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ObjectiveTo determine whether there is a causal relationship between thyroid dysfunction and the risk of age-related cataract (ARC) in the European population.DesignA two-sample Mendelian randomization (MR) study.MethodsHypothyroidism, hyperthyroidism, free thyroxine (fT4), and thyrotropin (TSH) were selected as exposures. The single nucleotide polymorphisms (SNP) of hypothyroidism and hyperthyroidism were obtained from the genome-wide association studies (GWAS) of the IEU database, including 337,159 subjects. Data for fT4 and TSH (72,167 subjects) were extracted from the ThyroidOmics Consortium. ARC was used as the outcome. The SNPs associated with ARC were selected from a GWAS of 216,362 individuals in the FinnGen database. The main method used was the inverse variance-weighted method, together with four complementary methods. Sensitivity analyses were performed using Cochran’s Q test, MR-PRESSO, MR-Egger regression and leave-one-out test. MR pleiotropy was used to test for pleiotropy. MR Steiger test was used to test for the directionality.ResultsTwo-sample MR analysis revealed a positive association between genetically predicted hypothyroidism and risk of ARC (OR = 2.501, 95% CI: 1.325-4.720; P = 0.004). Hyperthyroidism, circulating fT4 and TSH levels did not have a significant causal effect on ARC (P > 0.05). The results were robust and reliable, and no horizontal pleiotropy was found after sensitivity analyses. In the MR Steiger test, we found no reverse causal effects of hypothyroidism on the ARC (P

Abstract Both irradiation and dislocations have been proposed as routes to rationally manipulate spatial distribution and micromorphology of precipitate. An interesting effect emerges in Fe–10at.%Cu–3at.%Mn–1.5at.%Ni–1.5at.%Al alloy due to the synergistic-competitive roles of dislocation loop and irradiation. Base on cascade mixing, vacancy-interstitial atoms and dislocation stress field model, we examine nucleation and growth dynamics of Cu-rich precipitates, where both dislocation loop and irradiation act in conjunction. Analytical treatments identify regimes, where the distribution of elements and point defects due to irradiation and dislocations are specific to the Cu-rich precipitates. Simulation results reveal that density, size and distribution of Cu-rich precipitates are a manifestation of the competing effects of the dislocation loop and the irradiation rate. More specifically, the dislocation loop preferentially assists the formation of precipitates and new dislocations at lower irradiation rates. Only the irradiation induces the formation of Cu-rich precipitates with the irradiation rate continues to increase. Equipped with molecular dynamics, where reproduces major interaction features of the solutes with point defects under displacement cascade, can verify multi-component morphologies of Cu-rich precipitates. This modeling framework provides an avenue to explore the role of dislocation loop and irradiation on the microstructural evolution of Cu-rich precipitates.

The Yellow River is the main source of water for urban and rural area and agricultural irrigation in northern China. Herein, the distribution and risk assessment of perfluoroalkyl acids (PFAAs) were investigated from the Yellow River in Shandong Province, China. The total concentration of PFAAs (∑PFAAs) in surface water and sediments were 37.5–2128 ng/L (mean: 167 ng/L) and not detected−6.95 ng/g dry weight (dw) (mean: 1.02 ng/g dw), respectively. Short-chain PFAAs-perfluorobutanoic acid (PFBA), perfluorohexanoic acid (PFHxA), and perfluorobutane sulfonic acid (PFBS) were the most prevalent PFAAs in surface water. Source analysis showed that firefighting foam (proportion: 31.3 %) and textile treatments and food packaging (proportion: 30.3 %) were the main sources of PFAAs in water. Based on the concentration of PFAAs in water, ecological and potential human health risks were assessed. Perfluorooctanoic acid (PFOA), perfluorononanoate (PFNA), perfluorodecanoate (PFDA) and perfluoroundecanoic acid (PFUnDA) posed nonnegligible ecological risk for some aquatic organisms. Levels of PFAAs (e.g., PFOA, PFNA, and PFDA etc.) in some water samples were higher than the advisory guidelines of PFAAs concentrations in water worldwide, indicating a potential human health risk. Therefore, PFOA, PFNA, PFDA, and PFUnDA are the key focus of pollutants in the water of the Yellow River in Shandong Province, and the standards and limits of these PFAAs in environments including surface water and sediment should be promoted.

Epilepsy is a chronic neurological disease. Here we describe the generation of induced pluripotent stem cells (iPSCs) from a patient diagnosed as epilepsy caused by ATP1A2 gene mutation. Induced pluripotent stem cells (iPSCs) were developed using non-integrating episomal vectors containing OCT4, SOX2, KLF4, BCL-XL and C-MYC. The established iPSC line (SDCHi007-A) displayed pluripotent cell morphology, high expression levels of pluripotency markers, differentiation potential in vitro, normal karyotype, and remaining the original ATP1A2 gene mutation.

Abstract Existing hot sintering models based on molecular dynamics focus on single-crystal alloys. This work proposes a new multiparticle model based on molecular dynamics to investigate coalescence kinetics during the hot-pressed sintering of a polycrystalline Al0.3CoCrFeNi high-entropy alloy. The accuracy and effectiveness of the multiparticle model are verified by a phase-field model. Using this model, it is found that when the particle contact zones undergo pressure-induced evolution into exponential power creep zones, the occurrences of phenomena, such as necking, pore formation/filling, dislocation accumulation/decomposition, and particle rotation/rearrangement are accelerated. Based on tensile test results, Young’s modulus of the as-sintered Al0.3CoCrFeNi high-entropy alloy is calculated to be 214.11 ± 1.03 GPa, which deviates only 0.82% from the experimental value, thus further validating the feasibility and accuracy of the multiparticle model.

The properties of high-entropy alloys (HEAs) depend primarily on the composition and content of elements. However, getting the optimal composition of alloying elements through the traditional ''trial and error'' method is challenging, especially for non-equiatomic HEAs with a wide range of composition space. In this study, based on the knowledge that stacking fault energy (SFE) is the most crucial intrinsic property to determine the deformation mechanism and to optimize the mechanical properties of FCC HEAs, classical machine learning classification models including support vector classification (SVC) and random forest (RF), and deep learning regression model (Back Propagation Neural Network) were established to predict the stacking fault energy of Co–Cr–Fe–Mn–Ni–V–Al high-entropy alloys. These models can obtain the SFE data of any atomic ratio composition of the FCC structured Co–Cr–Fe–Mn–Ni–V–Al high-entropy alloy quickly and accurately. The high accuracy of these models indicates that using the compositions as features to predict stacking fault energy is feasible. Meanwhile, the monotonic relationship between alloying elements and SFE makes it possible to change the SFE of high-entropy alloy by fine-tuning the composition to realize the control of material deformation mechanism and mechanical properties. Component-based machine learning models provide a new method for rapidly discovering high-entropy alloys with exceptional strength and flexibility.

The precipitation of Cu-rich phases during the irradiation process will reduce radiation resistance in Fe-Cu-Mn-Ni reactor pressure vessel steels. Therefore, high-entropy alloys with excellent radiation resistance are expected to replace reactor pressure vessel steels and become structural materials for fusion and generation IV fission reactors. This study investigated the phase formation, spinodal decomposition tendency, and radiation resistance of the FeCuMnNi high entropy alloy using a multi-scale method. The results show that FeCuMnNi alloy has a dual-phase face-centered-cubic structure. Here, for the first time, we found that the precipitation phase of the FeCuMnNi high-entropy alloy during the irradiation process is a Mn-rich phase rather than a Cu-rich phase found in conventional Fe-Cu-Mn-Ni reactor pressure vessel steels. Mn separates from the Cu-Mn-rich phase and forms Fe-Mn-Ni clusters with a Fe-Ni phase during irradiation.

The existing high-entropy alloys' phase formation prediction models are established based on empirical thermophysical parameters. The process is complicated, and the accuracy of the descriptors seriously affects the final prediction results. In this article, we achieve the prediction of phase selection in high-entropy alloys by compositions. The high-entropy alloys compositions are mapped to the pseudo-two-dimensional periodic table, automatically extracting features through the convolutional neural network for classification. The results show that this method simplifies the prediction process while improving the prediction accuracy. The prediction accuracy of intermetallic compounds exceeds 89%, solid solutions and amorphous phases exceed 98%. The case study demonstrates the validity of our model. The phase composition of AlxFeCrNi (x = 0, 0.5, 1.0) high-entropy alloys are also accurately predicted and results in agreement with experiments are obtained.

Graphene nanoplatelets (GNPs) reinforced AZ91D (GNPs/AZ91D) composites were prepared by semi-solid electromagnetic stirring casting. The microstructure, mechanical properties and strengthening behavior of the composites were investigated. Two types of bonding interfaces were found in the composites: the GNPs/Mg and the GNPs/MgO interface. The GNPs/Mg interface has a high degree of lattice distortion and few dislocations. In contrast, the GNPs/MgO interface has lower lattice distortion and more dislocations. GNPs with different failure modes were found on the fracture surface. According to the simulation, the strength of GNPs parallel to the stretching direction is 9.8 × 103 MPa, while the strength of GNPs perpendicular to the stretching direction is 0.5 × 103 MPa. This indicates that the stretching direction and fracture mode of GNPs affect the mechanical properties of the composites.

Appropriate image preprocessing could improve machine learning performance, but the robustness of machine learning to preprocessing methods in micrograph datasets with significant sample differences remains unexplored. Here we collected hundreds of optical micrographs varied in color, contrast, size and brightness and tensile strength from published literature. After preprocessing including color transformation, size adjustment, normalization and image enhancement, classification model of whether it was texture or not and tensile strength prediction model were established using transfer learning and VGG16 model. Our results showed comparable accuracy between classification models using grayscale and color micrographs. Based on grayscale images, classification model utilizing center cropping achieved an average accuracy 1.09% higher than model employing scaling, while coefficient of determination (R2) of regression model showed an average increase of 0.0404. The combination of cropping and dividing by 255 was preferable for classification model, yielding an accuracy of 90.79%. Similarly, the combination of cropping and min–max normalization had a better effect on regression model, yielding R2 of 0.2167. Histogram equalization emerged as the optimal technique for improving classification model, yielding 2.63% increase in accuracy compared to models without image enhancement. For regression models, gamma correction exhibited the highest improvement effect, with an R2 of 0.2681.

The competition and balance mechanism between work hardening resulting from the surge in dislocations and material softening caused by plastic deformation during contact loading in Al0.3CoCrFeNi high-entropy alloy is unclear. The structural transformation and strain localization of Al0.3CoCrFeNi high-entropy alloy during nanoindentation are investigated using molecular dynamics, and the hardening-softening mechanism is discussed. The simulations demonstrate that the prismatic dislocation loop with independent nucleation is discovered for the first time in the [111] orientation. Dislocation multiplication and cross-slip lead to an increase in indentation resistance, resulting in work hardening. Free dislocation slip and dislocation annihilation accommodate plastic strain will reduce indentation resistance, resulting in plastic softening. Twin boundaries can effectively block dislocation propagation, which contributes to hardening. However, twin boundaries cause a softening effect in the later stage of plastic deformation owing to two reasons: (1) the formation of steps and the partial slip where the slip plane and Burgers vector are parallel to the twin boundary, (2) steps and local damage zones in twin boundaries become new nucleation sites for dislocations. The phase-field-crystal method confirms that Al0.3CoCrFeNi high-entropy alloy has the highest hardness when the twin spacing is 2.467 nm.

IntroductionSoil enzymes play a critical role in organic matter decomposition and nutrient cycling in forest ecosystems. However, the effects of forest gaps on soil enzyme activities remain uncertain.MethodsThis study aims to investigate the short-term effects of forest gap size on soil enzyme activities in Platycladus orientalis plantations. We conducted a study in a 50-year Platycladus orientalis plantation in Xuzhou, sampling soils from three levels of forest gap size (4 m radius, S; 8 m radius, M and 12 m radius, L) at different positions (within gap, edge, and outside the gap) and control plots (CK, no gaps) 2a after the creation of gaps. Soil peroxidase, dehydrogenase, urease, and invertase activities were measured.ResultsSpecifically, we found that M and S gaps had significantly (p

Abstract Background Soil organic carbon (SOC) is important for soil quality and fertility in forest ecosystems. Labile SOC fractions are sensitive to environmental changes, which reflect the impact of short-term internal and external management measures on the soil carbon pool. Organic mulching (OM) alters the soil environment and promotes plant growth. However, little is known about the responses of SOC fractions in rhizosphere or bulk soil to OM in urban forests and its correlation with carbon composition in plants. Methods A one-year field experiment with four treatments (OM at 0, 5, 10, and 20 cm thicknesses) was conducted in a 15-year-old Ligustrum lucidum plantation. Changes in the SOC fractions in the rhizosphere and bulk soil; the carbon content in the plant fine roots, leaves, and organic mulch; and several soil physicochemical properties were measured. The relationships between SOC fractions and the measured variables were analysed. Results The OM treatments had no significant effect on the SOC fractions, except for the dissolved organic carbon (DOC). OM promoted the movement of SOC to deeper soil because of the increased carbon content in fine roots of subsoil. There were significant correlations between DOC and microbial biomass carbon and SOC and easily oxidised organic carbon. The OM had a greater effect on organic carbon fractions in the bulk soil than in the rhizosphere. The thinnest (5 cm) mulching layers showed the most rapid carbon decomposition over time. The time after OM had the greatest effect on the SOC fractions, followed by soil layer. Conclusions The frequent addition of small amounts of organic mulch increased SOC accumulation in the present study. OM is a potential management model to enhance soil organic matter storage for maintaining urban forest productivity.

Abstract Background Endoplasmic reticulum (ER) stress is closely related with the pathological progression of rheumatoid arthritis (RA), and fibroblast-like synoviocytes (FLSs) are known as its resistance against ER stress-induced apoptosis. Studies on overcoming such resistance would provide a novel treatment strategy for RA in a clinical setting. Methods IL13Rα1 expression was assessed in the synovial tissue by RT-qPCR, immunohistology, and Western blot. Gain or loss of functional analysis was applied to evaluate the biological roles of IL13Rα1 in RA FLSs. Cell viability and apoptosis were assessed by MTS, Western blot, and flow cytometry. The therapeutic effects of IL13Rα1 on the severity of type II collagen-induced arthritis (CIA) in DBA-/1 mouse model were evaluated by scoring synovitis, hyperplasia, cartilage degradation, and bone destruction. Results IL13Rα1 expression was selectively downregulated when RA FLSs were stimulated by ER stress inducers. Functionally, IL13Rα1 overexpression could inhibit the viability, but induce the apoptosis of RA FLSs in the presence of ER stress inducers. Mechanistically, IL13Rα1 promotes cell apoptosis via transcriptionally activating trail expression. Besides, IL13Rα1 could interact and stabilize DR5 protein, thus forming a positive loop involving trail and DR5 to render RA FLSs more susceptible to apoptosis. Additionally, intraarticular injection of IL13Rα1 conferred therapeutic effects in CIA models and showed a limited degree of synovial proliferation and joint destruction. Conclusions Together, our data establishes a regulatory role for IL13Rα1 to combat the apoptotic resistance of RA FLSs against ER stress. The inhibitory effects of IL13Rα1 on arthritis progression suggest the therapeutic potential in RA.

The antique building combines traditional design with contemporary technology, making it an important structural style. Columns, as a crucial structural component, directly affect how well the building functions as a whole. This paper proposes a new connection form with the upper square concrete-filled steel tube-lower circular concrete-filled steel tube (USCFST-LCCFST). This study investigates the seismic performance of the proposed connection form of the columns. First, the finite element software ABAQUS-2021 is used to simulate and analyze the connection forms of the upper square concrete-filled steel tube and lower circular reinforced concrete (USCFST-LCRC) and the upper square steel reinforced concrete and lower circular reinforced concrete (USSRC-LCRC) above the antique building, respectively, which confirms the rationality of the modeling method explored in this paper. Then, geometric modeling of the USCFST-LCCFST connection is performed using ABAQUS. Simulation results demonstrate the superior seismic performance of the proposed connection form. In addition, the influence law of steel tube yield strength and the ratio of upper and lower column linear stiffness on its seismic performance are analyzed and determined through the variational parameter analysis of the USCFST-LCCFST connection form. The steel tube yield strength of USCFST-LCCFST column connection components is recommended to be 355–420 MPa and the ratio of upper and lower column linear stiffness should be no less than 0.063. In order to ensure the good seismic performance of the connection, the steel tube yield strength and the ratio of upper and lower column stiffness should be efficiently controlled in the design of antique buildings’ USCFST-LCCFST column connection components.

Both computed tomography (CT) and notched semicircular bend (NSCB) tests are performed for coals with high and medium bursting proneness to extract the scientific expression of pore-fracture and its influence mechanism on the tensile failure behavior. The acoustic emission (AE) parameters in the sample during loading and failure are monitored, and the influence mechanism of pore-fracture on tensile failure behavior of coal is analyzed. The result illustrates that the spatial distribution feature of the pore-fracture in coals with high and medium bursting proneness is extremely different. The deformation and failure mode of the coals are affected by many factors, loading mode, notch depth and width, mechanical properties of matrix and minal, spatial distribution feature of pore-fracture, etc. The influence of primary pore-fracture in the coal on the extension and penetration of the secondary fracture could be divided into two types: bifurcation and promotion, which would cause different local damage in the sample and affect the final failure mode. The feature of acoustic emission parameters indicates that the deformation and failure process of a sample under loading could be divided into four stages: compaction stage, elastic deformation stage, displacement plastic growth stage, and post peak failure stage, which is the result of comprehensive action of many factors. The evolution process of secondary fracture is accompanied by the dissipation of elastic strain energy and the intensification of internal damage of coal, which reflects the failure process of coal.

Being a positive candidate reinforcement material for laminar composites, the Mg2X (X = Si, Ge, Sn) based intermetallics have attracted much attention. The elastic properties, anisotropy, and electronic properties of intermetallic compounds with Bi-doped Mg2X (X = Si, Ge, Sn) are calculated by the first principles method. Results show that the lattice parameters of Mg2X are smaller than those of Bi-doped Mg2X. The element Bi preferentially occupies the position of the X (X = Si, Ge, Sn) atom than other positions. Mg2X (X = Si, Ge, Sn), Mg63X32Bi, Mg64X31Bi, Mg64Ge32Bi, and Mg64Sn32Bi are mechanically stable, while Mg64Si32Bi indicates that it cannot exist stably. The doping of alloying element Bi reduces the shear deformation resistance of the Mg2X (X = Si, Ge, Sn) alloy. The pure and Bi-doped Mg2X (X = Si, Ge, Sn) exhibits elastic and anisotropic characteristics. The contribution of the Bi orbitals of Mg63X32Bi, Mg64X31Bi, and Mg63X32Bi are different, resulting in different hybridization effects in three types of Bi-doped Mg2X.

Enhanced methods are required for mapping the forest aboveground biomass (AGB) over a large area in Chinese forests. This study attempted to develop an improved approach to retrieving biomass by combining PALSAR (Phased Array type L-band Synthetic Aperture Radar) and WorldView-2 data. A total of 33 variables with potential correlations with forest biomass were extracted from the above data. However, these parameters had poor fits to the observed biomass. Accordingly, the synergies of several variables were explored to identify improved relationships with the AGB. Using principal component analysis and multivariate linear regression (MLR), the accuracies of the biomass estimates obtained using PALSAR and WorldView-2 data were improved to approximately 65% to 71%. In addition, using the additional dataset developed from the fusion of FBD (fine beam dual-polarization) and WorldView-2 data improved the performance to 79% with an RMSE (root mean square error) of 35.13 Mg/ha when using the MLR method. Moreover, a further improvement (R2 = 0.89, relative RMSE = 17.08%) was obtained by combining all the variables mentioned above. For the purpose of comparison with MLR, a neural network approach was also used to estimate the biomass. However, this approach did not produce significant improvements in the AGB estimates. Consequently, the final MLR model was recommended to map the AGB of the study area. Finally, analyses of estimated error in distinguishing forest types and vertical structures suggested that the RMSE decreases gradually from broad-leaved to coniferous to mixed forest. In terms of different vertical structures (VS), VS3 has a high error because the forest lacks undergrowth trees, while VS4 forest, which has approximately the same amounts of stems in each of the three DBH (diameter at breast height) classes (DBH > 20, 10 ≤ DBH ≤ 20, and DBH < 10 cm), has the lowest RMSE. This study demonstrates that the combination of PALSAR and WorldView-2 data is a promising approach to improve biomass estimation.

This study attempted to measure forest resources at the individual tree level using high-resolution images by combining GPS, RS, and Geographic Information System (GIS) technologies. The images were acquired by the WorldView-2 satellite with a resolution of 0.5 m in the panchromatic band and 2.0 m in the multispectral bands. Field data of 90 plots were used to verify the interpreted accuracy. The tops of trees in three groups, namely ≥10 cm, ≥15 cm, and ≥20 cm DBH (diameter at breast height), were extracted by the individual tree crown (ITC) approach using filters with moving windows of 3 × 3 pixels, 5 × 5 pixels and 7 × 7 pixels, respectively. In the study area, there were 1,203,970 trees of DBH over 10 cm, and the interpreted accuracy was 73.68 ± 15.14% averaged over the 90 plots. The numbers of the trees that were ≥15 cm and ≥20 cm DBH were 727,887 and 548,919, with an average accuracy of 68.74 ± 17.21% and 71.92 ± 18.03%, respectively. The pixel-based classification showed that the classified accuracies of the 16 classes obtained using the eight multispectral bands were higher than those obtained using only the four standard bands. The increments ranged from 0.1% for the water class to 17.0% for Metasequoia glyptostroboides, with an average value of 4.8% for the 16 classes. In addition, to overcome the “mixed pixels” problem, a crown-based supervised classification, which can improve the classified accuracy of both dominant species and smaller classes, was used for generating a thematic map of tree species. The improvements of the crown- to pixel-based classification ranged from −1.6% for the open forest class to 34.3% for Metasequoia glyptostroboides, with an average value of 20.3% for the 10 classes. All tree tops were then annotated with the species attributes from the map, and a tree count of different species indicated that the forest of Purple Mountain is mainly dominated by Quercus acutissima, Liquidambar formosana and Pinus massoniana. The findings from this study lead to the recommendation of using the crown-based instead of the pixel-based classification approach in classifying mixed forests.