Association between standing height and physical disability among U.S. adults aged 60 years and older: findings from NHANES 2015-2018.

BACKGROUND: Physical disability is an important cause of affecting the quality of life in the elderly. The association between standing height and physical disability is less studied. PURPOSE: The purpose of this study is to investigate the possible link between standing height and physical disability among U.S. adults aged 60 years and older. METHODS: The cross-sectional data were obtained from the US National Health and Nutrition Examination Survey (NHANES) 2015-2018. Physical disability was assessed by six questions: "Have serious difficulty hearing (SDH)?", "Have serious difficulty seeing (SDS)?", "Have serious difficulty concentrating (SDC)?", "Have serious difficulty walking (SDW)?", "Have difficulty dressing or bathing (DDB)?" and "Have difficulty doing errands alone (DDEA)?". Responses to these questions were "yes" or "no". Answer yes to one of the above six questions was identified as physical disability. Standing height (cm) was measured with an altimeter. Multivariate logistic regression was performed to examine the possible link between standing height and physical disability after adjustment for all covariates. RESULTS: A total of 2624 participants aged ≥ 60 years were included in our study, including 1279 (48.7%) females and 1345 (51.3%) males. The mean age of participants was 69.41 ± 6.82 years. After adjusting for all potential confounders, the inverse relationship between standing height and all physical disability (APD) was statistically significant (OR = 0.976, 95%CI:0.957-0.995). In addition, among six types of physical disability (SDH, SDS, SDC, SDW, DDB, DDEA), standing height was also a protective factor for SDW (OR = 0.961, 95%CI:0.939-0.983) and DDEA (OR = 0.944, 95%CI:0.915-0.975) in the full-adjusted model. CONCLUSION: The cross-sectional population based study demonstrates that standing height is a protective factor for physical disability among U.S. adults aged 60 years and older.

Plasmonic Metal Mediated Charge Transfer in Stacked Core-Shell Semiconductor Heterojunction for Significantly Enhanced CO2 Photoreduction.

Construction of core-shell semiconductor heterojunctions and plasmonic metal/semiconductor heterostructures represents two promising routes to improved light harvesting and promoted charge separation, but their photocatalytic activities are respectively limited by sluggish consumption of charge carriers confined in the cores, and contradictory migration directions of plasmon-induced hot electrons and semiconductor-generated electrons. Herein, a semiconductor/metal/semiconductor stacked core-shell design is demonstrated to overcome these limitations and significantly boost the photoactivity in CO2  reduction. In this smart design, sandwiched Au serves as a "stone", which "kills two birds" by inducing localized surface plasmon resonance for hot electron generation and mediating unidirectional transmission of conduction band electrons and hot electrons from TiO2  core to MoS2  shell. Meanwhile, upward band bending of TiO2  drives core-to-shell migration of holes through TiO2 -MoS2  interface. The co-existence of TiO2  â†’ Au â†’ MoS2  electron flow and TiO2  â†’ MoS2  hole flow contributes to spatial charge separation on different locations of MoS2  outer layer for overall redox reactions. Additionally, reduction potential of photoelectrons participating in the CO2  reduction is elaborately adjusted by tuning the thickness of MoS2  shell, and thus the product selectivity is delicately regulated. This work provides fresh hints for rationally controlling the charge transfer pathways toward high-efficiency CO2  photoreduction.

Embedding Nano-Piezoelectrics into Heterointerfaces of S-Scheme Heterojunctions for Boosting Photocatalysis and Piezophotocatalysis.

Step-scheme (S-scheme) heterojunctions have exhibited great potential in photocatalysis due to their extraordinary light harvesting and high redox capacities. However, inadequate S-scheme recombination of useless carriers in weak redox abilities increases the probability of their recombination with useful ones in strong redox capabilities. Herein, a versatile protocol is demonstrated to overcome this impediment based on the insertion of nano-piezoelectrics into the heterointerfaces of S-scheme heterojunctions. Under light excitation, the piezoelectric inserter promotes interfacial charge transfer and produces additional photocarriers to recombine with useless electrons and holes, ensuring a more thorough separation of powerful ones for CO2 reduction and H2 O oxidation. When introducing extra ultrasonic vibration, a piezoelectric polarization field is established, which allows efficient separation of charges generated by the embedded piezoelectrics and expedites their recombination with weak carriers, further increasing the number of strong ones participating in the redox reactions. Encouraged by the greatly improved charge utilization, significantly enhanced photocatalytic and piezophotocatalytic activities in CH4 , CO, and O2 production are achieved by the designed stacked catalyst. This work highlights the importance in strengthening the necessary charge recombination in S-scheme heterojunctions and presents an efficient and novel strategy to synergize photocatalysis and piezocatalysis for renewable fuels and value-added chemicals production.

A microfluidic-chip-based system with loop-mediated isothermal amplification for rapid and parallel detection of Trichomonas vaginalis and human papillomavirus.

Cervical cancer is a significant global health issue primarily caused by high-risk types of human papillomavirus (HPV). Recent studies have reported an association between Trichomonas vaginalis (T. vaginalis) infections and HPV infections, highlighting the importance of simultaneously detecting these pathogens for effective cervical cancer risk management. However, current methods for detecting both T. vaginalis and HPV are limited. In this study, we present a novel approach using a microfluidic-chip-based system with loop-mediated isothermal amplification (LAMP) for the rapid and parallel detection of T. vaginalis, HPV16, HPV18, and HPV52 in a reagent-efficient and user-friendly manner. Compared to conventional LAMP assays in tubes, our system exhibits enhanced sensitivity with values of 2.43 × 101, 3.00 × 102, 3.57 × 101, and 3.60 × 102 copies per reaction for T. vaginalis, HPV16, HPV18, and HPV52, respectively. Additionally, we validated the performance of our chip by testing 47 clinical samples, yielding results consistent with the diagnostic methods used by the hospital. Therefore, our system not only offers a promising solution for concurrent diagnosis of T. vaginalis and HPV infections, particularly in resource-limited areas, due to its cost-effectiveness, ease of use, and rapid and accurate detection performance, but can also contribute to future research on the co-infection of these two pathogens. Moreover, the system possesses the capability to simultaneously detect up to 22 different types of pathogens, making it applicable across a wide range of domains such as diagnostics, food safety, and water monitoring.

Detecting hidden nodes in networks based on random variable resetting method.

Reconstructing network connections from measurable data facilitates our understanding of the mechanism of interactions between nodes. However, the unmeasurable nodes in real networks, also known as hidden nodes, introduce new challenges for reconstruction. There have been some hidden node detection methods, but most of them are limited by system models, network structures, and other conditions. In this paper, we propose a general theoretical method for detecting hidden nodes based on the random variable resetting method. We construct a new time series containing hidden node information based on the reconstruction results of random variable resetting, theoretically analyze the autocovariance of the time series, and finally provide a quantitative criterion for detecting hidden nodes. We numerically simulate our method in discrete and continuous systems and analyze the influence of main factors. The simulation results validate our theoretical derivation and illustrate the robustness of the detection method under different conditions.

Prolonged Rather Than Early Childhood Parent-Child Separation Predicts Change in Molecular Markers of Cellular Aging: A Consideration of the Role of Adolescence.

Early life adversity is a major risk factor for the onset of psychopathology, and cellular aging may be a mechanism underlying these associations. It is unknown whether and how the pattern (timing and duration) of parent-child separation is associated with accelerated cellular aging, particularly with respect to functional aging and replicative senescence, indexed by mitochondrial DNA copy number (mtDNAcn) elevation and telomere length (TL) attrition. This cohort study included 252 rural adolescents (mean age 11.62 years, SD: 1.56). Nearly one in five participants were persistently separated from both parents since birth. Compared with participants who never separated from their parents, adolescents with prolonged parent-child separation had higher acceleration both in functional aging and replicative senescence of cells. However, that was not the case in adolescents who experienced parent-child separation in early childhood but regained stable parental care during adolescence. These findings indicate that pubertal development reopens a window of opportunity for buffering the adverse biological effect based on significant improvements in the supportiveness of the caregiving environment relative to that in childhood. Translating such knowledge to inform intervention and prevention strategies for youths exposed to adversity is a critical goal for the field.

Construction and Research of Multiple Stimuli-Responsive 2D Photonic Crystal DNA Hydrogel Sensing Platform with Double-Network Structure and Signal Self-Expression.

The stimuli-responsive DNA hydrogel has attracted wide attention in the fields of chemical and biological sensing. However, it is still a challenge to integrate characteristics with low-cost, high mechanical strength, and signal self-expression into a DNA hydrogel simultaneously. Herein, a stimuli-responsive 2D photonic crystal double network DNA hydrogel (2D PhC DN-DNA hydrogel) sensing platform is developed via combining the signal self-expression of 2D PhC array with the selective recognition of polyacrylamide (PAM)/DNA DN hydrogel. The change of DNA configuration induced by specific target triggers the change of 2D PhC DN-DNA hydrogel volume, leading to a shift of the Debye diffraction ring diameter. In order to verify the feasibility of this strategy, the 2D PhC DN-DNA hydrogel with C-rich sequences is chosen as a proof-of-concept. The results indicate that the hydrogel has good detection performance for pH and Ag+/Cys. And the Debye diffraction ring diameter of the hydrogel is correlated with the concentration of the Ag+/Cys in the range of 0.5-20 µM. Compared with previously pure DNA hydrogel sensing platform, the 2D PhC DN-DNA hydrogel features low-cost preparation process and label-free determination. Meanwhile, only a laser pointer and a ruler are needed for the determination of targets, which shows that the hydrogel has application prospect in the development of portable response equipment.

Emerging Stacked Photocatalyst Design Enables Spatially Separated Ni(OH)2 Redox Cocatalysts for Overall CO2 Reduction and H2 O Oxidation.

Construction of photocatalytic systems with spatially separated dual cocatalysts is considered as a promising route to modulate charge separation/transfer, promote surface redox reactivities, and prevent unwanted reverse reactions. However, past efforts on the loading of spatially separated double-cocatalysts are limited to hollow structured semiconductors with inner/outer surface and monocrystalline semiconductors with different exposed facets. To overcome this limitation, herein, enabled by a unique stacked photocatalyst design, a facile and versatile strategy for spatial separation of redox cocatalysts on various semiconductors without structural and morphological restriction is demonstrated. The smart design begins with the deposition of light-harvesting semiconductors on reduced graphene oxide (rGO) nanosheets, followed with the coverage of Ni(OH)2 outer layer. The ternary photocatalysts exhibit superior activities and stabilities of H2 O oxidation and selective CO2 -to-CO reduction, remarkably surpassing other counterparts. The origin of the enhanced performance is attributed to the synergistic interplay of rGO@Ni(OH)2 reduction cocatalysts surrounding the semiconductors and Ni(OH)2 oxidation cocatalysts directly supported by the semiconductors, which mitigates the charge recombination, supplies highly active and selective sites for overall reactions, and preserves the semiconductors from photocorrosion. This work presents a new approach to regulating the position of dual cocatalysts and ameliorating the net efficiency of photoredox catalysis.

Control of Liquid Crystal Microarray Optical Signals Using a Microspectral Mode Based on Photonic Crystal Structures.

Herein, a novel liquid crystal microarray (LCM) film with optical regulation ability is first constructed by combining liquid crystals (LC) and the highly ordered microporous structure of inverse opal photonic crystals (IOPhCs). The LCM films are fabricated by infiltrating LC molecules into the LC polymer with the structure of IOPhCs, and their properties are very different from those without the LC. Interestingly, the optical property of LCM films can be controlled by changing the orientation of LC molecules, which varies with the interfacial force. In combination with polarization images, spectral reflection peak, circular dichroism spectra, potential difference, and fluorescence images of LCM films, the mechanism of this change is investigated. It is found that the exposed basic group of single-stranded DNA is the key to the change of the optical property of LC microarrays. Meanwhile, the optical signals of LC microarrays based on the PhCs provide a novel LC signal mode for an LC sensing system (microspectral signal mode), and it can be recorded by a fiber-optic spectrometer, which is a great improvement on LC sensing signals. Therefore, the LC microarray sensing signal can be used for accurate analysis of targets by the change of the reflection peak intensity of PhCs. When the LC molecules are induced by different aptamers, the LC microarray sensing interface can be further used for the determination of different targets, such as cocaine and Hg2+. The research on LCM films is of significant value for the development of LC sensing technology and also shows great application prospects in biochemical sensing fields.

A two-dimensional photonic crystal hydrogel biosensor for colorimetric detection of penicillin G and penicillinase inhibitors.

A simple penicillinase functionalized two-dimensional photonic crystal hydrogel (2DPPCH) biosensor was developed for colorimetric detection of penicillin G and penicillinase inhibitors. The penicillinase can specifically recognize penicillin G and catalyze it to produce penicilloic acid, which decreases the pH of the hydrogel microenvironment and shrinks the pH-sensitive hydrogel. The particle spacing decrease of the 2D photonic crystal array induced by the hydrogel shrinkage further causes a blue-shift in the diffraction wavelength. While the hydrolysis reaction is repressed upon treatment with clavulanate potassium (a kind of penicillinase inhibitor), no significant change in the diffraction wavelength is found. The detection of targets can be achieved by measuring the Debye diffraction ring diameter or observing the structural color change in the visible region. The lowest detectable concentrations for penicillin G and clavulanate potassium are 1 µM and 0.1 µM, respectively. Moreover, the 2DPPCH is proved to exhibit high selectivity and an excellent regeneration property, and it shows satisfactory performance for penicillin G analysis in real water samples.

Detecting interactions in discrete-time dynamics by random variable resetting.

Detecting the interactions in networks helps us to understand the collective behaviors of complex systems. However, doing so is challenging due to systemic noise, nonlinearity, and a lack of information. Very few researchers have attempted to reconstruct discrete-time dynamic networks. Recently, Shi et al. proposed resetting a random state variable to infer the interactions in a continuous-time dynamic network. In this paper, we introduce a random resetting method for discrete-time dynamic networks. The statistical characteristics of the method are investigated and verified with numerical simulations. In addition, this reconstruction method was evaluated for limited data and weak coupling and within multiple-attractor systems.

A self-healing smart photonic crystal hydrogel sensor for glucose and related saccharides.

A self-healing smart PhC hydrogel sensor that combines the optical property of photonic crystal and the dynamic regeneration property of boronate ester bond has been prepared for determination of glucose and related saccharides using Debye diffraction ring detection. The boronate ester bond formed through phenylboronic acid and dopamine endows the hydrogel network self-healing ability, and the tensile stress of the healing hydrogel can recover to 94.4%; this excellent self-healing property can effectively improve the reliability and lifetime of the hydrogel. Due to the high bonding capacity between 1,2- and 1,3-diol and phenylboronic acid, the hydrogel sensor has a good recognition ability for glucose and related saccharides. The reaction between the monosaccharides and the phenylboronic acid group makes the sensor swell and the diameter of the Debye diffraction ring decrease. The sensor shows good reuse and responsive ability for saccharides; the RSD of the recoverability assays is 4.3%. The determination range of the sensor to glucose is 0.5 to 12 mM. The sensor also has good response to glucose in urine, exhibiting potential application value in the preliminary screening of diabetes. Although the sensor has poor selectivity for specific monosaccharides, the process of measuring the Debye ring makes the determination no longer rely on expensive and complicated equipment and greatly simplifies the determining process and reduces the cost of determination, which shows a broad application prospect. The boronate ester bond formed through phenylboronic acid and dopamine results in the self-healing property of hydrogel network, which can effectively improve the reliability and lifetime of hydrogel. And due to the high bonding capacity between 1,2- and 1,3-diol and phenylboronic acid, the smart hydrogel sensor has a good recognition ability for glucose and related saccharides. The reaction between the monosaccharides and the phenylboronic acid group breaks the original boronate ester bond; this will lead to a decrease in cross-linking density of the PhC hydrogel sensor and further makes the sensor swell and the diameter of the Debye diffraction ring decrease.

Single Target SAR 3D Reconstruction Based on Deep Learning.

Synthetic aperture radar tomography (TomoSAR) is an important 3D mapping method. Traditional TomoSAR requires a large number of observation orbits however, it is hard to meet the requirement of massive orbits. While on the one hand, this is due to funding constraints, on the other hand, because the target scene is changing over time and each observation orbit consumes lots of time, the number of orbits can be fewer as required within a narrow time window. When the number of observation orbits is insufficient, the signal-to-noise ratio (SNR), peak-to-sidelobe ratio (PSR), and resolution of 3D reconstruction results will decline severely, which seriously limits the practical application of TomoSAR. In order to solve this problem, we propose to use a deep learning network to improve the resolution and SNR of 3D reconstruction results under the condition of very few observation orbits by learning the prior distribution of targets. We use all available orbits to reconstruct a high resolution target, while only very few (around 3) orbits to reconstruct a low resolution input. The low-res and high-res 3D voxel-grid pairs are used to train a 3D super-resolution (SR) CNN (convolutional neural network) model, just like ordinary 2D image SR tasks. Experiments on the Civilian Vehicle Radar dataset show that the proposed deep learning algorithm can effectively improve the reconstruction both in quality and in quantity. In addition, the model also shows good generalization performance for targets not shown in the training set.

A responsive photonic crystal film sensor for the ultrasensitive detection of uranyl ions.

As an effective nuclear energy resource, uranium plays an important role in industry and energy but the wastes of uranium also cause radioactive contamination, which is harmful to the environment and the human body. Herein, a responsive photonic crystal (PC) film sensor for the ultrasensitive and label-free detection of uranyl ions (UO22+) has been proposed, which is easy to construct and does not need to be combined with a hydrogel. The PC film is not pH-sensitive because it is obtained by the self-assembly of methyl methacrylate-acrylonitrile co-polymeric nanospheres (PMMA-AN). These nanospheres were modified with amidoxime groups, which have a good coordination ability with UO22+. The bindings between nanospheres and UO22+ change the refractive index and disturb the face-centered cubic structure of the film, which leads to a decrease in the diffraction peak intensity of the PC film. The sensor works in the concentration range of 10 pM to 100 µM for UO22+ determination and the decreased intensities of the diffraction peaks are linearly correlated with the logarithm of UO22+ concentration in the range from 1 nM to 100 µM. Moreover, the sensor shows good selectivity for UO22+ and can also perform the determination of UO22+ in a real sample. The responsive PC film sensor shows great potential in the label-free and ultrasensitive detection of UO22+.

Detecting network structures from measurable data produced by dynamics with hidden variables.

Depicting network structures from measurable data is of significance. In real-world situations, it is common that some variables of networks are unavailable or even unknown. These unavailable and unknown variables, i.e., hidden variables, will lead to much reconstruction error, even make reconstruction methods useless. In this paper, to solve hidden variable problems, we propose three reconstruction methods, respectively, based on the following conditions: statistical characteristics of hidden variables, linearizable hidden variables, and white noise injection. Among them, the method based on white noise injection is active and invasive. In our framework, theoretic analyses of these three methods are given at first, and, furthermore, the validity of theoretical derivations and the robustness of these methods are fully verified through numerical results. Our work may be, therefore, helpful for practical experiments.

Low-angle-dependent CdS@SiO2 photonic crystal hydrogel material for visual detection and removal of uranyl ions.

A low-angle-dependent photonic crystal hydrogel (LAD-PCH) material was developed to simultaneously detect and remove uranyl ions (UO22+). Different from traditional SiO2 photonic crystal hydrogel with the problem of angle dependency, the LAD-PCH material overcomes the restriction of observation direction. The LAD-PCH is a composite material with the photonic crystal array of 180-nm monodisperse CdS@SiO2 particles embedded into the functional hydrogel. As one UO22+ can bind to multiple carboxyl groups and amide groups, the functional hydrogel fabricated by acrylic acid and acrylamide will shrink after chelating. These changes in the hydrogel volume alter the array spacing and trigger a blue shift of diffraction wavelength and naked-eye visual color changes of LAD-PCH. The color can vary from orange-red to orange, yellow, green, and cyan, corresponding to the determination range of 100 pM-100 µM. The LAD-PCH material detects UO22+ sensitively as the lowest detectable concentration is about 100 pM, and removes UO22+ high-efficiently as the maximum adsorption capacity of U(VI) is about 1010 mg g-1 at 298 K. This LAD-PCH material is convenient and has potential to simultaneously monitor and remove UO22+ from uranium-polluted water. Schematic representation of the low-angle-dependent photonic crystal hydrogel (LAD-PCH) material for UO22+ detection and removal: The structural colors of LAD-PCH material overcome the restriction of observation angles. After the ligands complex with UO22+, the networks of LAD-PCH show different degrees of shrinkage; these volume changes of hydrogel trigger obvious naked-eye visual color changes of LAD-PCH.

Sensing of cocaine using polarized optical microscopy by exploiting the conformational changes of an aptamer at the water/liquid crystal interface.

Liquid crystals (LCs) have the ability to transduce and amplify a molecular stimulus into optical signals due to their elastic and birefringence properties. An aptamer-based LC sensor for cocaine is described here. 3-Morpholinopropanesulfonic acid with amphipathic structure was used to establish recognition sites at a water/LC interface for the detection of cocaine. The cocaine-binding aptamer is formed at the interface. The conformation of the aptamer undergoes a change on binding cocaine, and this triggers the LCs anchoring transition from homeotropic to planar. Binding can also be detected by polarized optical microscopy. The fluorescence spectroscopy and circular dichroism results are used to prove that the conformation of aptamer changed from a hairpin structure to a special three-way junction structure on binding of cocaine at the interface. The assay works in the 1 nM to 10 µM cocaine concentration range and is specific. Graphical abstract Schematic representation of aptamer-based liquid crystal (LC) biosensor for the detection of cocaine. In this interface biosensing system, after the aptamer binding with cocaine, the conformation of aptamer at the aqueous/LC interface was changed from a hairpin structure to a special three-way junction structure. This triggered the Liquid crystals (LCs) anchoring transition from homeotropic to planar and the sign-on optical signal could be obtained by polarizing optical microscope (POM) in real-time.

Association between vitamin C in serum and trouble sleeping based on NHANES 2017-2018.

Vitamin C is an important micronutrient for human. Association between vitamin C and trouble sleeping was less studied. Therefore, the purpose of this study was to investigate the possible link between vitamin C in serum and trouble sleeping. The cross-sectional data was derived from the National Health and Nutrition Examination Survey (NHANES, 2017-2018). Trouble sleeping was measured by asking participants: "Have you ever told doctor had trouble sleeping". Responses to this question was "yes" or "no". vitamin C in serum was obtained by measuring the serum samples. We used multivariable binary logistic regressions to examine the possible link between vitamin C in serum and trouble sleeping, and then a subgroup analysis was performed. Moreover, the non-linear relationship between vitamin C in serum and trouble sleeping was further detected using a restricted cubic spline (RCS) model. A total of 3227 participants were included in the study. After adjusting all potential confounders, the results of multivariable logistic regression showed the significant negative association between vitamin C in serum and trouble sleeping(OR = 0.816; 95% CI:0.669 ~ 0.995). The significant inverse association was also found in female(OR = 0.713; 95% CI:0.546 ~ 0.931), age ≤ 65 years(OR = 0.773; 95% CI:0.600 ~ 0.996), and in participants with high cholesterol level(OR = 0.738; 95% CI:0.548 ~ 0.994). In addition, the RCS model demonstrated the significant non-linear relationship between vitamin C in serum and trouble sleeping (P value of nonlinear = 0.010). Our study demonstrates the significant negative association between vitamin C in serum and trouble sleeping.

Regulation of Root Exudation in Wheat Plants in Response to Alkali Stress.

Soil alkalization is an important environmental factor limiting crop production. Despite the importance of root secretion in the response of plants to alkali stress, the regulatory mechanism is unclear. In this study, we applied a widely targeted metabolomics approach using a local MS/MS data library constructed with authentic standards to identify and quantify root exudates of wheat under salt and alkali stresses. The regulatory mechanism of root secretion in alkali-stressed wheat plants was analyzed by determining transcriptional and metabolic responses. Our primary focus was alkali stress-induced secreted metabolites (AISMs) that showed a higher secretion rate in alkali-stressed plants than in control and salt-stressed plants. This secretion was mainly induced by high-pH stress. We discovered 55 AISMs containing -COOH groups, including 23 fatty acids, 4 amino acids, 1 amino acid derivative, 7 dipeptides, 5 organic acids, 9 phenolic acids, and 6 others. In the roots, we also discovered 29 metabolites with higher levels under alkali stress than under control and salt stress conditions, including 2 fatty acids, 3 amino acid derivatives, 1 dipeptide, 2 organic acids, and 11 phenolic acids. These alkali stress-induced accumulated carboxylic acids may support continuous root secretion during the response of wheat plants to alkali stress. In the roots, RNAseq analysis indicated that 5 6-phosphofructokinase (glycolysis rate-limiting enzyme) genes, 16 key fatty acid synthesis genes, and 122 phenolic acid synthesis genes have higher expression levels under alkali stress than under control and salt stress conditions. We propose that the secretion of multiple types of metabolites with a -COOH group is an important pH regulation strategy for alkali-stressed wheat plants. Enhanced glycolysis, fatty acid synthesis, and phenolic acid synthesis will provide more energy and substrates for root secretion during the response of wheat to alkali stress.

Role of remnant cholesterol in the relationship between physical activity and diabetes mellitus: an intermediary analysis.

Objective: The purpose of this investigation was to evaluate the potential link between physical activity (PA) and the heightened susceptibility to diabetes mellitus (DM), by examining whether remnant cholesterol (RC) might act as a mediator in this correlation. Methods: The research utilized data from the National Health and Nutrition Examination Survey, spanning from 2005 to 2018. Various statistical analyses were conducted for continuous and categorical variables, including the t-test, ANOVA, and χ2 test. Logistic regression was employed to analyze the association between PA and DM across three distinct models. Mediation analysis was also conducted to assess the potential mediation effects of RC. Results: The study encompassed a total of 9,149 participants, and it was observed that individuals with DM exhibited lower levels of PA. Furthermore, PA levels were found to be associated with all participant characteristics except poverty income ratio, fasting blood glucose, and HOMA-IR (p < 0.05). After adjusting for covariates (Model 3), individuals with high PA levels demonstrated a decreased likelihood of developing DM compared to those in the low PA group (OR: 0.73, 95%CI: 0.54-0.99). A significant dose-response relationship was identified (p < 0.05). No interaction between PA and RC in relation to DM risk was detected, and RC was found to serve as a mediator in the connection between PA and DM. After considering covariates, the mediating effect of RC between PA and DM weakens. Discussion: Our findings suggest that higher levels of PA are linked to a reduced risk of DM in U.S. adults, with RC likely playing a mediating role.