality has a negative effect on brand equity. The simultaneous impact is shown by brand awareness, perceived quality, brand association, and brand loyalty on the brand equity of the Oppo smartphone. However, this research has a small sample size and limited variables used; future research is expected to expand the sample size and include new variables.

Unraveling patterns, causes, and nature-based remediation strategy for non-grain production on farmland in hilly regions

The surge in non-grain production on farmland (NGPF) poses significant threats to food security and land sustainability, particularly in hilly regions. However, there remains a lack of clarity on how to effectively balance grain and non-grain production in relation to land remediation. Using Wannian County as a case study, we investigate the evolution of this by leveraging high-precision land surveys and satellite imagery. Through the application of bootstrapped partial linear regression models, we identify key influencers behind each type of NGPF. In proposing land remediation solutions, we integrate the results of NGPF and land quality evaluations to identify mismatches between non-grain production and land attributes (i.e., topography, geology, soil, and land use). Our findings reveal a substantial growth in NGPF, expanding from 3838.72 ha to 5659.64 ha (2010–2020), and predominantly occurring on farmland with favorable natural conditions and connected locations such as proximity to roads, town centers, and industrial plants. Surprisingly, the basic farmland protection policy shows limited effectiveness in curbing NGPF, except for garden operations. We identify 1674 NGPF patches suitable for conversion to grain production and provide land remediation suggestions tailored to low-quality farmland with specific natural barriers, thus complementing the demand for regional non-grain production. This study thereby innovatively proposes nature-based land remediation strategies to address the non-grain production dilemma by tailoring NGPF and land quality, offering valuable insights for sustainable farmland management in China and beyond.

Assessing the Application of Near-Infrared Spectroscopy to Determine Saccharification Efficiency of Corn Biomass

Nowadays, in the bioethanol production process, improving the simplicity and yield of cell wall saccharification procedure represent the main technical hurdles to overcome. This work evaluated the application of a rapid and cost-effective technology such as near -infrared spectroscopy (NIRS) for easily predict saccharification efficiency from corn stover biomass. Calibration process focussing on the number of samples and the genetic background of the maize inbred lines were tested; while Modified Partial Least Squares Regression (MPLS) and Multiple Linear Regression (MLR) were assessed in predictions. The predictive capacity of the NIRS models was mainly determined by the coefficient of determination (r2ev) and the index of prediction to deviation (RPDev) in external validation. Overall, we could check a better efficiency of the NIRS calibration process for saccharification using larger number of observations (1500 sample set) and genetic backgrounds; while MPLS regression provided better prediction statistics (r2ev = 0.80; RPDev = 2.21) compared to MLR (r2ev = 0.68; RPDev = 1.75). These results indicate that NIRS could be successfully implemented as a large-phenotyping tool in order to test the saccharification potential of corn biomass.

Local linear-$k$NN smoothing for semi-functional partial linear regression

The aim of this paper is to study a semi-functional partial linear regression model. The estimators are constructed by $k$-nearest neighbors local linear method. Some asymptotic results are established for an i.i.d sample under certain conditions, including asymptotic normality of the parametric component and the almost certain convergence (with rate) of the non-parametric component. Lastly, using cross-validation, the performances of our estimation method are presented on simulated data and on real data by comparing them with other known approaches for semi-functional partial linear regression models.

Targeted metabolomic profiles of serum amino acids are independently correlated with malnutrition in older adults.

Malnutrition is a common geriatric syndrome that is closely associated with adverse clinical outcomes and poses significant harm to older adults. Early assessment of nutritional status plays a crucial role in preventing and intervening in cases of malnutrition. However, there is currently a lack of measurable methods and biomarkers to evaluate malnutrition in older adults accurately. The aim of this study is to investigate the independent correlation between serum levels of amino acids and malnutrition in older adults, and to identify effective metabolomics biomarkers that can aid in the early detection of geriatric malnutrition. A total of 254 geriatric medical examination participants from Beijing Hospital were included in the study, consisting of 182 individuals with normal nutritional status (Normal group) and 72 patients at risk of malnutrition or already malnourished (MN group). Malnutrition was assessed using the Mini-Nutritional Assessment Short-Form (MNA-SF). Demographic data were collected, and muscle-related and lipid indexes were determined. Serum amino acid concentrations were measured using isotope dilution liquid chromatography-tandem mass spectrometry (LC-MS/MS). The correlation between serum amino acid levels and malnutrition was analyzed using non-parametric tests, partial correlation analysis, linear regression, and logistic regression. The geriatric MN group exhibited significantly lower serum aromatic amino acid levels (P < 0.05) compared to the normal group. A positive correlation was observed between serum aromatic amino acid levels and the MNA-SF score (P = 0.002), as well as with known biomarkers of malnutrition such as body mass index (BMI) (P < 0.001) and hemoglobin (HGB) (P = 0.005). Multivariable logistic or linear regression analyses showed that aromatic amino acid levels were negatively correlated with MN and positively correlated with the MNA-SF score, after adjusting for some confounding factors, such as age, gender, BMI, smoking status, history of dyslipidemia, diabetes mellitus and frailty. Stratified analyses revealed that these trends were more pronounced in individuals without a history of frailty compared to those with a history of frailty, and there was an interaction between aromatic amino acid levels and frailty history (P = 0.004). Our study suggests that serum aromatic amino acids are independently associated with malnutrition in older adults. These results have important implications for identifying potential biomarkers to predict geriatric malnutrition or monitor its progression and severity, as malnutrition can result in poor clinical outcomes.

Sex-specific associations of plasma neutrophil gelatinase-associated lipocalin (NGAL) with cognition in patients with drug-naïve schizophrenia

This present study aimed to investigate the sex-specific association of plasma neutrophil gelatinase-associated lipocalin (NGAL) with cognition in drug-naïve schizophrenia patients for the first time. A total of 204 participants in this study, including 137 drug-naïve schizophrenia (DNS) patients and 67 healthy controls (HCs). All participants completed the Measurements and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB), and were collected fasting venous blood for NGAL measurement. DNS patients also complete the Positive and Negative Syndrome Scale (PANSS). Partial correlation analysis and multiple linear regression were used to explore sex-specific associations between NGAL and cognition. All dimensions of MCCB scores were significantly lower in both male and female DNS patients than HCs. Sex differences were significant in cognitive performance in both DNS patients and HCs. Female DNS patients experienced poorer working memory and reason& problem solving than male patients. Female HCs performed a better attention/vigilance and visual learning, a poorer reason& problem solving than male HCs. In patients with DNS, NGAL levels were negatively associated with positive subscale of PANSS and positively associated with working memory and visual learning only in female. However, there was no significant correlation between NGAL levels and all cognitive tests in both male and female HCs. Regression model showed that higher level of NGAL was an independent protective factor for cognitive performance in female patients with DNS, whereas there was no such role in male patients. Our findings suggest sex specificity between NGAL levels and cognitive performance in DNS patients.

FACTORS AFFECTING TAX REVENUE AT KPP PRATAMA TERNATE CITY

Taxes have an important role in the life of the country and the implementation of development. Tax revenues are very much needed for this. Revenue that does not meet the target will certainly be an obstacle in the country's development process. The purpose of this research is to determine and analyze the influence of E-Commerce Transactions, Tax Audits, Information Technology, Account Representative Professionalism and E-Tax Applications on Tax Revenue at KPP Pratama Ternate City. The population in this study are individual taxpayers registered at KPP Pratama Ternate City. The sampling method used was simple random sampling. with a total sample of 399 respondents and using primary data. The test tool used is partial linear regression analysis (Partial Least Square/PLS). The research results show that e-commerce transactions have no effect on tax revenues. Meanwhile, audits, information technology, professionalism of account representatives and e-tax applications influence tax revenues.

Semi-Functional Partial Linear Quantile Regression Model with Randomly Censored Responses

Semi-Functional Partial Linear Quantile Regression Model with Randomly Censored Responses