Principal Component Scores Research Articles

Nonlinear modeling of oral glucose tolerance test response to evaluate associations with aging outcomes.

As people age, their ability to maintain homeostasis in response to stressors diminishes. Physical frailty, a syndrome characterized by loss of resilience to stressors, is thought to emerge due to dysregulation of and breakdowns in communication among key physiological systems. Dynamical systems modeling of these physiological systems aims to model the underlying processes that govern response to stressors. We hypothesize that dynamical systems model summaries are predictive of age-related declines in health and function. In this study, we analyze data obtained during 75-gram oral-glucose tolerance tests (OGTT) on 1,120 adults older than 50 years of age from the Baltimore Longitudinal Study on Aging. We adopt a two-stage modeling approach. First, we fit OGTT curves with the Ackerman model-a nonlinear, parametric model of the glucose-insulin system-and with functional principal components analysis. We then fit linear and Cox proportional hazards models to evaluate whether usual gait speed and survival are associated with the stage-one model summaries. We also develop recommendations for identifying inadequately-fitting nonlinear model fits in a cohort setting with numerous heterogeneous response curves. These recommendations include: (1) defining a constrained parameter space that ensures biologically plausible model fits, (2) evaluating the relative discrepancy between predicted and observed responses of biological interest, and (3) identifying model fits that have notably poor model fit summary measures, such as [Formula: see text], relative to other fits in the cohort. The Ackerman model was unable to adequately fit 36% of the OGTT curves. The stage-two regression analyses found no associations between Ackerman model summaries and usual gait speed, nor with survival. The second functional principal component score was associated with faster gait speed (p<0.01) and improved survival (p<0.01).

Geometric morphometric anatomy of the lacrimal punctum in normal population: Punctum update (PUP) study – Paper 5

BackgroundTo investigate the geometric morphological differences of the lacrimal punctum by analyzing its shape in relation to age and sex in a normal population. Methods960 high-magnification slit-lamp images were obtained from 320 puncta of normal asymptomatic Indian individuals across eight decades of life. Using advanced geometric morphometric techniques, including Elliptic Fourier Analysis and Principal Component Analysis, the intricate details of the lacrimal punctum's shape in a diverse population sample were categorized by age and sex. High-resolution images of the lacrimal punctum underwent standardization for scale and orientation, followed by precise landmark identification and coordinate data extraction. ResultsThe geometric morphometry of the lacrimal punctum shows significant changes as one ages. However, the gender differences, in isolation, without consideration of age, remain subtle and are not pronounced. Interestingly, detailed Principal Component scores analysis revealed potential sex- and age-related variations specifically for the left and right lower puncta, which warrant further investigation. These changes could reflect unique aging changes in the proximal lacrimal drainage system. ConclusionThe study is a starting point for geometric morphometric analysis of the lacrimal punctum and provides valuable insights into the punctal changes in size, orientation, and overall morphology across different age groups and between sexes. These findings highlight the significance of considering individual age-wise anatomical variations to better understand the lacrimal punctum.

Spatial variability and source analysis of soil heavy metals: A case study of the key planting area of special agricultural products in Cangxi County, China.

Heavy metal pollution in farmland soil represents a considerable risk to ecosystems and human health, constituting a global concern. Focusing on a key area for the cultivation of special agricultural products in Cangxi County, we collected 228 surface soil samples. We analyzed the concentration, spatial distribution, and pollution levels of six heavy metals (Cr, Cu, Pb, Ni, Zn, and Hg) in the soil. Moreover, we investigated the sources and contribution rates of these heavy metals using Principal Component Analysis/Absolute Principal Component Scores (PCA/APCS) and Positive Matrix Factorization (PMF) models. Our findings indicate that none of the six metals exceeded the pollution thresholds for farmland soils. However, the mean concentrations of Cr and Ni surpassed the background levels of Sichuan Province. A moderate spatial correlation existed between Pb and Ni, attributable to both natural and anthropogenic factors, whereas Zn, Cu, Hg, and Cr displayed a strong spatial correlation, mainly due to natural factors. The spatial patterns of Cr, Cu, Zn, Pb, and Ni were similar, with higher concentrations in the northern and eastern regions and lower concentrations centrally. Hg's spatial distribution differed, exhibiting a broader range of lower values. The single pollution index evaluation showed that Cr and Ni were low pollution, and the other elements were no pollution. The average value of comprehensive pollution index is 0.994, and the degree of pollution is close to light pollution. Predominantly, higher pollution levels in the northern and eastern regions, lower around reservoirs. The PCA/APCS model identified two main pollution sources: agricultural traffic mixed source (65.2%) and natural parent source (17.2%). The PMF model delineated three sources: agricultural activities (32.59%), transportation (30.64%), and natural parent sources (36.77%). Comparatively, the PMF model proved more accurate and reliable, yielding findings more aligned with the study area's actual conditions.

Genome-wide association study of metabolic dysfunction-associated fatty liver disease in a Korean population

Genome-wide association studies have identified several genetic variants associated with nonalcoholic fatty liver disease. To emphasize metabolic abnormalities in fatty liver, metabolic (dysfunction)-associated fatty liver disease (MAFLD) has been introduced; thus, we aimed to investigate single-nucleotide polymorphisms related to MAFLD and its subtypes. A genome-wide association study was performed to identify genetic factors related to MAFLD. We used a Korean population-based sample of 2282 subjects with MAFLD and a control group of 4669. We replicated the results in a validation sample which included 639 patients with MAFLD and 1578 controls. Additionally, we categorized participants into three groups, no MAFLD, metabolic dysfunction (MD)-MAFLD, and overweight/obese-MAFLD. After adjusting for age, sex, and principal component scores, rs738409 [risk allele G] and rs3810622 [risk allele T], located in the PNPLA3 gene, showed significant associations with MAFLD (P-values, discovery set = 1.60 × 10–15 and 4.84 × 10–10; odds ratios, 1.365 and 1.284, validation set = 1.39 × 10–4, and 7.15 × 10–4, odds ratios, 1.299 and 1.264, respectively). An additional SNP rs59148799 [risk allele G] located in the GATAD2A gene showed a significant association with MAFLD (P-values, discovery set = 2.08 × 10–8 and validation set = 0.034, odds ratios, 1.387 and 1.250). rs738409 was significantly associated with MAFLD subtypes ([overweight/obese-MAFLD; odds ratio (95% confidence interval), P-values, 1.515 (1.351–1.700), 1.43 × 10–12 and MD-MAFLD: 1.300 (1.191–1.416), 2.90 × 10–9]. There was a significant relationship between rs3810622 and overweight/obese-MAFLD and MD-MAFLD [odds ratios (95% confidence interval), P-values, 1.418 (1.258, 1.600), 1.21 × 10–8 and 1.225 (1.122, 1.340), 7.06 × 10–6, respectively]; the statistical significance remained in the validation set. PNPLA3 was significantly associated with MAFLD and MAFLD subtypes in the Korean population. These results indicate that genetic factors play an important role in the pathogenesis of MAFLD.

Different PCA approaches for vector functional time series with applications to resistive switching processes

This paper is motivated by modeling the cycle-to-cycle variability associated with the resistive switching operation behind memristors. Although the data generated by this stochastic process are by nature current–voltage curves associated with the creation (set process) and destruction (reset process) of a conductive filament, the statistical analysis is usually based on analyzing only the scalar time series related to the reset and set voltages/currents in consecutive cycles. As the data are by nature curves, functional principal component analysis is a suitable candidate to explain the main modes of variability associated with these processes. Taking into account this data-driven motivation, in this paper we propose two new forecasting approaches based on studying the sequential cross-dependence between and within a multivariate functional time series in terms of vector autoregressive modeling of the most explicative functional principal component scores. The main difference between the two methods lies in whether a univariate or multivariate PCA is performed so that we have a different set of principal component scores for each functional time series or the same one for all of them. Finally, the sample performance of the proposed methodologies is illustrated by an application on a bivariate functional time series of reset/set curves.

The relationship between joint kinematic patterns during single-leg drop landing and perceived instability in individuals with chronic ankle instability

BackgroundPerceived instability is a primary symptom among individuals with chronic ankle instability. However, the relationship between joint kinematics during landing remains unclear. Therefore, we investigated the relationships between landing kinematics and perceived instability in individuals with chronic ankle instability. MethodsIn 32 individuals with chronic ankle instability, we recorded ankle, knee, and hip joint angles during a single-leg drop landing. Joint angle waveforms during 200 ms before and after initial contact were summarized into single values using two methods: peak joint angles and principal component scores via principal component analysis. Using Spearman's rank correlation coefficient (ρ), we examined the relationships of peak joint angles and principal component scores with the Cumberland Ankle Instability Tool score, with a lower score indicating a greater perceived instability (α = 0.05). FindingsThe second principal component scores of ankle angle in the horizontal and sagittal planes significantly correlated with the Cumberland Ankle Instability Tool score (Horizontal: ρ = 0.507, P = 0.003; Sagittal: ρ = −0.359, P = 0.044). These scores indicated the differences in the magnitude of angles before and after landing. Significant correlations indicated a greater perceived instability correlated with smaller internal rotation and plantarflexion before landing and smaller external rotation and dorsiflexion after landing. In contrast, no peak joint angles correlated with the Cumberland Ankle Instability Tool score (P > 0.05). InterpretationIn individuals with chronic ankle instability, ankle movements during landing associated with perceived instability may be a protective strategy before landing and potentially cause ankle instability after landing.

Water Quality Assessment of Mananga River Using Principal Component Analysis

The Mananga River in the Philippines has been classified as a Class A river in 1997. Since then, the river has significantly deteriorated due to pollution, especially in the downstream area around Talisay in the Cebu region. In this study, the water quality parameters – namely, dissolved oxygen (DO), biological oxygen demand (BOD), and total suspended solids (TSS) – were assessed using principal component analysis (PCA). Water quality data were obtained from the Department of Environmental and Natural Resources–Environmental Management Bureau Region 7. The water samples were collected on a quarterly basis from 2016–2019. The effect of weather conditions on water quality and significant relationships between the water quality parameters were determined. In addition, the comparison of water quality in each sampling station was investigated. Results show that about 84.3% of the total variance in water quality can be attributed to two significant principal component scores. Evident correlations were observed such as DO and BOD are negatively correlated, whereas a positive correlation exists between DO to RH and wind speed, BOD to temperature, and TSS to wind speed. Furthermore, negative correlations are observed between DO to temperature and wind direction, BOD to rainfall, and TSS to wind direction and rainfall. In the overall analysis of the results, the heavier the influence of a variable, the more likely it is to contribute to affecting the water quality. Therefore, this can alter the overall distribution within the plots and the correlations among the variables. The findings are a predictive measure of future changes and trends in the Mananga River. Therefore, the results of the present work will be used in environmental monitoring, environmental management, and assistance for the rehabilitation of the Mananga River using PCA.

Impaired Neuromotor Control During Gait in Concussed Adolescents-A Frequency Analysis.

Disruptions in gait function are common after concussion in adolescents; however, the neuromotor control deficits driving these gait disruptions are not well known. Fifteen concussed (age mean [SD]): 17.4 [0.6], 13 females, days since injury: 26.3 [9.9]) and 17 uninjured (age: 18.0 [0.7], 10 females) adolescents completed 3 trials each of single-task gait and dual-task gait (DT). During DT, participants simultaneously walked while completing a serial subtraction task. Gait metrics and variability in instantaneous mean frequency in lower extremity muscles were captured by inertial sensors and surface electromyography, respectively. A 2-way analysis of covariance was used to compare gait metrics across groups and conditions. Functional principal components analysis was used to identify regions of variability in instantaneous mean frequency curves. Functional principal component scores were compared across groups using a Welch statistic. Both groups displayed worse performance on gait metrics during DT condition compared to single-task, with no differences between groups (P < .001). Concussed adolescents displayed significantly greater instantaneous mean frequency, indicated by functional principal component 1, in the tibialis anterior, biceps femoris, and semitendinosus (P < .05) during single-task and DT compared with uninjured adolescents. Our observations suggest that concussed adolescents display inefficient motor unit recruitment lasting longer than 2weeks following injury, regardless of the addition of a secondary task.

Fingerprinting of monovarietal olive oils from Argentina and Uruguay via stable isotope, fatty acid profile, and chemometric analyses

Tracing methods of non-European EVOOs commercialized worldwide are becoming crucial for effective authenticity controls. Limited analytical studies of these oils are available on a global scale, similar to those of European EVOOs. We report for the first time the fatty acid concentrations, bulk-oil 2H/1H, 13C/12C, and 18O/16O ratios and fatty acid 13C/12C ratios of 43 authentic monovarietal EVOOs from different geographical regions in Argentina and Uruguay. The samples were obtained from a wide range of latitudes and altitudes along an E–W profile, from lowlands near the Atlantic Ocean to the pre-Andean highlands near the Pacific Ocean. Principal component scores were used to cluster EVOOs into three groups— central-western Argentina, central Argentina, and Uruguay—based on nine stable isotope ratios and the oleic-linoleic acid concentration ratio. The bulk 2H/1H and 18O/16O values and 13C/12C of palmitoleic and linoleic acids provide good tools for differentiating these oils via linear discriminant analysis.

Affective product form bionic design based on functional analysis

To address the challenge of the absence of automated design and evaluation methods encompassing both functional and emotional analysis in product form bionic design (PFBD), this study introduces a novel affective PFBD method based on functional analysis. Firstly, the target product, target emotion and target creature are identified through questionnaire surveys and statistical analysis. Elliptical Fourier analysis and principal component analysis are employed to parameterize the shape outlines of the target products and creature, yielding principal component score data capturing their shape features. Secondly, product form and biological shape are fused by utilizing a proposed fusion generation operator to generate diverse PFBD conceptual schemes. Finally, a comprehensive multi-dimensional evaluation method, incorporating the functional analysis model, bionic evaluation model and emotional evaluation model, is formulated to assess the conceptual schemes and identify the optimal scheme. To validate the feasibility and effectiveness of the proposed method, it is applied to the automated generative design and evaluation of water cup shapes. The outcome reveals the identification of an optimal solution that not only satisfies the functional requirements of the product but also addresses user emotional needs. The results underscore the method's outstanding performance in PFBD involving both functional and emotional analysis, establishing it as an effective tool for designers to efficiently generate a variety of solutions, thereby ensuring the rationality and scientific foundation of product design.

Improving chemical reaction yield prediction using pre-trained graph neural networks

Graph neural networks (GNNs) have proven to be effective in the prediction of chemical reaction yields. However, their performance tends to deteriorate when they are trained using an insufficient training dataset in terms of quantity or diversity. A promising solution to alleviate this issue is to pre-train a GNN on a large-scale molecular database. In this study, we investigate the effectiveness of GNN pre-training in chemical reaction yield prediction. We present a novel GNN pre-training method for performance improvement.Given a molecular database consisting of a large number of molecules, we calculate molecular descriptors for each molecule and reduce the dimensionality of these descriptors by applying principal component analysis. We define a pre-text task by assigning a vector of principal component scores as the pseudo-label to each molecule in the database. A GNN is then pre-trained to perform the pre-text task of predicting the pseudo-label for the input molecule. For chemical reaction yield prediction, a prediction model is initialized using the pre-trained GNN and then fine-tuned with the training dataset containing chemical reactions and their yields. We demonstrate the effectiveness of the proposed method through experimental evaluation on benchmark datasets.

Comparative Receptor Models Using Principal Component Analysis/Absolute Principal Component Scores and Positive Matrix Factorization to Assess Source Apportionment of PM2.5-10 and PM2.5 in Urban Cities

b Source apportionment of PM2.5-10 and PM2.5 was conducted using two receptor models: the principal component analysis/absolute principal component scores (PCA/APCS) and the positive matrix factorization (PMF). Methods: The PCA/APCS model resolved four sources, namely, mineral dust, sea salt/secondary sulfur, a mixed source, and traffic and industry emissions. PMF model also resolved four sources of the same origin based on the proportion of each component from the analysis. All models identified the main sources that contribute to PM2.5-10 and PM2.5 emissions and reconfirmed that the potential sources were the dominants in particulate matter in the Capital city. Findings: The first four extracted component accounted for nearly 88.3% of the variability of the data set. The running matrix elements used for data processing within collected air particulate matter was BC, Na, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, Rb, Sr, Ba, and Pb. This discrimination of the area of Dakar cites according to the PCA/APCS results confirms the impact that the major activities have on the environment in the most appropriate way, and it verifies the results of previous studies about the influence of Industry emissions, mineral dust, traffic emissions and sea salt/secondary sulfur on the surrounding urban areas. Novelty: Continued collection of speciation data at the urban areas will enhance the understanding of local versus regional source contributions for air quality index in sub-Sahara region. Keywords: Aerosol, Particulate Matter, Source apportionment, receptor Modelling, XRF, heavy metals

Associations of micronutrient dietary patterns with sarcopenia among US adults: a population-based study.

Current epidemiological evidence points to an association between micronutrient (MN) intake and sarcopenia, but studies have focused on single MN, and no combined effects on MNs have been reported. The aim of this study was to investigate the relationship between different MN intake patterns and sarcopenia and skeletal muscle mass. We performed a population-based cross-sectional study, with a total of 5,256 U.S. adults aged 20-59 years, and we collected total daily MN intake and appendicular skeletal muscle mass measured by Dual-Energy X-ray Absorptiometry (DXA). Principal component analysis (PCA) was used to obtain nutrient patterns and principal component scores based on the intake of 14 MNs, and logistic regression analysis was used to assess the effects of single MN and MN intake patterns on sarcopenia and muscle mass. We defined three MN intake patterns by PCA: (1) adherence to VitB-mineral, high intake of vitamin B and minerals; (2) adherence to VitAD-Ca-VB12, high intake of vitamin A, vitamin D, calcium and vitamin B12; and (3) adherence to Antioxidant Vit, high intake of antioxidant vitamins A, C, E, and K. These three nutrient patterns explained 73.26% of the variance of the population. A negative association was observed between most single MN intakes and sarcopenia, and after adjusting for confounders, adherence to the highest tertile of the three nutrient patterns was associated with a lower risk of sarcopenia and relatively higher skeletal muscle mass compared to the lowest adherence. In subgroup analysis, MN intake patterns were significantly correlated with sarcopenia in middle-aged females. Nutritional patterns based on MN intake were significantly related to sarcopenia, indicating that MNs interact with each other while exerting their individual functions, and that MN dietary patterns may provide promising strategies for preventing the loss of muscle mass, with further prospective studies warranted in the future.

P1207 Microbiome composition determines sustained remission after the Crohn’s Disease Exclusion Diet (CDED) with or without Partial Enteral Nutrition in adults with mild-moderate Crohn’s disease; Results from the CDED-AD Pilot Randomized Controlled Trial

Abstract Background The Crohn's Disease (CD) Exclusion Diet (CDED) is an established dietary therapy for children with active mild-to-moderate CD. A recent randomized controlled trial showed efficacy in mild-to-moderate CD in adults. We have previously demonstrated the impact of CDED on the microbiome of children. The aim of this work was to assess if we can determine sustained clinical remission (SCR) from the baseline microbiome composition and examine the effect of CDED on microbiome composition in adult CD. Methods This was an open-label, prospective, randomized, controlled pilot trial involving patients with mild-to-moderate CD and evidence of active inflammation. Patients were randomly assigned to receive CDED with partial Enteral Nutrition (PEN) or CDED alone, both for 24 weeks. The V4V5 region of the 16S rRNA gene was amplified and sequenced, and reads were processed using QIIME2 and the SILVA database. Relationships between the microbiome, SCR, and Fecal Calprotectin (FC) were analyzed using logistic regression, Poisson Principal Component Analysis, paired T-Tests and subsampling ranking forward selection. Results Clinical remission (CR) at weeks 6, 12 and 24 was achieved in 62.5%, 52.5%, and 50% of patients, respectively. Week 0, 6, 12 and 24 stool samples were available for 35,32,30 and 26 patients, respectively. In patients with SCR, defined as remission at both weeks 12 and 24, the baseline microbiome revealed lower alpha diversity compared to those without SCR (p=0.018), and differing Microbiome principal components (PC) scores (p &amp;lt; 0.01). PC1 and PC3, characterized mainly by Alistipes, Faecalibacterium and UCG-002 predicted SCR (P&amp;lt;0.01). PC2 predicted elevated FC&amp;gt;250 µg/g at week 12 (p=0.004). Baseline abundance of Haemophilus was associated with decreased SCR probability (p&amp;lt;0.01). At week 6 in the entire cohort, there was a significant decrease in Actinobacteriota (p=0.035) and a significant increase in Bacteroidota (p=0.003). Additionally, there was a significant increase in several genera, including beneficial Alistipes and Oscillibacter (p&amp;lt;0.02) in those who achieved CR after 6, 12, or 24 weeks. Higher Firmicutes at week 6 were associated with FC&amp;lt;250 at week 12 (p=0.031). An uncultured genus from the Oscillospiraceae family was significantly associated with a decrease in the probability of SCR (p&amp;lt;0.01) and was significantly increased at week 6 in patients who did not attain CR (p&amp;lt;0.05). Conclusion The baseline microbiome composition was associated with SCR and FC&amp;lt;250 at week 12 in adult patients treated with CDED (with or without PEN) over 24 weeks. This suggests that the microbiome could be useful tool to identify patients who would benefit from long-term dietary intervention.

3D-QSAR Studies on High-affinity Phosphodiestera.

Recent studies have found that Phosphodiesterase-4 (PDE4) is closely related to the pathogenesis of depression, cognitive impairment and neurological impairment. Our objective is to develop potent inhibitors of the high-affinity phosphodiesterase 4D isoform (PDE4D) that can serve as radioligands for Positron Emission Tomography (PET) imaging, thereby advancing research in the field of neurological diseases. We employed a multi-step approach combining three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, classification techniques, and CoMSIA analysis to investigate the conformational relationship of highaffinity PDE4D inhibitors as PET ligands. ADMET and Drug-likeness predictions were also conducted. By utilizing these methods, our aim was to identify more potent PDE4D inhibitors. The results showed that the CoMSIA model with the best principal component scores (n=7) had a cross-validated Q2 value of 0.602 and a non-cross-validated R2 value of 0.976. These results affirmed the excellent predictive capability of the established CoMSIA model. Analysis of the generated 3D-QSAR contour plots highlighted specific regions in the molecular structure of the compounds that can be further optimized and modified. Guided by the contour plots, we designed 100 novel PDE4D inhibitors, and molecular docking was performed for the top 4 compounds with high activity. The molecular docking scores were promising, and ADMET and drug similarity predictions yielded satisfactory results. Taking into consideration these factors, compound 51c was determined to be the optimal compound, laying a solid foundation for further research. For the continued development of PDE4D PET radioligand, these models and new compounds' developing methodology offer a theoretical foundation and crucial references.

Clarifying the correlations between hydraulic indicators evaluating the hydraulic performance of free water surface constructed wetlands.

Previous studies have shown a strong linear correlation between the common hydraulic indicators of free water surface constructed wetlands (FWS CWs), which results in the waste of computing resources and confusion in evaluating the hydraulic performance of FWS CWs. There is an urgent need to define a relatively independent and authoritative hydraulic indicator. In this study, based on three years' data, the correlations among five representative hydraulic indicators, that is, short-circuit index (φ10), Morrill dispersion index (MDI), effective volume ratio (e), moment index (MI), and hydraulic efficiency (λe), were analyzed by combining a variety of methods. The results of the correlation analysis and principal component analysis clearly showed the positive correlations among φ10, e, MI, and λe (p < 0.01), and the strong negative correlations between MDI and the remaining four indexes, which were further confirmed by redundancy analysis (RDA). Most importantly, the significant correlations between MI vs e and φ10 vs e were proved through rigorous mathematical reasoning for the first time. Besides, the results of RDA indicated that the studied design parameters, namely, water depth, hydraulic loading rate, plant spacing, aspect ratio, layout of inlet and outlet, and plant species, could generally explain the variation in hydraulic indicators. The layout of inlet and outlet displayed positive effects on hydraulic performance, and the water depth exhibited negative effects. Combining the clarity of physical meaning, convenience of calculation, and universality of reflecting both hydraulic and treatment performances, e was recommended as the most authoritative hydraulic indicator to evaluate hydraulic performance. The clustering results based on e were highly consistent with those based on the comprehensive principal component score. Wetlands with a combination of lower water depth and better layout of inlet and outlet usually have better hydraulic performance. This study successfully revealed the significant correlations among hydraulic indicators and their sources, and recommended a unique hydraulic indicator to authoritatively evaluate the hydraulic performance of FWS CWs.

Population trends in human rib cross-sectional shapes.

Rib fractures remain the most frequent thoracic injury in motor vehicle crashes. Computational human body models (HBMs) can be used to simulate these injuries and design mitigation strategies, but they require adequately detailed geometry to replicate such fractures. Due to a lack of rib cross-sectional shape data availability, most commercial HBMs use highly simplified rib sections extracted from a single individual during original HBM development. This study provides human rib shape data collected from chest CT scans of 240 females and males across the full adult age range. A cortical bone mapping algorithm extracted cross-sectional geometry from scans in terms of local periosteal position with respect to the central rib axis and local cortex thickness. Principal component analysis was used to reduce the dimensionality of these cross-sectional shape data. Linear regression found significant associations between principal component scores and subject demographics (sex, age, height, and weight) at all rib levels, and predicted scores were used to explore the expected rib cross-sectional shapes across a wide range of subject demographics. The resulting detailed rib cross-sectional shapes were quantified in terms of their total cross-sectional area and their cortical bone cross-sectional area. Average-sized female ribs were smaller in total cross-sectional area than average-sized male ribs by between 20% and 36% across the rib cage, with the greatest differences seen in the central portions of rib 6. This trend persisted although to smaller differences of 14%-29% when comparing females and males of equal intermediate weight and stature. Cortical bone cross-sectional areas were up to 18% smaller in females than males of equivalent height and weight but also reached parity in certain regions of the rib cage. Increased age from 25 to 80 years was associated with reductions in cortical bone cross-sectional area (up to 37% in females and 26% in males at mid-rib levels). Total cross-sectional area was also seen to reduce with age in females but to a lesser degree (of up to 17% in mid-rib regions). Similar regions saw marginal increases in total cross-sectional area for male ribs, indicating age affects rib cortex thickness moreso than overall rib cross-sectional size. Increased subject height was associated with increased rib total and cortical bone cross-sectional areas by approximately 25% and 15% increases, respectively, in mid-rib sections for a given 30 cm increase in height, although the magnitudes of these associations varied by sex and rib location. Increased weight was associated with approximately equal changes in both cortical bone and total cross-sectional areas in males. These effects were most prominent (around 25% increases for an addition of 50 kg) toward lower ribs in the rib cage and had only modest effects (less than 12% change) in ribs 2-4. Females saw greater increases with weight in total rib area compared to cortical bone area, of up to 21% at the eighth rib level. Results from this study show the expected shapes of rib cross-sections across the adult rib cage and across a broad range of demographics. This detailed geometry can be used to produce accurate rib models representing widely varying populations.

Research on Digital Inclusive Finance, Innovative Factor Allocation and High Quality Economic Development Based on Artificial Intelligence

Abstract This paper firstly organizes and summarizes the random forest model algorithm in artificial intelligence and secondly uses the XGBoost algorithm with the classification regression tree as the base classifier to explore the relationship between digital financial inclusion, innovative factor allocation and high-quality economic development. Major databases and relevant websites are used to establish the evaluation index system, and the index homogenization is done. The economic high-quality development score is measured using the principal component analysis next. Finally, the principal component scores are weighted and summed according to the proportion of the variance contribution ratio of each principal component to the cumulative variance contribution ratio of the extracted principal components. After calculating the score, it was found that the innovation indicator, which had risen year after year between 2014 and 2018, decreased significantly from 2019 onwards, with the score falling from 2.21 to 1.48, a decrease of 33%. The growth rate of digital financial inclusion declined from 0.93 and finally maintained at 0.45 to 0.47. The economic development index first grew from -11.88 to 2.41, increasing by 3 to 5 each year, and then the growth rate was maintained at about 66%, growing to 6.58 in 2022.

Current Situation and Trend of Internationalization Development of Higher Education in the Context of Internet

Abstract Under the background of the Internet era, this article emphasizes the importance of internationalization of higher education in the strategic adjustment and acceleration of opening up to the outside world. The article analyzes the internationalization development policy and talent cultivation mode of higher education. It applies structural equations and kernel principal component analysis to study the influencing factors of internationalization of education and the level of its development. Taking the 12 colleges and universities in the city of M as the objects of the study, the impact of these indicators on the development of internationalization of education is explored after constructing the indicators of internationalization of education. After making the evaluation indexes of internationalization of education, the influence of these indexes on the development of internationalization of education was explored, and the principal component coefficients of the indexes and the principal component scores of the universities were calculated through principal component analysis to assess the development level of internationalization of education in the universities. The results showed that all the influencing factors were positively correlated at the 0.05 significance level, and the standardized coefficients ranged from 0.1 to 0.5, which indicated that these factors had a medium degree of influence on the internationalization of education. In particular, the correlations with the number of foreign exchange students, the number of visitors and the number of professional teachers are high, so the article suggests expanding the enrollment of foreign students and strengthening international exchange and cooperation to further improve the level of internationalization of education.

Distribution, sources, contamination, and risks of toxic metals in Zijiang River, a typical tributary of the midstream of the Yangtze River in China

Excessive concentrations of toxic metals are a global threat to aquatic systems. Taking a typical tributary (Zijiang River, ZR) of the midstream of the Yangtze River as the research area, the concentration distribution and chemical fractions occurrence characteristics of five toxic metals (Cd, Cr, Cu, Pb, and Zn) were analyzed, their potential sources were explored, and their contamination and ecological risk was assessed. In the surface waters and sediments, there were high concentrations of Zn, a low concentration of Cd, and small spatial differences in concentration among the upstream, midstream, and downstream. In terms of speciation, Cd mainly existed in the acid-soluble fraction, Pb mainly existed in the reducible fraction, and Cr, Cu, and Zn mainly existed in the residue fraction. The potential sources in surface waters and sediments were determined to be industrial emissions and agricultural non-point sources through the absolute principal component scores–multiple linear regression model (APCS–MLR). Based on the assessment results of total concentration and speciation, Cd was the typical contamination element in ZR sediments. In addition, the secondary phase enrichment factor (SPEF) based on speciation underestimates the degree of Pb contamination, and the ecological risk of Zn assessed by the ratio of secondary phase and primary phase (RSP) and the risk assessment code (RAC) was higher than that of Cr, which was inconsistent with the results based on total concentrations. SOM and Al/Fe/Mn cycles in sediments influenced the geochemical behavior of toxic metals.