Natural Scale Research Articles

Associations of Palmito-leic Acid and Nervonic Acid Hexosylceramides with Type 2 Diabetes Mellitus in a Prospective Nested Case-control Study Among Chinese Population

ABSTRACT Objectives We aimed to evaluate the associations of hexosylceramides (HexCers) and their ratio with incident type 2 diabetes mellitus (T2DM) and explore underlying mechanisms. Methods We conducted a nested case-control study using the Suzhou chronic disease cohort including 234 T2DM cases and 468 controls, 1:2 matched on age (±2 y) and sex. HexCer(d18:1/16:1) and HexCer(d18:1/24:1) were measured by targeted UPLC-MS/MS. Multivariable conditional logistic regression was used to estimate the associations of these HexCer species and their ratio with T2DM risk. Results After adjustment for potential confounders, compared with the lowest quartile, the highest quartile of HexCer(d18:1/24:1) was positively associated with T2DM risk (OR: 1.91; 95%CI, 1.12, 3.26; P-trend <0.05). The ratio of HexCer(d18:1/24:1) to HexCer(d18:1/16:1) showed a positive association with T2DM risk (OR: 1.89; 95%CI, 1.13, 3.18; P-trend <0.05). On the natural log scale, each SD increases in HexCer(d18:1/24:1) and its ratio to HexCer(d18:1/16:1) increased by 29% and 30%, respectively. No significant association for HexCer(d18:1/16:1) was found. Additive value of HexCer(d18:1/24:1) or HexCer(d18:1/24:1)/HexCer(d18:1/16:1) ratio for prediction of T2DM above traditional risk factors. Conclusions HexCer(d18:1/24:1) and its ratio to HexCer(d18:1/16:1) are positively associated with incident T2DM in a community-based Chinese population. Future studies are warranted to confirm our findings.

Nature's impact on human health and wellbeing: the scale matters.

Contact with nature can have a significant influence on human physical and mental health and wellbeing. As such, various concepts and theories as well as therapeutic approaches have been developed. The term "nature," however, covers a broad range of size and scales, ranging from individuals or small groups of animals or plants, certain ecosystems toward landscapes. The purpose of this paper is to differentiate concepts, theories, and therapy forms according to the scales of nature. We base our conceptional approach on the biological/ecological scales of species/individuals, ecosystems/land-use types, and landscapes. Based on a review, we differentiate the current state of the utilization of greenspace exposure measurements and measures assessing mental health according to these scales. We argue that a clear differentiation of biological and ecological scales provides a better understanding of the impact of nature with its components, characteristics, and dynamics on human health and wellbeing. Our paper also supports further inter- and transdisciplinary research as well as methodological approaches with regard to environment and health, such as environmental public health.

Bonding with nature: a validation of the dispositional empathy with nature scale in Italy.

This study proposes a psychometric validation of the Italian version of the Dispositional Empathy with Nature (DEN). Scientific research data has found high levels of environmental concern among people around the world, showing that majority of the population is aware of the seriousness of the environmental problems we are witnessing, as well as is conscious of the damage that some of their behaviors cause to the environment. Based on this premise, Empathy with Nature could be an important educational strategy for addressing the environmental crisis. A study was conducted involving 307 Italian adults (CFA = 146; 45.9% women; 54.1% man; Mean age = 34.65; SD = 11.770); (EFA = 161; 50.3% women; 49.7% man; Mean age = 34.30; SD = 10.360) to o assess the psychometric properties of a scale in the Italian context. The study aimed to establish the internal consistency of the DEN scale and evaluate its convergent, discriminant, and predictive validity. Both confirmatory and exploratory factor analyses, using a split sample, supported the one-factor structure consistent with the original version proposed by Tam. These findings strongly suggest that the DEN scale is reliable and valid in the Italian context.

Dynamical Generation of Electroweak Scale From the Conformal Sector: A Strongly Coupled Higgs via the Dyson–Schwinger Approach

AbstractA novel pathway to generate the electroweak (EW) scale via non‐perturbative dynamics of a conformally invariant scalar sector at the classical level is proposed. A method is provided to estimate the non‐perturbative EW scale generation using the exact solution of the background equations of motion in a scalar theory via the Dyson–Schwinger approach. Particularly, an analytical result for the Higgs mass in the strongly coupled regime is found in terms of its quartic self interaction term and the cut‐off scale of the theory. It is also shown that the Higgs sector is an essential part of the standard model as, without it, a Yang–Mills gauge theory cannot acquire mass even if a self‐interaction term is present. A more realistic model building with possible solutions to the gauge hierarchy problem and, in general, to the dynamical generation of any scales scales in nature, be it the visible sector or the dark sector, is led by this analysis.

Bioinspired design of a tissue-engineered ray with machine learning.

In biomimetic design, researchers recreate existing biological structures to form functional devices. For biohybrid robotic swimmers assembled with tissue engineering, this is problematic because most devices operate at different length scales than their naturally occurring counterparts, resulting in reduced performance. To overcome these challenges, here, we demonstrate how machine learning-directed optimization (ML-DO) can be used to inform the design of a biohybrid robot, outperforming other nonlinear optimization techniques, such as Bayesian optimization, in the selection of high-performance geometries. We show how this approach can be used to maximize the thrust generated by a tissue-engineered mobuliform miniray. This results in devices that can swim at the millimeter scale while more closely preserving natural locomotive scaling laws. Overall, this work provides a quantitatively rigorous approach for the engineering design of muscular structure-function relationships in an automated fashion.

Enhancing active ingredient biosynthesis in Chinese herbal medicine: biotechnological strategies and molecular mechanisms.

Chinese herbal medicine (CHM) is a fundamental component of traditional Chinese medical practice, offering a rich source of natural remedies with significant therapeutic potential. However, the scarcity of active ingredients and complex extraction procedures present substantial challenges to their widespread clinical application. This review aims to address this gap by exploring the potential of modern biotechnological advancements in enhancing the biosynthesis of these valuable compounds. The study takes a comprehensive approach, delving into the chemical composition of CHM's active ingredients and elucidating their biosynthetic pathways and molecular regulatory mechanisms. Additionally, it surveys recent progress in extraction methodologies and evaluates engineering strategies aimed at synthetic production. This multifaceted analysis forms the foundation for examining the role of synthetic biology in augmenting CHM's active ingredient synthesis. Our examination provides insights into the intricate biosynthetic pathways governing the formation of CHM's active ingredients, as well as the complex molecular regulatory networks that underlie these processes. Furthermore, the review highlights advancements in extraction techniques, demonstrating their ability to streamline and enhance the isolation of these compounds. Engineering approaches for synthetic production, including metabolic engineering and synthetic biology tools, are assessed for their potential to overcome natural limitations and scale up production. By integrating insights from biosynthesis, molecular regulation, extraction methodologies, and synthetic biology, this review establishes a robust theoretical framework for enhancing the production of CHM's active ingredients. The proposed strategies and practical guidance aim to facilitate their broader utilization in modern medicine while promoting sustainability and accessibility within this invaluable medicinal heritage.

Attosecond microscopy —Advances and outlook

Attosecond microscopy aims to record electron movement on its natural length and time scale. It is a gateway to understanding the interaction of matter and light, the coupling between excitations in solids, and the resulting energy flow and decoherence behavior, but it demands simultaneous temporal and spatial resolution. Modern science has conquered these scales independently, with ultrafast light sources providing sub-femtosecond pulses and advanced microscopes achieving sub-nanometer resolving power. In this perspective, we inspect the challenges raised by combining extreme temporal and spatial resolution and then highlight how upcoming experimental techniques overcome them to realize laboratory-scale attosecond microscopes. Referencing proof-of-principle experiments, we delineate the techniques’ strengths and their applicability to observing various ultrafast phenomena, materials, and sample geometries.

Mean relative error and standard relative deviation

AbstractStatistics on the natural log scale, such as differences, regression coefficients, and standard deviations, frequently arise when analyzing log‐transformed data or modeling binary, count, or time‐to‐event data. The statistical properties of log‐transformed estimators (for example, log odds ratio estimators) frequently appear in statistical articles. Understanding the magnitude of these quantities can be useful. Remarkably, many can be readily interpreted, without exponentiation. In this note, we introduce four new interpretations. While a log‐scale standard deviation can be interpreted as an approximate coefficient of variation describing variation about an arithmetic mean, we argue it can be more useful to interpret it exactly as a “standard relative deviation” describing variation about a geometric mean. For positive‐valued estimators, we show how the standard error of a log‐transformed estimator can be interpreted as the “standard relative error” describing the precision of the untransformed estimator. We also show how the bias and root mean squared error of a the log‐transformed estimator can be interpreted as the “mean relative error” and “root mean squared relative error” of the untransformed estimator. We illustrate the usefulness of our interpretations for (i) the usual scale parameter of a lognormal distribution, (ii) statistical properties of a log odds ratio estimator, and (iii) a single measure of the precision of an odds ratio estimator which agrees with its usual, asymmetric 95% confidence interval.

The Power of Connection: Discovering the Effects of Formal and Nonformal Environmental Education on Students’ Connectedness to Nature

This study examined the relationship between students’ feeling of connectedness to nature (CN) and their exposure to environmental education (EE) experiences at formal or nonformal environmental education organizations. CN, often developed through EE programs, is a good predictor of environmentally responsible behavior (ERB) and is also an indicator of self-nature overlap. The Connectedness to Nature Scale Revised (CNS-R), and the Inclusion of Nature in Self (INS) surveys were administered at formal (N=3) and nonformal (N=3) environmental education organizations (EEOs), surveying students (N= 5,994) aged 7-14. Students participated in a range of program types including homeschool, summer camps, extended day, and overnight programs. Aggregated pretest and posttest survey data were analyzed to determine how students’ feelings of CN were affected by their EE experience and how these scores were influenced by age, duration, and frequency.

Incompressible limit for the compressible viscoelastic fluids in critical space

Abstract In this article, we consider the incompressible limit of global-in-time strong solutions with arbitrary large initial velocity for the compressible viscoelastic fluids in the sense of critical Besov framework. We decouple our compressible system into two coupling sub-systems by introducing a skew symmetric matrix, which is related to the deformation tensor. This work generalizes the similar result obtained by Hu et al. (Incompressible limit for compressible viscoelastic flows with large velocity, Advances in Nonlinear Analysis 12 (2023), 20220324) to the critical functional space with respective to the natural scaling of the system. The proof relies on the dispersive property of the linear system on the high-frequency regime and the parabolic property on the low-frequency regime. The dispersion tends to disappear when λ \lambda tends to infinite, but having large λ \lambda provides strong dissipation on the potential part of the velocity and thus makes the flow almost incompressible. In addition, by exploiting the intrinsic structure of the viscoelastic system, we obtain the global uniform estimates of the solutions near equilibrium.

Creep enhancement and sliding in a temperate, hard-bedded alpine glacier

Abstract. Glacier internal deformation is usually described by Glen's flow law using two material parameters: the creep factor (A) and the flow law exponent (n). However, the values of these parameters and their spatial and temporal variability are rather uncertain due to the difficulty in quantifying internal strain and stress fields at natural scales. In this study, we combine 1-year-long continuous measurements of borehole inclinometry and surface velocity with three-dimensional full-Stokes ice flow modeling to infer ice rheologies and sliding velocities for the ablation zone of the Argentière Glacier, a temperate glacier in the French Alps. We demonstrate that the observed deformation rate profile has limited sensitivity to the flow law exponent (n) and instead mainly reflects an increase in the creep factor (A) with depth, with A departing from its surface value by up to a factor of 2.5 below 160 m depth. We interpret this creep factor enhancement as an effect of increasing interstitial water content with depth (from 0 % to 1.3 %), which results in an average value of A=148 MPa−3 a−1. We further observe that internal ice deformation exhibits seasonal variability similar to that concerning surface velocity, indicating that the local basal sliding velocity exhibits no significant seasonal variation. We suggest that these changes in deformation rate are due to variations in the stress field, driven by contrasting changes in subglacial hydrology conditions between the sides and center of the glacier. Our study provides further evidence that borehole inclinometry, combined with full-Stokes flow modeling, allows for the constraining of both ice rheology and basal friction at scales that cannot be inferred from surface velocity measurements alone.

Janus Protein Nanoparticles Tailoring Bicontinuous Aerogels for Efficient Heavy Metal Ion Absorption.

Bicontinuous structures are exquisite interpenetrating constructs with an optimal balance between connectivity and surface area. Such unique geometry favors exceptional mechanical properties and efficient inward mass diffusion essential for an absorbent material. Although bicontinuous structures are found across many length scales in nature, synthesizing artificial analogs using biological building blocks remains largely unexplored. In this study, it is shown that manipulation of the surface chemistry of rapeseed cruciferin nanoparticles (≈50nm) leads to the formation of a highly amphiphilic stabilizer, ensuring equal wetting of water and oil phases in a demixed system, thereby enabling the formation of bicontinuous emulsions. By further eliminating both volatile liquid phases (water and toluene) through freeze-drying, bicontinuous emulsions are transformed into bicontinuous aerogels featuring highly interpenetrating networks with uniform domain size. These materials, characterized by high surface area (224 m2g-1) and mechanical robustness, can efficiently absorb various heavy metal ions multiple times displaying excellent absorption capacity (up to 200mgg-1) and efficiency (less than 30min). This study is at the forefront of constructing biomacromolecular bicontinuous structures, potentially expanding their applications in diverse fields such as food, cosmetics, and medicine.

Unveiling Different Probability Distribution Functions and Their Applications

Nowadays, data is everywhere from social economic activities to the observation of natural phenomena, the complexity and scale of data requires researchers to master more advanced data analysis tools. As a bridge between random events and mathematical models, probability distribution function not only provides a theoretical basis for describing data characteristics, but also provides an important means for quantitative analysis of data and modeling prediction. This paper focuses on several typical probability distributions which have wide application background and deep theoretical foundation in their respective fields. This paper mainly focuses on two core issues. The first is the introduction of various probability distribution functions and their applications, while the second is how to use these distribution functions for effective data analysis and prediction. Through a series of empirical studies, this paper verifies the applicability and validity of different probability distribution functions in different fields. The results show that the correct selection and application of probability distribution function can significantly promote the accuracy and efficiency of data analysis, and provide strong support for decision making. Finally, the research in this paper not only makes the theoretical system of probability distribution function more abundant, but also offers some valuable references to practical applications. By revealing the key role of different distribution functions in data analysis and prediction, this paper provides new ideas and new methods for interdisciplinary research.

Rare, noise-induced, bypass transition in plane Couette flow can bypass instantons.

This paper presents a study of rare noise-induced transitions from stable laminar flow to transitional turbulence in plane Couette flow, which we will term buildup. We wish to study forced paths that go all the way from laminar to turbulent flow and to focus the investigation on whether these paths share the properties of noise-induced transitions in simpler systems. The forcing noise has a red spectrum without any component in the natural, large-scale, linear receptivity range of the flow. As we decreased the forcing energy injection rate, the transitions became rare. The rare paths from laminar to turbulent flow are computed using adaptive multilevel splitting, a rare event simulation method, and are validated against direct numerical simulations at moderately small energy injection rates. On the computed trajectories, the flow manages to nonlinearly redistribute energy from the small forced scales to the unforced large scales so that the reactive trajectories display forced streamwise velocity tubes at the natural scale of velocity streaks. As the trajectory proceeds, these tubes gradually grow in amplitude until they cross the separatrix between laminar and turbulent flow. Streamwise vortices manifest themselves only after velocity tubes have reached near-turbulent amplitude, displaying a two-stage process reminiscent of the "backward" path from turbulence to laminar flow. We checked that these were not time-reversed turbulence collapse paths. As the domain size is increased from a minimal flow unit (MFU) type flow at L_{x}×L_{z}=6×4 (in half gap units) to a large domain L_{x}×L_{z}=36×24, spatial localization and then extension of the generated coherent streaks and vortices in the spanwise direction is observed in the reactive paths. The paths systematically computed in MFU display many of the characteristics of instantons that often structure noise-induced transitions: such as concentration of trajectories, exponentially increasing waiting times before transition, and Gumbel distribution of trajectory durations. However, bisections started from successive states on the reactive trajectories indicate that for all sizes and energy injection rates investigated, the trajectory lacks two key ingredients of instantons. First, they do not visit the neighborhood of the nearest saddle point and do not display the natural relaxation path from that saddle to transitional wall turbulence. This discrepancy is observed for all system sizes. Second, the reactive paths do not concentrate more and more around the same trajectory as energy injection rate is decreased, but instead gradually move in phase space. They might reconnect with instantons at very small energy injection rate and exceedingly long waiting times. They would explain why classical instanton calculations have proved to be tremendously difficult in wall flows.

Upscaling drag reduction of rotational rheometer to linear pipe flow

Drag reduction (DR) is a phenomenon associated with adding small amounts of drag-reducing agents to reduce friction causing a reduction in the pressure gradient needed for pumping a solution under turbulent conditions. Traditionally, DR has been measured with linear flow devices, such as flow loops. Recent developments in rheological measurement techniques have enabled the evaluation of DR using rotational rheometers. However, due to differences in flow nature and length scale, direct comparison between outcomes is not possible. This study introduces nondimensional quantities, namely dimensionless pressure difference for pipe flow and dimensionless torque for rheometers, as the basis for comparing the DR results of linear and rotational flow. Theoretically, the DR expressions derived from these dimensionless quantities showed similar structures, featuring a factor with quadratic viscosity and linear density terms. Experimentally, DR tests were carried out using an industrial-scale pipe flow and a laboratory-scale rotational rheometer, using solutions of tap water with high-viscosity partially hydrolyzed polyacrylamide at two molecular weights. Samples tested with the rheometer were collected from flow loop experiments, ensuring the same polymer solutions were tested with both devices. Results showed that DR, expressed as a function of Reynolds number using nondimensional measurements, follows similar behavior for both instruments. The experimental DR results from the rheometer were extrapolated to the pipe flow scale, showing overall agreement between extrapolated and experimental results. These findings suggest that rotational rheometers could effectively replace linear flow instruments for screening polymer solutions in DR applications.

Stage IV baryonic feedback correction for non-Gaussianity inference

Abstract Non-Gaussian statistics of the projected weak lensing field are powerful estimators that can outperform the constraining power of the two-point functions in inferring cosmological parameters. This is because these estimators extract the non-Gaussian information contained in the small scales. However, fully leveraging the statistical precision of such estimators is hampered by theoretical uncertainties, such as those arising from baryonic physics. Moreover, as non-Gaussian estimators mix different scales, there exists no natural cut-off scale below which baryonic feedback can be completely removed. We therefore present a Bayesian solution for accounting for baryonic feedback uncertainty in weak lensing non-Gaussianity inference. Our solution implements Bayesian model averaging (BMA), a statistical framework that accounts for model uncertainty and combines the strengths of different models to produce more robust and reliable parameter inferences. We demonstrate the effectiveness of this approach in a Stage IV convergence peak counts analysis, including three baryonic feedback models. We find that the resulting BMA posterior distribution safeguards parameter inference against biases due to baryonic feedback, and therefore provides a robust framework for obtaining accurate cosmological constraints at Stage IV precision under model uncertainty scenarios.

Expected improvements in precision when integrating opportunistic close-kin mark-recapture data into fisheries stock assessments

Expected improvements in precision when integrating opportunistic close-kin mark-recapture data into fisheries stock assessments

Validation of Multi-Frequency Inductive-Loop Measurement System for Parameters of Moving Vehicle Based on Laboratory Model.

The paper presents research on a system for measuring the parameters of a vehicle in motion and the process of validating it under laboratory conditions. The measurement system uses four inductive-loop (IL) sensors, two slim ILs and two wide ILs. The vehicle speed, wheelbase, length, and overhangs are all determined on the basis of a vehicle magnetic profile (VMP) waveform. VMPs are captured from the continuous IL-based impedance measurement. The impedance measurement for a single IL is performed simultaneously at three carrier frequencies. The uncontrolled measurement conditions in the field on a real road test bed (RTB), including the speed of passing vehicles, motivated the development of a laboratory test bed (LTB). This LTB serves as a model of an existing measurement setup installed on the road, i.e., the RTB. The LTB includes IL sensors and a movable model of the vehicle made in 1:50 scale. The LTB enables validation of the whole measurement system in the vehicle speed range from 10 km/h up to 150 km/h in 5 km/h increments in fully controlled conditions. The measurement results are presented in the distance domain, calculated from the VMPs and the measured speed. The largest errors in estimating vehicle-model body parameters, on a natural scale, do not exceed 4.3 cm.

Testing the Biophilia Hypothesis Through the Human and Nature Scale on Four Continents

Testing the Biophilia Hypothesis Through the Human and Nature Scale on Four Continents

Development of a deep learning–based feature stream network for forecasting riverine harmful algal blooms from a network perspective

Development of a deep learning–based feature stream network for forecasting riverine harmful algal blooms from a network perspective