This study aimed to investigate the gradient properties of bamboo at the microscopic level and provide a basis for improving the utilization rate of bamboo. Using moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) as a research subject, the variation of vascular bundle area percentage, chemical content, relative crystallinity (CR), mechanical properties of different bamboo slivers, and correlation between those parameters were analyzed. From the bamboo green layer (BGL) to the bamboo yellow layer (BYL), the distribution of vascular bundles changed from dense to sparse. Cellulose and lignin mass content decreased gently, and hemicellulose mass content showed gradual increases. The CR showed an order of bamboo middle layer (BML) > BGL > BYL. The tensile modulus of elasticity, tensile strength, bending modulus of elasticity, and bending strength decreased from BGL to BYL. The order of influence degree on mechanical properties of moso bamboo was vascular bundle area, hemicellulose content, lignin mass content, density, and CR, and these factors correlated with mechanical properties at a significant level (p < 0.05). Vascular bundle area had a decisive effect on the mechanical properties of bamboo. The vascular bundle area and density were linearly correlated with mechanical properties, while the lignin mass content and CR were curve-linearly correlated with mechanical properties.

UNASSIGNED: Migraine and severe headaches are extremely prevalent neurological disorders that plague humans and society. Prior research has revealed that DII may affect the occurrence of migraines, but there are too few relevant studies and more are required. This study aimed to determine the association between severe headache or migraine and the Dietary Inflammatory Index (DII), with particular attention to age and gender differences.
UNASSIGNED: Using data from the National Health and Nutrition Examination Survey (NHANES), we performed a cross-sectional study. In addition, we investigated the association between DII and severe headache or migraine using weighted multivariate logistic regression models, and restricted cubic splines models were plotted to explore their linear correlation.
UNASSIGNED: There were a total of 13,439 people participating in the study, and of those, 2745 experienced a severe headache or migraine within the previous three months. The DII was linearly and positively correlated with severe headache or migraine (odds ratio [OR] = 1.05, 95% confidence interval [CI] = 1.01-1.08, p = 0.0051). Stratified analysis showed that this relationship persisted among women and those aged < 60 years, with ORs of 1.08 (95% CI = 1.04-1.13, p = 0.0004) and 1.05 (95% CI = 1.01-1.09, p = 0.0071), respectively.
UNASSIGNED: We found that greater levels of DII were significantly related to an increased likelihood of migraine onset, especially among women and young and middle-aged populations. Further research is required to validate and expand upon our results.

Investigating the correlation between the interlaminar space size on plain radiograph and lumbar spinal stenosis (LSS).
100 Patients with LSS and 100 normal participants without LSS were included in this study. Lumbar interlaminar parameters were measured on plain radiographs. Spinal canal parameters were measure on CT and MRI. These image parameters were compared between LSS and control group. The linear correlation among interlaminar parameters, spinal canal parameters and Oswestry Disability Index (ODI) were analyzed. The cut-off values of interlaminar parameters for diagnosing L3/4 and L4/5 symptomatic LSS were analyzed.
Excepting for L1/2 interlaminar width, all interlaminar and spinal canal related parameters in LSS group were significantly smaller than those in control group. Excepting for L1/2 interlaminar width and L5/S1 interlaminar height, all interlaminar parameters had significantly positive linear correlation with spinal canal parameters accordingly in each lumbar level. For diagnosis of symptomatic LSS, The cut off values of L4/5 interlaminar width, height and area were 18.46 mm, 11.37 mm and 134.05 mm2 while 15.78 mm, 13.59 mm and 157.98 mm2 in L3/4. Both spinal canal size and interlaminar size had no linear correlation with ODI in cases of L4/5 LSS.
Lumbar interlaminar space size on plain radiograph has positive linear correlation with developmental and degenerative LSS, excepting for L1/2 interlaminar width and L5/S1 interlaminar height. Lumbar plain radiograph can be a feasible way for predicting and helping to diagnose LSS through evaluating the interlaminar space size.
III.

The idealized urea electrocatalyst is crucial to boost the CN coupling reaction and simultaneously suppress their isolated reduction process after adsorbing N2 and CO2 molecules. Therefore, the dispersed MN3 -M\'N4 moiety is investigated systematically, including 26 homonuclear and 650 heteronuclear di-metal systems. After, 205 stable systems are selected using lowest-energy principle and ab initio molecular dynamics simulations. According to three possible pathways, NCON, CO, and OCOH to produce urea, a five-step high-throughput screening method for excellent catalytic activity and a five-aspect high-throughput screening strategy for outstanding catalytic selectivity are proposed, respectively. The potential determined steps and the limiting potential through three pathways are identified. The data indicates both CO pathway and OCOH pathway are more competitive at lower Gibbs free energy. Significantly, the most favorite RuN3 -CoN4 combination possesses an extremely low limiting potential of -0.80 V for urea production, meanwhile it exists a strong foundation for experimental preparation. This work not only broadens electrocatalytic potentiality of developing di-metals as two active sites, but also provides a feasible high-throughput screening recipe for urea production.

Although many catalysts have been reported for the CO2 electroreduction to C1 or C2 chemicals, the insufficient understanding of fundamental correlations among different products still hinders the development of universal catalyst design strategies. Herein, we first discover that the surface *CO coverage is stable over a wide potential range and reveal a linear correlation between the partial current densities of CH4 and C2 products in this potential range, also supported by the theoretical kinetic analysis. Based on the mechanism that *CHO is the common intermediate in the formation of both CH4 (*CHO → CH4) and C2 (*CHO + *CO → C2), we then unravel that this linear correlation is universal and the slope can be varied by tuning the surface *H or *CO coverage to promote the selectivity of CH4 or C2 products, respectively. As proofs-of-concept, using carbon-coated Cu particles, the surface *H coverage can be increased to enhance CH4 production, presenting a high CO2-to-CH4 Faradaic efficiency ( [Formula: see text] ∼52%) and an outstanding CH4 partial current density of -337 mA cm-2. On the other hand, using an Ag-doped Cu catalyst, the CO2RR selectivity is switched to the C2 pathway, with a substantially promoted [Formula: see text] of 79% and a high partial current density of -421 mA cm-2. Our discovery of tuning intermediate coverages suggests a powerful catalyst design strategy for different CO2 electroreduction pathways.

UNASSIGNED: This study aimed to investigate the correlation between component alignment and short-term clinical outcomes after total knee arthroplasty (TKA).
UNASSIGNED: 50 TKA patients from a regional hospital were enrolled in the study. The following component alignments were measured from radiological data acquired within 1 week after surgery: hip-knee-ankle angle (HKA), medial distal femoral angle (MDFA), medial proximal tibial angle (MPTA), femoral flexion-extension angle (FEA), tibial slope angle (TSA), femoral rotational angle (FRA) and tibial rotational angle (TRA). The Hospital for Special Surgery (HSS) knee scoring system was used to assess clinical outcomes after 1 year, with patients being divided into three groups (excellent, good and not good) according to the HSS scores. Difference analysis and linear correlation analysis were used for the statistical analysis.
UNASSIGNED: The results showed significant differences in MDFA (p = 0.050) and FEA (p = 0.001) among the three patient groups. It was also found that the total HSS had only a moderate correlation with FEA (r = 0.572, p < 0.001), but FEA had a positive linear correlation with pain scores (r = 0.347, p = 0.013), function scores (r = 0.535, p = 0.000), ROM scores (r = 0.368, p = 0.009), muscle scores (r = 0.354, p = 0.012) and stability scores (r = 0.312, p = 0.028). A larger MDFA was associated with lower FE deformity scores (r = -0.289, p = 0.042) and the TSA had a positive influence on the ROM (r = 0.436, p = 0.002). Also, changes in FRA produced a consequent change in the FE deformity score (r = 0.312, p = 0.027), and the muscle strength scores increased as TRA increased (r = 0.402, p = 0.004).
UNASSIGNED: The results show that the FEA plays a significant role in clinical outcomes after TKA. Surgical techniques and tools may need to be improved to accurately adjust the FEA to improve joint functionality and patient satisfaction.

The efficient prediction of biomechanical properties of bone plays an important role in the assessment of bone quality. However, the present techniques are either of low accuracy or of high complexity for the clinical application. The present study aims to investigate the predictive ability of the evolving convolutional neural network (CNN) technique in predicting the effective compressive modulus of porous bone structures. The T11/T12/L1 segments of thirty-five female cadavers were scanned using the HR-pQCT scanner and the images obtained from it were used to generate 10896 2 D bone samples, in which only the cancellous bony parts were processed and investigated. The corresponding 10896 heterogeneous finite-element (FE) models were generated, and then a CNN model was constructed and trained using the predictions of the FE analysis as the ground truths. Then the remaining 260 bone samples generated from the initial HR-pQCT images were used to test the predictive power of the CNN model. The results show that the coefficient of the determinant (R2) from the linear correlation between the CNN and FE predicted elastic modulus is 0.95, which is much higher than that from the correlation between the BMD and the FE predictions (R2 = 0.65). Furthermore, the 95th and 50th percentiles of relative prediction error are below 0.28 and 0.09, respectively. In the conclusion, the CNN model can efficiently predict the effective compressive modulus of human cancellous bone and can be used as a promising and clinically applicable method to evaluate the mechanical quality of porous bone.

Many properties of starch-containing foods are significantly statistically correlated with various structural parameters. The significance of a correlation is judged by the p-value, and this evaluation is based on the assumption of linear relationships between structural parameters and properties. We here examined the linearity assumption to see if it can be used to predict properties at conditions that are not close to those under which they were measured. For this we used both common domesticated rices (DRs) and Australian wild rices (AWRs), the latter having significantly different structural parameters and properties compared to DRs. The results showed that (1) the properties were controlled by more than just the amylopectin or amylose chain-length distributions or amylose content, other structural features also being important, (2) the linear model can predict the enthalpy ΔHg of both AWRs and DRs from the structural parameters to some extent but is often not accurate; it can predict the ΔHg of indica rices with acceptable accuracy from the chain length distribution and the amount of longer amylose chains (degree of polymerization > 500), and (3) the linear model can predict the stickiness of both AWRs and DRs to acceptable accuracy in terms of the amount of longer amylose chains. Thus, the commonly used linearity assumption for structure-property correlations needs to be regarded circumspectly if also used for quantitative prediction.

Amantadine, an antiviral drug, has been widely used in human anti-influenza treatments. However, several highly pathogenic avian influenza viruses show amantadine-resistance mutations in the viral matrix 2 (M2) protein. Here we analyzed global H5N1 sequencing data and calculate possible correlations between frequencies of key mutations in M2 and the mortality rates. We found that the frequency of L26I/V27A mutation in M2 (isolated from both human and avian hosts) is linearly correlated with the mortality rates of human H5N1 infections. The significant correlation between M2 mutations in avians and the mortality rates in humans suggests that the pre-existence of L26I/V27A in birds may determine patient fatalities after transinfections from avian to human hosts. 100% prevalence of L26I/V27A mutation increased the mortality rates from 51% (95% confidence interval [CI] 37%-65%) to 89% (95% CI 88%-90%). Mutations involving Leu26 or Val27 were identified to be the major mutations emerging from drug selection pressure. Thus the emergence of the super H5N1 virus with a fatality of over 90% may be attributed to the abuse of amantadine in poultry, especially in some southeast Asian countries. A more stringent control to antiviral veterinary drugs is imperative.

Urea electrosynthesis under mild conditions starting from the adsorption of inert N2 molecules has brought out a promising alternative experimentally to conquer its huge energy consumption in industrial Haber-Bosch process. The most crucial and inevitable reaction is the formation of urea precursor *NCON from *N2 and CO based on the pre-selected reaction pathway, together with the following protonated processes. It is significant to comprehend their intrinsic intercorrelation and explore the principal descriptor from massive reaction data. Hereby, the authors study the dispersed dual-metals (homonuclear MN3 -MN3 moiety and heteronuclear MN3 -M\'N3 moiety) anchored on N-doped graphene as electrocatalysts to synthesize urea. Based on the screened out 72 stable systems by ab initio molecular dynamics (AIMD) simulations as the database, six significant linear correlations between the computed Gibbs free energy and other important factors are achieved. Most encouragingly, the principal descriptor (ΔE(*NCONH)) is established because 72% low-performance systems can be filtered out and its effective range (-1.0 eV < ΔEE(*NCONH) < 0.5 eV) is identified by eight optimal systems. This study not only suggests that dispersed dual-metals via MN3 moiety can serve as promising active sites for urea production, but also identifies the principal descriptor and its effective range in high-throughput methods.