Multiple Weight Research Articles

Leveraging genetic ancestry continuum information to interpolate PRS for admixed populations.

The relatively low representation of admixed populations in both discovery and fine-tuning individual-level datasets limits polygenic risk score (PRS) development and equitable clinical translation for admixed populations. Under the assumption that the most informative PRS weight for a homogeneous sample varies linearly in an ancestry continuum space, we introduce a Genetic Dis tance-assisted PRS Co mbination Pipeline for Div erse Genetic A ncestrie s ( DiscoDivas ) to interpolate a harmonized PRS for diverse, especially admixed, ancestries, leveraging multiple PRS weights fine-tuned within single-ancestry samples and genetic distance. DiscoDivas treats ancestry as a continuous variable and does not require shifting between different models when calculating PRS for different ancestries. We generated PRS with DiscoDivas and the current conventional method, i.e. fine-tuning multiple GWAS PRS using the matched or similar ancestry samples. DiscoDivas generated a harmonized PRS of the accuracy comparable to or higher than the conventional approach, with the greatest advantage exhibited in admixed individuals.

Neuron‐Inspired Biomolecular Memcapacitors Formed Using Droplet Interface Bilayer Networks

AbstractBrain‐inspired (or neuromorphic) computing circumvents costly bottlenecks in conventional Von Neumann architectures by collocating memory and processing. This is accomplished through dynamic material architectures, strengthening or weakening internal conduction pathways similar to synaptic connections within the brain. A new class of neuromorphic materials approximates synaptic interfaces using lipid membranes assembled via the droplet interface bilayer (DIB) technique. These DIB membranes have been studied as novel memristors or memcapacitors owing to the soft, reconfigurable nature of both the lipid membrane geometry and the embedded ion‐conducting channels. In this work, a biomolecular approach to neuromorphic materials is expanded from model synapses to a charge‐integrating model neuron. In these serial membrane networks, it is possible to create distributions of voltage‐sensitive gates capable of trapping ionic charge. This trapped charge creates transmembrane potential differences that drive changes in the system's net capacitance through electrowetting, providing a synaptic weight that changes in response to the history and timing of input signals. This fundamental change from interfacial memory (dimensions of the membrane) to internal memory (charge trapped within the droplets) provides a functional plasticity capable of multiple weights, longer‐term retention roughly an order of magnitude greater than memory stored in the membranes alone, and programming‐erasure.

Social and economic patterning in UPF intake in toddlerhood and middle childhood: longitudinal data from the UK Gemini cohort.

Children's consumption of ultra-processed food (UPF) may contribute to inequalities in obesity and wider health. Socioeconomic patterning in younger UK children's UPF intake is unknown. To investigate socioeconomic patterning of UK toddlers' (21-months) and children's (7-years) UPF intake across several household and neighbourhood indicators. Secondary analysis of data from a prospective longitudinal cohort study using parent-report sociodemographic data and 3-day diet diaries. /setting: Participants were children from the UK Gemini study of n=4,804 twins born in 2007. At 21-months and 7-years, n=2,591 and n=592 children had at least 2-days of dietary data, respectively. Percentage energy from UPF at 21-months and 7-years-of-age, classified using the NOVA system. Unadjusted linear regression models were run for household socioeconomic position (SEP) composite score, Index of multiple deprivation decile, income, occupation level, mother's age, education of mother and partner, child's ethnicity, sex, and age. Adjusted multivariable linear regression models were adjusted for ethnicity and all SEP indicators except SEP composite score (Adjusted 1), in addition to child sex and age (Adjusted 2). Missing data were addressed with multiple imputation and inverse probability weighting. Confidence intervals and P-values were adjusted to account for clustering within families. Children of lower SEP had higher UPF intake across several indicators. Mother's education was the strongest predictor, with postgraduate education associated with 8.64% (95% CI -12.08 to -5.20; P<0.001) and 10.12% (95% CI -15.68 to -4.56; P<0.001) less energy from UPF at 21-months and 7-years, respectively, compared to no educational qualifications in Adjusted model 2. UK children from more disadvantaged backgrounds consumed a greater proportion of their energy from UPF. Mother's education seemed the most influential factor. Socioeconomic inequalities, particularly in maternal education, may drive disparities in diet quality and associated health outcomes. Addressing these gaps is essential to reduce childhood obesity and improve long-term health in socioeconomically disadvantaged populations.

Hybrid quantum neural network based on weight remapping and its applications

Abstract In order to explore the possibility of cross-fertilization between quantum computing and neural networks, and to analyse the effects of multiple weight remapping functions on the model performance, this article proposes two hybrid models based on weight remapping: the hybrid quantum neural network (HQNN-WR) and the hybrid quantum convolutional neural network (HQCNN-WR). The HQNN-WR model uses a two-layer fully connected network to process the input features, performs feature transformation by applying multiple weight remapping functions, and subsequently passes the processed features to the quantum neural network for computation, and finally outputs the classification results. The experimental results show that the model significantly improves the classification accuracy on Iris, Wine and Breast datasets and the convergence speed is faster. The HQCNN-WR model integrates convolutional layers, pooling layers, and fully connected layers, and prevents over-fitting through a dropout layer, and exhibits excellent performance in binary classification tasks on MNIST and KMNIST datasets. The model effectively mitigates the over-fitting problem on small sample datasets and enhances the robustness and generalization ability of the model while improving the digit recognition accuracy. By comparing different models, this article also demonstrates their significant effects on the performance of hybrid quantum neural networks, providing a new theoretical basis and experimental support for the optimization and application of quantum machine learning methods.

Analysis of the Driving Stability of a Multi-Model Driven Electric-Tracked Intelligent Detection Device

The electric-tracked intelligent detection device is extensively used in engineering exploration and construction. However, in unstructured and complex terrains, its stability is challenged by a high center of gravity and poor road conditions, making the enhancement of its stability analysis crucial. This study is based on tracked vehicle models, constructing kinematic and dynamic equations for the device, and developing virtual and physical prototypes. Combining theoretical analysis, virtual simulations, and physical testing, the study analyzes motion parameters under typical conditions through numerical calculations and simulations. It examines the device’s dynamic characteristics during rapid turns in unstructured terrains. The research identifies variations in motion parameters, determines dynamic stability thresholds, and refines stability analysis methods applicable to the device, establishing evaluation criteria with multiple parameters and weight vectors. In conclusion, the developed models and prototypes help us understand the electric-tracked intelligent detection device’s dynamic characteristics and instability thresholds, enhancing its stability and operational efficiency and providing a theoretical foundation for related engineering machinery development.

Quantifying years of life lost in Australia: a multiple cause of death analysis.

Deaths in Australia and other high-income countries increasingly involve multiple conditions. However, key burden of disease measures typically only use the underlying cause of death (UC). We quantified sex and cause-specific years of life lost (YLL) based on UC compared with a method integrating multiple causes of death. Causes of death for all deaths in Australia (2015-17), mapped to 136 groups based on International Classification of Diseases 10th revision (ICD-10), were ascribed using (1) the UC only and (2) a multiple cause weighting (WT) strategy. Applying the Global Burden of Disease 2010 life table, YLLUC and YLLWT rates were calculated for each sex and cause of death and compared using relative and absolute measures. All-cause YLL rates were 113.4/1000 for males and 79.9/1000 for females. Cancers, cardiovascular diseases, external causes, respiratory diseases and nervous system diseases were the five biggest contributors to YLL for each method. For the top 20 causes combined, YLLWT rates were 10% lower for males (YLLWT = 74.93/1000 vs YLLUC = 67.38/1000) and 7% lower for females (YLLWT = 51.34/1000; YLLUC = 47.90/1000); YLLWT rates were lower for ischaemic heart disease and all cancers, but higher for diabetes and dementia, and for chronic obstructive pulmonary disease in males. With multiple cause weighting, renal failure emerged among the top 20 causes of YLL, as did atrial fibrillation and hypertension among females. YLLWT rates for substance abuse, mood disorders, hypertension and schizophrenia were relatively high compared with YLLUC. The YLLWT metric highlights epidemiologically important conditions that are less often selected as the UC.

Comparative Study of Quantitative Removal Methods for Effects of Light Intensity Using Multiple Weights Conditions

Comparative Study of Quantitative Removal Methods for Effects of Light Intensity Using Multiple Weights Conditions

Changes Over Time in Short-Term Stroke Outcomes by Race-Ethnicity.

The objective of this study was to quantify trends (2008-2019) in stroke outcomes by race-ethnicity. Patients with ischemic stroke from a population-based study were interviewed at 90 days to assess outcomes. Linear regression with multiple imputation and inverse probability weighting was used to model trends. The median age was 66 years (n = 1,449); 61% were Mexican American (MA). QOL remained stable with no race-ethnic difference in trends (p for time*race-ethnicity interaction = 0.81). Neurologic outcomes improved for MA (p < 0.01) but not non-Hispanic White (NHW) persons with stroke (p = 0.23) with no race-ethnic difference in trends (p for interaction = 0.23). For functional outcomes, trends were stable and then improved in MA persons with stroke (p for interaction = 0.01), whereas trends were stable in NHW persons with stroke (p = 0.52). For cognitive outcomes, there was little change in NHW persons with stroke (p = 0.50); in MA persons with stroke, there was improvement followed by decline and then improvement (p = 0.03). No race-ethnic differences in trends in functional (p for interaction = 0.51) or cognitive (p for interaction = 0.21) outcomes were noted. Outcome improvements were noted in MA but not NHW persons with stroke; race-ethnic differences were not present in 2019. Understanding factors contributing to favorable trends in MA persons may be informative for improving outcomes in all persons.

Synchronization for Directed Multi-Level Complex Networks With Multiple Weights

Synchronization for Directed Multi-Level Complex Networks With Multiple Weights

Vaping and socioeconomic inequalities in smoking cessation and relapse: a longitudinal analysis of the UK Household Longitudinal Study

BackgroundSmoking is a key cause of socioeconomic health inequalities. Vaping is considered less harmful than smoking and has become a popular smoking cessation aid, and therefore has potential to reduce...

Межличностная чувствительность, страх негативной оценки внешности и стыд собственного тела у девочек-подростков с расстройствами пищевого поведения

&lt;p&gt;&lt;strong&gt;Study relevance.&lt;/strong&gt; Recent years have been marked by the rising frequency of eating disorders (EDs) and the growing popularity of various weight control behaviors in adolescents. Although numerous studies have shown that interpersonal functioning is impaired in people with EDs, personal traits reinforcing these impairments have not been studied enough. &lt;strong&gt;Objective&lt;/strong&gt;. The study focused on the relationship between interpersonal sensitivity, fear of negative appearance evaluation, body shame and weight control behaviors in clinical (n=54) and community (n=54) samples of adolescent girls (aged 13&amp;mdash;17). &lt;strong&gt;Method&lt;/strong&gt;. The participants filled out a survey and 3 measures in person. The measures included Interpersonal Sensitivity Measure (Boyce, Parker, 1989; Razvaliaeva, Polskaya, 2021), Fear of Negative Appearance Evaluation scale (Lundgren et al., 2004; Razvaliaeva, Polskaya, 2020), and Phenomenological Body Shame Scale-Revised (Siegel et al., 2021). &lt;strong&gt;Results&lt;/strong&gt;. Girls with EDs reported food restrictions, purging, body measurements and multiple weight control behaviors, whereas girls from the community sample reported engaging in sports as a weight control behavior. Fear of negative appearance evaluation, body shame and interpersonal sensitivity score significantly higher in girls with EDs; the first 2 traits are also related to numerous weight control behaviors. &lt;strong&gt;Conclusion&lt;/strong&gt;. The study was the first to show that interpersonal sensitivity, fear of negative appearance evaluation and body shame were more pronounced in Russian-speaking adolescent girls with EDs, especially in girls with numerous weight control behaviors.&lt;/p&gt;

Comparative effects of biological and targeted synthetic DMARDs on incident chronic kidney disease in patients with rheumatoid arthritis.

The impact of individual biological/targeted synthetic disease-modifying anti-rheumatic drug (b/tsDMARD) on kidney function in patients with rheumatoid arthritis (RA) remains unclear. This study aimed to determine the comparative effects of b/tsDMARDs on chronic kidney disease (CKD) incidence in patients with RA. This multicentre cohort study included patients with RA who had baseline estimated glomerular filtration rate (eGFR) of ≥ 60 mL/min/1.73 m2 and started a tumor necrosis factor inhibitor (TNFi), cytotoxic T lymphocyte-associated antigen-4-Ig (CTLA4-Ig), interleukin-6 receptor inhibitor (IL-6Ri), or Janus kinase inhibitor (JAKi) in Japan. Multiple propensity score-based inverse probability weighting (IPW) was used to adjust confounders. The incidence of CKD was compared among b/tsDMARDs using IPW mixed-effect Cox proportional hazards models and linear mixed-effect models with IPW examined trajectories of eGFR. Among 2187 patients with 3068 treatment courses and up to 11 years of follow-up, CKD occurred in 275 cases. Compared with the CTLA4-Ig group, the TNFi group had a significantly lower CKD incidence (hazard ratio [HR] 0.67, 95% confidence interval [CI] 0.46-0.97, p= 0.04), whereas the JAKi group had a significantly higher incidence (HR 2.16, 95% CI 1.23-3.79, p= 0.01). The trajectory of eGFR was significantly greater in the JAKi group than in the CTLA4-Ig group (CTLA4-Ig: -1.28 mL/min/1.73 m2/year, JAKi: -2.29 mL/min/1.73 m2/year, p< 0.001). TNFi use was associated with reduced CKD incidence, whereas JAKi showed a less protective association for kidney function in patients with RA.

Reducing Information and Selection Bias in EHR-Linked Biobanks via Genetics-Informed Multiple Imputation and Sample Weighting.

Electronic health records (EHRs) are valuable for public health and clinical research but are prone to many sources of bias, including missing data and non-probability selection. Missing data in EHRs is complex due to potential non-recording, fragmentation, or clinically informative absences. This study explores whether polygenic risk score (PRS)-informed multiple imputation for missing traits, combined with sample weighting, can mitigate missing data and selection biases in estimating disease-exposure associations. Simulations were conducted for missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) conditions under different sampling mechanisms. PRS-informed multiple imputation showed generally lower bias, particularly when combined with sample weighting. For example, in biased samples of 10,000 with exposure and outcome MAR data, PRS-informed imputation had lower percent bias (3.8%) and better coverage rate (0.883) compared to PRS-uninformed (4.5%; 0.877) and complete case analyses (10.3%; 0.784) in covariate-adjusted, weighted, multiple imputation scenarios. In a case study using Michigan Genomics Initiative (n=50,026) data, PRS-informed imputation aligned more closely with a sample-weighted All of Us-derived benchmark than analyses ignoring missing data and selection bias. Researchers should consider leveraging genetic data and sample weighting to address biases from missing data and non-probability sampling in biobanks.

GOG-MBSHO: multi-strategy fusion binary sea-horse optimizer with Gaussian transfer function for feature selection of cancer gene expression data

Cancer gene expression data has the characteristics of high-dimensional, multi-text and multi-classification. The problem of cancer subtype diagnosis can be solved by selecting the most representative and predictive genes from a large number of gene expression data. Feature selection technology can effectively reduce the dimension of data, which helps analyze the information on cancer gene expression data. A multi-strategy fusion binary sea-horse optimizer based on Gaussian transfer function (GOG-MBSHO) is proposed to solve the feature selection problem of cancer gene expression data. Firstly, the multi-strategy includes golden sine strategy, hippo escape strategy and multiple inertia weight strategies. The sea-horse optimizer with the golden sine strategy does not disrupt the structure of the original algorithm. Embedding the golden sine strategy within the spiral motion of the sea-horse optimizer enhances the movement of the algorithm and improves its global exploration and local exploitation capabilities. The hippo escape strategy is introduced for random selection, which avoids the algorithm from falling into local optima, increases the search diversity, and improves the optimization accuracy of the algorithm. The advantage of multiple inertial weight strategies is that dynamic exploitation and exploration can be carried out to accelerate the convergence speed and improve the performance of the algorithm. Then, the effectiveness of multi-strategy fusion was demonstrated by 15 UCI datasets. The simulation results show that the proposed Gaussian transfer function is better than the commonly used S-type and V-type transfer functions, which can improve the classification accuracy, effectively reduce the number of features, and obtain better fitness value. Finally, comparing with other binary swarm intelligent optimization algorithms on 15 cancer gene expression datasets, it is proved that the proposed GOG1-MBSHO has great advantages in the feature selection of cancer gene expression data.

Improving cardiovascular risk stratification with ECG-predicted risk factors in primary prevention

Abstract Background Cardiovascular risk- and screening scores guide clinical practice. Built upon simple risk factors, they could benefit from information on actual, subclinical cardiac changes. The ECG provides proxies for various cardiac states, which can be detected using machine learning. We systematically assess the added value of ECG-based cardiovascular risk factor estimates for six outcomes in two population-based cohorts: Study of Health in Pomerania (SHIP-Start) and UK Biobank (UKB). Methods We employed convolutional neural networks to analyze ECGs from the Hamburg City Health Study (HCHS) for the assessment of multiple risk factors, including age, weight, diabetes, atrial fibrillation (AF) and heart failure. Cox models were fitted on UKB and SHIP to predict 5 and 15 year time-to-event. Endpoints were death, cardiovascular death, AF, stroke, heart failure and myocardial infarction. Covariates were sex, age, body mass index, diabetes, hypertension and smoking (Cox-RF), ECG-predicted risk factors (Cox-AI), and a combination (Cox-RF/AI). Stratified Monte Carlo cross-validation was used as validation and groups of C-indices were compared using Mann-Whitney U tests. Results The study included 9103 HCHS participants (mean age 62.0±8.4 years, 51% women), 3546 SHIP participants (age 49.7±16.4, 51% women), and 32828 UKB participants (age 64±7.6 years, 52% women). Cox-AI performed comparable or better than Cox-RF for AF (SHIP: 0.88 vs. 0.84, UKB: 0.71 vs. 0.72) and heart failure (SHIP: 0.8 vs. 0.79, UKB: 0.76 vs. 0.75), but was less strong for the other outcomes. The combination of information on cardiovascular risk factors and ECG information (Cox-RF/AI) significantly improved the predictive ability compared to risk factors alone (Cox-RF) for AF (SHIP: 0.89 vs. 0.84, UKB: 0.76 vs. 0.72), cardiovascular death (SHIP: 0.90 vs. 0.89, UKB: 0.75 vs. 0.74) and heart failure (SHIP: 0.81 vs. 0.79, UKB: 0.80 vs. 0.75). Discussion ECG-based risk factors complement risk stratification by incorporating nuanced information on cardiac function and structure, improving risk prediction and thus possibly clinical decision making and patient outcomes.

Chemotherapeutic Drug Delivery Nanoplatform Development: From Physicochemical to Preclinical Evaluation.

Through this study, the synergistic behavior of small-molecular-weight, amphiphilic surfactant molecules and the triblock copolymer Pluronic 188 was extensively evaluated based on their ability to formulate nanocarriers with novel properties for the delivery of class II and IV (biopharmaceutical classification system) chemotherapeutic compounds. The combination of four different surfactants at multiple weight ratios and twelve initially formulated nanosystems resulted in four hybrid delivery platforms, which were further studied in terms of multiple physicochemical characteristics, as well as their stability in protein-rich media (fetal bovine serum/phosphate-buffer saline). Finally, we obtained a single final nanoformulation that exhibited a high loading capacity (%EE ≥ 75%) and a sustained drug release profile under physiological conditions (model drug methotrexate), without altering the original physicochemical characteristics of the carrier. With a mean hydrodynamic radius (Rh) of less than 70 nm, a polydispersity index of 0.219, and no protein complexation, the system is a suitable candidate for in vivo, intravenous, and/or intramuscular administration. The cytotoxicity and genotoxicity of both loaded and unloaded carriers were evaluated through the examination of the upregulation or downregulation of apoptosis-related pathways. Multiple conventional 2D and 3D spheroidal conformations were used for these assessments, including HEK293, HCT-116, and MCF-7 cell lines, the results of which stressed the safety and biocompatibility of the empty nanocarrier. Additionally, experiments on Caenorhabditis elegans were conducted to evaluate the system's in vivo toxicity, focusing on developmental stages, egg-laying behavior, and locomotion. Nanosystems studied in terms of chemotherapeutic encapsulation have mostly focused on the physiochemical aspect of the development of such novel delivery platforms, with only few exceptions proceeding step-by-step from cellular 2D to 3D to in vivo experimentation. The present study offers a holistic view of the behavior of such a novel system, advancing our understanding of the capabilities of polymeric/surfactant-based nanodelivery platforms.

A retrospective cohort analysis of factors influencing continuous antibiotic therapy with ampicillin

AimsThere are limited data on ampicillin/sulbactam, both for continuous infusion and for use in critically ill patients. We aimed to identify factors that help predict ampicillin plasma levels during continuous antibiotic therapy in intensive care patients. Main methodsWe retrospectively reviewed and retrieved a large dataset of patients who received continuous ampicillin infusion with therapeutic drug monitoring between 2015 and 2022. Patients initially received standard dosing (single shot of 2/1 g followed by continuous infusion of 6/3 g ampicillin/sulbactam per day), which was then adjusted based on the results of regular therapeutic drug monitoring and according to a target range of 30–60 mg/l (equivalent to four to eight times the minimum inhibitory concentration of ampicillin for Enterobacterales). Main results466 measurements from 225 patients (152 male, mean age 61 years) were analyzed. Initial measurements of ampicillin plasma levels were below the predefined optimal therapeutic range in 50 %, within the range in 30 % and above the range in 20 %. Target attainment increased to 70 % by the 4th measurement. There was a significant negative correlation between ampicillin plasma levels and estimated glomerular filtration rate (eGFR) (r = −0.74; p < 0.001) and, to a lesser extent, with height (r = −0.31; p < 0.001). Based on multiple linear regression, eGFR and body weight or height were the factors accounting for 63 % of the variability in the data. SignificanceTo optimise target achievement, ampicillin dosing in critically ill patients requires a personalized approach based on renal function. Importantly, patients with normal or augmented eGFR require higher standard doses of ampicillin/sulbactam.

Transactive energy management for CIES considering tri-level hybrid game-theoretic approaches with multiple weight allocation factors

In city integrated energy systems, to reduce the costs of energy production and usage, it is extremely important for different entities to design proper energy management approaches since they have diverse behaviours and benefits. The hierarchy of the system is naturally divided into three layers, and hybrid game theories are employed on the basis of the vertical master–slave and horizontal equal cooperation characteristics among energy providers, aggregators, and users. First, to reflect the interactions between different levels' entities, Stackelberg game theory is adopted vertically, and the model complexity is lessened by the KKT conditions. Then, for energy providers, the asymmetric bargaining game is improved by combining multiple weight allocation factors, which can help reduce renewable energy fluctuations and encourage the consumption of clean energy to reduce the carbon emissions of the system. Also, the results show that the proposed method can enhance the utility of renewable energy generation units by approximately 64 % to 69 %. Finally, the optimality and effectiveness of the proposed game theoretic approaches are verified in case studies and in strict mathematical proofs. In addition, the impact mechanisms on the demand response from the perspectives of the user load and price sensitivity are analysed. To explore pricing methods that are more suitable for user aggregators, two pricing strategies are designed for the user aggregator and compared in terms of computation time and revenue. The results show that a unified pricing strategy can increase the aggregator's revenue by 1.24 % and reduce the computation time by 24.47 %.

Combining the aggregated forecasts: An efficient method for improving accuracy by stacking multiple weighting models.

Combining the aggregated forecasts: An efficient method for improving accuracy by stacking multiple weighting models.

Topology Identification for Networked Piecewise-smooth Systems With Multiple Weight Couplings Based a Novel Fixed-time Synchronization Approach

Topology Identification for Networked Piecewise-smooth Systems With Multiple Weight Couplings Based a Novel Fixed-time Synchronization Approach