Patterns Of Variation Research Articles

Deformation Characteristics and Influence Factors of Shear Force Lateral Stiffness Matching Index for Non-Rigid Plate Bent Frame Structures

The period ratio and the drift ratio are commonly used as plane regularity control indices for multi-story buildings. However, they fail to reasonably reflect the regularity of lateral force-resisting component configuration and deformation characteristics in non-rigid plate bent frame structures. This study focuses on the analysis of non-rigid single-span bent frames, examining the variation patterns of a suitable regularity index for non-rigid plate bent frame structures, referred to as the shear force lateral stiffness matching index, under various parameters. Additionally, it introduces indices to quantify the deformation response of non-rigid plate bent frame structures, providing a detailed analysis of the impact of factors such as eccentricity, torsional stiffness, and roof slab stiffness on the deformation characteristics of non-rigid plate bent frame structures and the shear force lateral stiffness matching index. The results show that the shear force lateral stiffness matching index can reflect the inconsistency in the horizontal displacement response of lateral force-resisting components caused by deformations in the roof slab. The proposed indices for torsional and bending deformations accurately quantify the roof slab’s deformation response, revealing the horizontal deformation characteristics of lateral force-resisting components in non-rigid frames. When eccentricity is present, the stiffness of the roof slab has a non-monotonic effect on the torsional component of the structural seismic response.

Coordinated, multicellular patterns of transcriptional variation that stratify patient cohorts are revealed by tensor decomposition.

Tissue-level and organism-level biological processes often involve the coordinated action of multiple distinct cell types. The recent application of single-cell assays to many individuals should enable the study of how donor-level variation in one cell type is linked to that in other cell types. Here we introduce a computational approach called single-cell interpretable tensor decomposition (scITD) to identify common axes of interindividual variation by considering joint expression variation across multiple cell types. scITD combines expression matrices from each cell type into a higher-order matrix and factorizes the result using the Tucker tensor decomposition. Applying scITD to single-cell RNA-sequencing data on 115 persons with lupus and 83 persons with coronavirus disease 2019, we identify patterns of coordinated cellular activity linked to disease severity and specific phenotypes, such as lupus nephritis. scITD results also implicate specific signaling pathways likely mediating coordination between cell types. Overall, scITD offers a tool for understanding the covariation of cell states across individuals, which can yield insights into the complex processes that define and stratify disease.

Influence of Immersion Orientation on Microstructural Evolution and Deformation Behavior of 40Cr Steel Automobile Front Axle during Oil Quenching.

This study employs the finite element method to investigate the microstructural evolution and deformation behavior of a 40Cr steel automobile front axle during the quenching process. By establishing a multi-physics field coupling model, the study elucidates the variation patterns of the microstructure field in the quenching process of the front axle under different immersion orientations. It is found that along the length direction, the bainite and martensite structures decrease from the center to the edge region, while the ferrite structure shows an increasing trend. Additionally, the influence of immersion orientation on the hardness of the front axle's microstructure and deformation behavior is thoroughly discussed. The results indicate that, firstly, when quenched horizontally, the hardness difference among different regions of the front axle is approximately 8.2 HRC, whereas it reaches 10.3 HRC when quenched vertically. Considering the uniformity of the microstructure, the horizontal immersion method is preferable. Secondly, due to the different immersion sequences in different regions of the front axle leading to varying heat transfer rates, as well as the different amounts of martensite structures obtained in different regions, the deformation decreases along the length direction from the center to the edge region. Horizontal immersion quenching, compared to vertical immersion, results in a reduction of approximately 56.2% and 48.9% in deformation on the representative central cross-section (A-A) and the total length of the front axle, respectively. Therefore, considering aspects such as microstructure uniformity and deformation, the horizontal immersion quenching orientation is more favorable.

Research on temperature performance prediction of vortex tubes based on artificial neural networks

Abstract This study constructs a hybrid neural network model by integrating the physical constraints of the Bernoulli equation and Nikolaev's formula. The model is designed to explore and predict the variation pattern of the cold end temperature in a vortex tube. The input parameters include inlet pressure, inlet temperature, and cold mass fraction, with the cold end temperature as the output parameter. The network employs a multilayer feedforward model and the Levenberg-Marquardt learning algorithm, using a hyperbolic tangent function as the activation function. To evaluate the statistical validity of the developed model, the coefficient of determination (R²) and root mean square error (RMSE) are utilized, along with an analysis of the model's uncertainty and robustness. The hybrid model achieves an R² of 0.9936 and an RMSE of 0.3392, demonstrating strong performance in terms of uncertainty and robustness. These results indicate that the model accurately predicts the cold end temperature variation in the vortex tube. Furthermore, the findings reveal an optimal pressure range (0.49 MPa to 0.76 MPa) and cold mass fraction range (0.1 to 0.2) that minimize the cold end temperature.

Continuous and Stable Printing Method of Planar Microstructure Based on Meniscus-Confined Electrodeposition.

The meniscus-confined electrodeposition (MCED) technique offers advantages such as low cost and wide applicability, making it a promising method in the field of micro/nanofabrication. However, unstable meniscal morphology and poor deposition quality during planar deposition in MCED necessitate the development of improved methods. Therefore, a planar adaptive micro-tuning deposition method (PAMTDM), which utilizes the positioning technology of scanning electrochemical cell microscopy (SECCM) and employs a singular value decomposition (SVD) planar fitting method to determine the flatness of the deposition plane, is proposed. An adaptive micro-tuning motion mode was proposed by analyzing the variation patterns of the meniscus. Moreover, a combination of multi-physics finite element simulations and orthogonal experimental methods was introduced to determine the optimal motion parameters. The experimental results demonstrate that the PAMTDM effectively addresses the issues encountered during planar growth. Compared to the point-by-point deposition method, the PAMTDM achieves a threefold increase in deposition speed for continuous deposition of 105-μm-long line segments in two-dimensional planes, with a deposition current error of less than 0.2 nA. In conclusion, the proposed method provides significant insights into the broad future applications of MCED.

Selection on the vascular-remodeling BMPER gene is associated with altitudinal adaptation in an insular lizard

Abstract High altitude imposes several extreme constraints on life, such as low oxygen pressure and high levels of ultraviolet radiation, which require specialized adaptations. Many studies have focused on how endothermic vertebrates respond to these challenging environments, but there is still uncertainty on how ectotherms adapt to these conditions. Here, we used whole-genome sequencing of low-altitude (100–600 m) and high-altitude (3,550 m) populations of the wide-ranging Tenerife lizard Gallotia galloti to uncover signatures of selection for altitudinal adaptation. The studied populations show reduced differentiation, sharing similar patterns of genetic variation. Selective sweep mapping suggests that signatures of adaptation to high altitude are not widespread across the genome, clustering in a relatively small number of genomic regions. One of these regions contains BMPER, a gene involved with vascular remodeling, and that has been associated with hypoxia-induced angiogenic response. By genotyping samples across 2 altitudinal transects, we show that allele frequency changes at this locus are not gradual, but rather show a well-defined shift above ca. 1,900 m. Transcript and protein structure analyses on this gene suggest that putative selection likely acts on noncoding variation. These results underline how low oxygen pressure generates the most consistent selective constraint in high-altitude environments, to which vertebrates with vastly contrasting physiological profiles need to adapt in the context of ongoing climate change.

Intraspecific variation in leaf morphology of three widespread woody species along climatic gradients

Abstract The variation and plasticity of leaf morphology plays a pivotal role in the response to environmental changes for plant individuals. Discovering the large-scale pattern of such variation can reveal plants' general adaptive strategies. We analyzed leaf morphology of three widespread woody species in the northern hemisphere using specimen data from the iDigBio and GBIF databases, to investigate the variations in the individual mean traits, in the inter- and intra-individual variability of traits, and in the allometry between traits, along climatic gradients. We found that larger and wider leaves were associated with warmer, wetter and low-sunlight habitats, while smaller but wider leaves are linked to higher wind speed, indicating the response of leaf morphology to multiple climate stresses. The inter-individual variation in leaf area was smaller in colder and windier conditions, suggesting the trait convergence among individuals under environmental filtering, while the intra-individual variation in leaf relative width was smaller in warmer habitats, indicating the similar growth optimum of leaves within one individual in more favorable conditions. Finally, the allometric exponent between leaf length (X-axis) and width (Y-axis) became greater under lower solar radiation and higher wind speed, while the squared correlation coefficient (r2) indicating phenotypic integration showed a decoupling trend under colder conditions, indicating that climate affected the variation tendency of leaf relative width during leaf enlargement. These results reveal the common patterns of leaf morphology responding to climate variation spatially and underscore the necessity to consider inter- and intra-individual variability when examining plant responses to environmental changes.

Stability Study and Strengthening Strategy of Spiral Case-Encased Concrete Structure of Pumped Storage Power Station

With the development of global hydropower, the scale of hydropower stations is increasing, and the operating conditions are becoming more complex, so the stable operation of hydropower stations is very important. The vibration of the turbine unit will cause resonance in the powerhouse, and the structural stability of the powerhouse will be affected. Many scholars pay attention to the stability of the turbine unit operation, and there are few studies on the powerhouse of the hydropower station. Therefore, this paper relies on the Weifang Hydropower Station project to study key issues such as the tensile strength of concrete and how to arrange steel bars to increase the structural stability by changing the material properties through FEA. Three schemes are designed to evaluate the safety of the powerhouse structure when the turbine unit is running through the safety factor. Our findings indicate that the stress variation patterns observed on the inner surface of the powerhouse remain consistent across different operating scenarios. Notably, along the spiral line of the worm section, we observed that the stress levels on the vertical loop line decrease gradually with increasing distance from the inlet. Conversely, stress concentrations arise near the inlet and the tongue. Additionally, it has been noted that the likelihood of concrete cracking increases significantly at the tongue region.

Oceanographical-driven dispersal and environmental variation explain genetic structure in an upwelling coastal ecosystem

The seascape comprises multiple environmental variables that interact with species biology to determine patterns of spatial genetic variation. The environment imposes spatially variable selective forces together with homogenizing and diverging drivers that facilitate or restrict dispersal, which is a complex, time-dependent process. Understanding how the seascape influences spatial patterns of genetic variation remains elusive, particularly in coastal upwelling systems. Here, we combine genome-wide SNP data, Lagrangian larval dispersal simulated over a hydrodynamic model, and ocean environmental information to quantify the relative contribution of ocean circulation and environmental heterogeneity as drivers of the spatial genetic structure of two congeneric intertidal limpets, Scurria scurra and S. araucana, along the central coast of Chile. We find that a genetic break observed in both limpet species coincides with a break in connectivity shown by the Lagrangian dispersal, suggesting that mean ocean circulation is an important seascape feature, in particular for S. scurra. For S. araucana, environmental variation appears as a better predictor of genetic structure than ocean circulation. Overall, our study shows broad patterns of seascape forcing on genetic diversity and contributes to our understanding of the complex ecological and evolutionary interactions along coastal upwelling systems.

Genome-wide patterns of selection-drift variation strongly associate with organismal traits across the green plant lineage.

There are many gaps in our knowledge of how life cycle variation and organismal body architecture associate with molecular evolution. Using the diverse range of green algal body architectures and life cycle types as a test case, we hypothesize that increases in cytomorphological complexity are likely to be associated with a decrease in the effective population size, because larger-bodied organisms typically have smaller populations, resulting in increased drift. For life cycles, we expect haploid-dominant lineages to evolve under stronger selection intensity relative to diploid-dominant life cycles owing to masking of deleterious alleles in heterozygotes. We use a genome-scale data set spanning the phylogenetic diversity of green algae and phylogenetic comparative approaches to measure the relative selection intensity across different trait categories. We show stronger signatures of drift in lineages with more complex body architectures compared with unicellular lineages, which we consider to be a consequence of smaller effective population sizes of the more complex algae. Significantly higher rates of synonymous as well as nonsynonymous substitutions relative to other algal body architectures highlight that siphonous and siphonocladous body architectures, characteristic of many green seaweeds, form an interesting test case to study the potential impacts of genome redundancy on molecular evolution. Contrary to expectations, we show that levels of selection efficacy do not show a strong association with life cycle types in green algae. Taken together, our results underline the prominent impact of body architecture on the molecular evolution of green algal genomes.

Constraining models of dominance for nonsynonymous mutations in the human genome.

Dominance is a fundamental parameter in genetics, determining the dynamics of natural selection on deleterious and beneficial mutations, the patterns of genetic variation in natural populations, and the severity of inbreeding depression in a population. Despite this importance, dominance parameters remain poorly known, particularly in humans or other non-model organisms. A key reason for this lack of information about dominance is that it is extremely challenging to disentangle the selection coefficient (s) of a mutation from its dominance coefficient (h). Here, we explore dominance and selection parameters in humans by fitting models to the site frequency spectrum (SFS) for nonsynonymous mutations. When assuming a single dominance coefficient for all nonsynonymous mutations, we find that numerous h values can fit the data, so long as h is greater than ~0.15. Moreover, we also observe that theoretically-predicted models with a negative relationship between h and s can also fit the data well, including models with h = 0.05 for strongly deleterious mutations. Finally, we use our estimated dominance and selection parameters to inform simulations revisiting the question of whether the out-of-Africa bottleneck has led to differences in genetic load between African and non-African human populations. These simulations suggest that the relative burden of genetic load in non-African populations depends on the dominance model assumed, with slight increases for more weakly recessive models and slight decreases shown for more strongly recessive models. Moreover, these results also demonstrate that models of partially recessive nonsynonymous mutations can explain the observed severity of inbreeding depression in humans, bridging the gap between molecular population genetics and direct measures of fitness in humans. Our work represents a comprehensive assessment of dominance and deleterious variation in humans, with implications for parameterizing models of deleterious variation in humans and other mammalian species.

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease.

Disease mechanisms are usually complex and governed by the interaction of several distinct molecular processes. Complex, multidimensional datasets are a valuable resource to generate more insights into those processes, but the analysis of such datasets can be challenging due to the high dimensionality resulting, for example, from different disease conditions, timepoints, and omics capturing the process at different resolutions. Here, we showcase an approach to analyze and explore such a complex multiomics dataset in an unsupervised way by applying multi-omics factor analysis (MOFA) to a dataset generated from blood samples that capture the immune response in acute and chronic coronary syndromes. The dataset consists of several assays at differing resolutions, including sample-level cytokine data, plasma-proteomics and neutrophil prime-seq, and single-cell RNA-seq (scRNA-seq)data. Further complexity is added by having several different time points measured per patient and several patient subgroups. The analysis workflow outlines how to integrate and analyze the data in several steps: (1) Data pre-processing and harmonization, (2) Estimation of the MOFA model, (3) Downstream analysis. Step 1 outlines how to process the features of the different data types, filter out low-quality features, and normalize them to harmonize their distributions for further analysis. Step 2 shows how to apply the MOFA model and explore the major sources of variance within the dataset across all omics and features. Step 3 presents several strategies for the downstream analysis of the captured patterns, linking them to the disease conditions and potential molecular processes governing those conditions. Overall, we present a workflow for unsupervised data exploration of complex multi-omics datasets to enable the identification of major axes of variation composed of differing molecular features that can also be applied to other contexts and multi-omics datasets (including other assays as presented in the exemplary use case).

Temporal and spatial variation patterns of chlorophyll a in marine ranching under global interannual events

Marine ecosystems are facing numerous environmental challenges due to global climate change. In response to these challenges, the establishment and growth of marine ranching has emerged as a pivotal solution. Chlorophyll a concentration (Chla) is recognized as a valuable indicator for the ecological assessment of marine ranching. This study focused on the spatiotemporal distribution of Chla and its response to environmental factors according to the dataset in the marine ranching area of Haizhou Bay (Lianyungang, Jiangsu, China) from 2003 to 2022. The results showed that Chla had a significant cycle of summer > spring > autumn and was distributed evenly in the central area of the marine ranching. During interannual changes, Chla patches were centered in the central region during 2014, 2015, and 2016. The Chla patches predominantly focused on the eastern area in 2018–2019, shifting to the western area in 2020–2021. The generalized additive model (GAM) indicated that salinity, depth, temperature, biological oxygen demand (BOD5) and SiO3−–Si were the main environmental factors affecting Chla during spring, summer and autumn. However, during El Niño events, salinity, depth, temperature, BOD5 and transparency became the main environmental factors. We concluded that salinity, depth and temperature consistently played a crucial role in determining Chla under various climate conditions, and SiO3−–Si and transparency will no longer be an environmental factor limiting Chla. In addition, The effect of interannual variability on upwelling and vertical mixing of water layers may potentially alter the spatial distribution pattern of Chla. These findings can offer ideas into predicting the variation of Chla in marine ranching under global interannual events in the future. Furthermore, this can contribute to the comprehensive assessment of ecological benefits and the in-depth construction of marine ranching. Ultimately, it can provide essential data and scientific references for offshore ecological environment assessment and ecosystem restoration.

Genetic diversity during selective sweeps in non-recombining populations.

Selective sweeps, resulting from the spread of beneficial, neutral, or deleterious mutations through a population, shape patterns of genetic variation at linked neutral sites. While many theoretical, computational, and statistical advances have been made in understanding the genomic signatures of selective sweeps in recombining populations, substantially less is understood in populations with little/no recombination. We present a mathematical framework based on diffusion theory for obtaining the site frequency spectrum (SFS) at linked neutral sites immediately post and during the fixation of moderately or strongly beneficial mutations. We find that when a single hard sweep occurs, the SFS decays as 1/ x for low derived allele frequencies ( x ), similar to the neutral SFS at equilibrium, whereas at higher derived allele frequencies, it follows a 1/ x 2 power law. These power laws are universal in the sense that they are independent of the dominance and inbreeding coefficient, and also characterize the SFS during the sweep. Additionally, we find that the derived allele frequency where the SFS shifts from the 1/ x to 1/ x 2 law, is inversely proportional to the selection strength: thus under strong selection, the SFS follows the 1/ x 2 dependence for most allele frequencies, resembling a rapidly expanding neutral population. When clonal interference is pervasive, the SFS immediately post-fixation becomes U-shaped and is better explained by the equilibrium SFS of selected sites. Our results will be important in developing statistical methods to infer the timing and strength of recent selective sweeps in asexual populations, genomic regions that lack recombination, and clonally propagating tumor populations.

The Primate Major Histocompatibility Complex: An Illustrative Example of Gene Family Evolution.

Gene families are groups of evolutionarily-related genes. One large gene family that has experienced rapid evolution is the Major Histocompatibility Complex (MHC), whose proteins serve critical roles in innate and adaptive immunity. Across the ~60 million year history of the primates, some MHC genes have turned over completely, some have changed function, some have converged in function, and others have remained essentially unchanged. Past work has typically focused on identifying MHC alleles within particular species or comparing gene content, but more work is needed to understand the overall evolution of the gene family across species. Thus, despite the immunologic importance of the MHC and its peculiar evolutionary history, we lack a complete picture of MHC evolution in the primates. We readdress this question using sequences from dozens of MHC genes and pseudogenes spanning the entire primate order, building a comprehensive set of gene and allele trees with modern methods. Overall, we find that the Class I gene subfamily is evolving much more quickly than the Class II gene subfamily, with the exception of the Class II MHC-DRB genes. We also pay special attention to the often-ignored pseudogenes, which we use to reconstruct different events in the evolution of the Class I region. We find that despite the shared function of the MHC across species, different species employ different genes, haplotypes, and patterns of variation to achieve a successful immune response. Our trees and extensive literature review represent the most comprehensive look into MHC evolution to date.

Damage evolution behavior of tunnel lining concrete with non‐through crack under single‐sided sulfate attack

AbstractIn order to study the dynamic damage evolution law of tunnel‐lining concrete with non‐through crack under single‐side sulfate attack, a 420‐day sulfate attack concrete test was conducted and the uniaxial compression and acoustic emission (AE) tests were carried out on each corrosion time. The test results showed that the compressive strength of each group can be divided into rising stage and falling stage, and the existence of non‐through crack reduced the compressive strength of concrete specimens and accelerated the corrosion of concrete specimens by sulfate. AE ringing count can be divided into compaction stage, elastic stage, yield stage, and post‐peak stage; the variation patterns of AE varied at different stages. Sulfate attack caused the yield point of concrete to advance. With the increase of corrosion time, microcracks increased gradually, cement mortar was consumed continuously, concrete spalling occurred, the proportion of AE cumulative ringing count Stage II decreased gradually, and the proportion of Stage III increased. Non‐through crack changed the energy storage of Stages II and III and reduced the AE activity of concrete after corrosion. The deeper the crack was, the smaller the average cumulative ringing count was and the lower the AE activity was. AE cumulative ringing counts can be used to characterize the damage evolution of corroded concrete.

Predator exposure early in life shapes behavioral development and individual variation in a clonal fish

Predation risk is one of the most important factors generating behavioral differences among populations. In addition, recent attention focusses on predation as a potential driver of patterns of individual behavioral variation within prey populations. Previous studies provide mixed results, reporting either increased or decreased among-individual variation in response to risk. Here, we take an explicit developmental approach to documenting how among-individual variation develops over time in response to predator exposure, controlling for both genetic and experiential differences among individuals. We reared juveniles of naturally clonal Amazon mollies, Poecilia formosa, either with or without a predator visible during feedings over 4 weeks and analyzed activity during feedings, time spent feeding and number of visits to the feeding spot. (I) Predator-exposed fish did not differ from control fish in average feeding behavior, but they were less active during feeding trials. (II) In the absence of the predator, substantial changes in among-individual variation over time were detected: among-individual differences in feeding duration increased whereas differences in activity decreased, but there were no changes in feeder visits. In contrast, in the presence of a predator, among-individual variation in all three behaviors was stable over time and often lower compared to control conditions. Our work suggests that predation risk may have an overall stabilizing effect on the development of individual variation and that differences in predation risk may well lead to population-wide differences in among-individual behavioral variation.

Seasonal variations in haematological and biochemical parameters of healthy Gambian adults: Retrospective study 2018-2022.

The objectives of this study were to determine the presence and effect of seasonal variations and provide insights into trend from 2018 to 2022 in a comprehensive set of routine haematological indices and biochemical measurements in Gambian adults with no known underlying health condition. We retrieved five years of data from an electronic database and analysed 493 full blood counts and 643 biochemical data from different individuals. In this study, we focused on data from individuals with no known underlying health condition who visited the clinical diagnostic laboratory for routine medical examinations or assessments.Our study found a positive association between seasonality (wet season as the reference) and Hb (HB: 0.014(0.015), P<0.05), White blood cells (WBC) (WBC: 0.243(0.163), p = 0.0014), and neutrophils (neutrophils: 0.271(0.131), P<0.05) with exception to red blood cells (RBC) (RBC: - 0.184(0.061), P< 0.003) that showed negative association. Despite the association, the seasonal effects on our derived reference intervals for haematological indices and biochemical measurements from wet season to dry season were not statistically significant (P>0.05). In addition, we observed in our heatmap result that some laboratory parameters, including HB, RBC, haematocrit (HCT), urea, liver enzymes, and potassium, showed seasonal variation patterns throughout the year, with median levels being normal to slightly low during the dry season and normal to high during the wet season. We also found no significant difference (P>0.05) among the median values for all parameters from 2018 to 2022. Additionally, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) parameters showed a consistent declining trend from 2018 to 2022. Our study found no seasonal effects on the derived reference intervals of haematological indices and biochemical measurements. However, we observed changes in patterns for certain parameters particularly HB, RBC, liver enzymes, and potassium based on seasonality.

Decoupling and predicting natural gas deviation factor using machine learning methods

Accurately predicting the deviation factor (Z-factor) of natural gas is crucial for the estimation of natural gas reserves, evaluation of gas reservoir recovery, and assessment of natural gas transport in pipelines. Traditional machine learning algorithms, such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory Neural Networks (BiLSTM), often lack accuracy and robustness in various situations due to their inability to generalize across different gas components and temperature-pressure conditions. To address this limitation, we propose a novel and efficient machine learning framework for predicting natural gas Z-factor. Our approach first utilizes a signal decomposition algorithm like Variational Mode Decomposition (VMD), Empirical Fourier Decomposition (EFD) and Ensemble Empirical Mode Decomposition (EEMD) to decouple the Z-factor into multiple components. Subsequently, traditional machine learning algorithms is employed to predict each decomposed Z-factor component, where combination of SVM and VMD achieved the best performance. Decoupling the Z-factors firstly and then predicting the decoupled components can significantly improve prediction accuracy of all traditional machine learning algorithms. We thoroughly evaluate the impact of the decoupling method and the number of decomposed components on the model’s performance. Compared to traditional machine learning models without decomposition, our framework achieves an average correlation coefficient exceeding 0.99 and an average mean absolute percentage error below 0.83% on 10 datasets with different natural gas components, high temperatures, and pressures. These results indicate that hybrid model effectively learns the patterns of Z-factor variations and can be applied to the prediction of natural gas Z-factors under various conditions. This study significantly advances methodologies for predicting natural gas properties, offering a unified and robust solution for precise estimations, thereby benefiting the natural gas industry in resource estimation and reservoir management.

Microfluctuations in Capillary Lumens Independent of Pericyte Lining Density in the Anesthetized Mouse Cortex.

This study aimed to examine the spatiotemporal coherence of capillary lumen fluctuations in relation to spatial variations in the pericyte lining in the cortex of anesthetized mice. Two-photon microscopic angiography data (previously published) were reanalyzed, and spatial variations in capillary diameter fluctuations at rest and in capillary lining with vascular mural cells were measured along capillary centerlines. Relatively large diameters of the capillaries (5.5 μm) coincided with a dense pericyte lining, while small capillaries (4.3 μm) had a sparse pericyte lining. Temporal variations had a frequency of about 0.1 Hz with an amplitude of 0.5 μm, which were negatively correlated with pericyte lining density. Spatial frequency analysis further revealed a common pattern of spatial variations in capillary diameter and pericyte lining, but temporal variations differed. The temporal variations in capillary lumens were locally distinct from those in neighboring locations, suggesting intrinsic fluctuations independent of the pericyte lining. Capillary lumens in the brain exhibit slow microfluctuations that are independent of pericyte lining. These microfluctuations could affect the distribution of flowing blood cells and may be important for homogenizing their distribution in capillary networks.