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Abstract The stability of particle assemblies is strongly affected by particle shape, yet definitive laws describing key properties, such as the mean contact number and apparent friction coefficient, remain elusive. Using X-ray computed tomography and discrete element simulations, we study 70 assemblies of 3D frictional particles. Once properly rescaled, our data collapse onto master curves, revealing linear relationships linking particle shape to these properties for short-axis particles below certain crossover points. These data suggest that the scaling behavior for the mean contact number can be maintained at lower sphericity than the apparent friction coefficient, indicating different sensitivity of the system’s structural versus mechanical properties to particle shape. Through analyzing elongated particles beyond the crossover points, we find that while particle elongation increases the contact number, it has limited effects on improving mechanical stability. This insight, along with the law, paves the route towards optimizing granular packing via manipulating particle shape.

Abstract The recent advance in Neural Radiance Fields (NeRF), which utilizes Multilayer Perceptrons (MLP) for implicit scene representation, enables the synthesis of realistic views from new perspectives. However, degradation in the quantity and quality of input images can lead to failure in scene reconstruction and difficulty in synthesizing high-quality views. To address these limitations, this paper presents a NeRF-based framework (MBS-NeRF), which can reconstruct sharp NeRF from a limited number of motion-blurred input images for high-quality view synthesis. The framework integrates depth information as a constraint to counter the lack of sufficient view information and introduces a Motion Blur Simulation Module (MBSM) to simulate the physical formation process of motion blur. We further introduce camera trajectory optimization during the exposure process to robust the incorrect camera position. MBS-NeRF is thoroughly trained considering photometric consistency and depth supervision. Comprehensive experiments on synthetic and real datasets validate the effectiveness of the model in reconstructing sharp NeRF and achieving high-quality view synthesis from sparse, motion-blurred inputs.

Abstract Nanoparticles have great potential for the application in new energy and aerospace fields. The distribution of nanoparticle sizes is a critical determinant of material properties and serves as a significant parameter in defining the characteristics of zero-dimensional nanomaterials. In this study, we proposed HRU2-Net†, an enhancement of the U2-Net† model, featuring multi-level semantic information fusion. This approach exhibits strong competitiveness and refined segmentation capabilities for nanoparticle segmentation. It achieves a Mean intersection over union (MIoU) of 87.31%, with an accuracy rate exceeding 97.31%, leading to a significant improvement in segmentation effectiveness and precision. The results show that the deep learning-based method significantly enhances the efficacy of nanomaterial research, which holds substantial significance for the advancement of nanomaterial science.

Abstract A pressing need for enhancing lithium-ion battery (LIB) performance exists, particularly in ensuring reliable operation under extreme cold conditions. All-solid-state batteries (ASSBs) offer a promising solution to the challenges posed by conventional LIBs with liquid electrolytes in low-temperature environments. In this study, leveraging the benefits of amorphous solid-state electrolytes (SSEs) xLi3N-TaCl5 (1 ≤ 3x ≤ 2), we develop ASSBs capable of functioning effectively under extreme cold conditions. The designed ASSBs, employing LiCoO2 positive electrode with a mass loading of 4.46 mg cm‒2 and a Li-In negative electrode, demonstrate initial discharge capacities of 183.19, 164.8 and 143.78 mAh g‒1 under 18 mA g‒1 at ‒10, ‒30, and ‒40 °C, respectively, and exhibit a final discharge capacity of 137.6 mAh g‒1 at 18 mA g‒1 and ‒30 °C in the 100th cycle. Moreover, the ASSBs demonstrate an initial discharge capacity of 51.94 mAh g‒1 at 18 mA g‒1 and ‒60 °C with cycling over 200 h.

Abstract Artificial Intelligence (AI) assumes a pivotal role in Earth science, leveraging deep learning’s predictive capabilities. Despite its prevalence, the impact of AI on scientific discovery remains uncertain. In Earth sciences, the emphasis extends beyond mere accuracy, striving for groundbreaking discoveries with distinct physical properties essential for driving advancements through thorough analysis. Here, we introduce a novel knowledge-guided deep symbolic regression model (KG-DSR) incorporating prior knowledge of physical process interactions into the network. Using KG-DSR, we successfully derived the Penman-Monteith (PM) equation and generated a novel surface resistance parameterization. This new parameterization, grounded in fundamental cognitive principles, surpasses the conventional theory currently accepted in surface resistance parameterization. Importantly, the explicit physical processes generated by AI can generalize to future climate scenarios beyond the training data. Our results emphasize the role of AI in unraveling process intricacies and ushering in a new paradigm in tasks related to “AI for Land Surface Modeling.”

Multiple studies have demonstrated a significant association between glucose transporter-1 (GLUT1) and the development and recurrence of adenoid cystic carcinoma (ACC). In this study, we investigated the impact of GLUT1 knockdown on adenoid cystic carcinoma. Our findings revealed that hypoxic conditions promoted the progression and autophagy of SACC83 and SACC-LM cell lines, an effect that was mitigated by GLUT1 knockdown. In vivo experiments showed that the combination of lentivirus-delivered GLUT1 shRNA and autophagy inhibitor chloroquine (CQ) produced the most substantial reduction in tumor volume, weight, Ki67 expression, and autophagy in tumor tissues. In conclusion, hypoxia facilitates ACC progression by upregulating GLUT1 expression. The suppression of GLUT1 expression and autophagy effectively inhibited ACC cell proliferation both in vitro and in vivo.

Abstract Effective extraction of building from remote sensing images requires both global and local information. Despite convolutional neural networks (CNNs) excelling at capturing local details, their intrinsic focus on local operations poses challenge in effectively extracting global features, especially in the context of large‐scale buildings. In contrast, transformers excel at capturing global information, but compared to CNNs, they tend to overly rely on large‐scale datasets and pre‐trained parameters. To tackle the challenge, this paper presents the multi‐scale and dual‐branch feature fusion network (MDTrans). Specifically, the CNN and transformer branches are integrated in a dual‐branch parallel manner during both encoding and decoding stages, local information for small‐scale buildings is extracted by utilizing Dense Connection Blocks in the CNN branch, while crucial global information for large‐scale buildings is effectively captured through Swin Transformer Block in the transformer branch. Additionally, Dual Branch Information Fusion Block is designed to fuse local and global features from the two branches. Furthermore, Multi‐Convolutional Block is designed to further enhance the feature extraction capability of buildings with different sizes. Through extensive experiments on the WHU, Massachusetts, and Inria building datasets, MDTrans achieves intersection over union (IoU) scores of 91.36%, 64.69%, and 79.25%, respectively, outperforming other state‐of‐the‐art models.

Abstract Lithium (Li) oxyhalides have emerged as promising solid electrolyte candidates for all-solid-state batteries (ASSBs) due to their superior ionic conductivity and excellent cathode capability. However, the mechanism of Li transport in oxyhalides has remained unclear due to the complex nature of these compounds, which often comprise multiple phases such as crystalline and amorphous structures. Herein, a first-principles study using density functional theory and ab initio molecular dynamic simulation is performed to gain a theoretical insight into the structural behavior and conduction pathway in oxyhalide electrolytes. Li2.5ZrCl5.5O0.5 electrolyte, as a case of study, is comprehensively investigated by considering various phases. It’s revealed that the $${\rm{P}}\bar{3}{\rm{m}}1$$ P 3 ̅ m 1 phase is energetically more stable and exhibits one order of magnitude higher Li conductivity compared to the C2/m phase among potential crystalline phases (1.63 mS cm−1 versus 0.24 mS cm−1 at room temperature). Interestingly, an amorphous phase with even higher ionic conductivity (~40 mS cm−1) can be generated by creating LiCl-deficient Li-Zr-O-Cl compounds. The remarkably high ionic conductivity suggests that the amorphous structure is likely the dominant conducting pathway, facilitating fast Li transport due the weak bonding between Li and other atoms. Cl anion mobility is also observed in the amorphous phase through mean square displacement (MSD) calculation, although further analyses revealed this to be localized vibration. This study sheds light on the nature of oxychloride structures and promotes a deeper understanding of the superionic conduction mechanism in oxychlorides, which will contribute to the development of advanced solid electrolytes and ASSBs.

Understanding and predicting global soil moisture (SM) is crucial for water resource management and agricultural production. While deep learning methods (DL) have shown strong performance in SM prediction, imbalances in training samples with different characteristics pose a significant challenge. We propose that improving the diversity and balance of batch training samples during gradient descent can help address this issue. To test this hypothesis, we developed a Cluster-Averaged Sampling (CAS) strategy utilizing unsupervised learning techniques. This approach involves training the model with evenly sampled data from different clusters, ensuring both sample diversity and numerical consistency within each cluster. This approach prevents the model from overemphasizing specific sample characteristics, leading to more balanced feature learning. Experiments using the LandBench1.0 dataset with five different seeds for 1-day lead-time global predictions reveal that CAS outperforms several Long Short-Term Memory (LSTM)-based models that do not employ this strategy. The median Coefficient of Determination (R2) improved by 2.36 % to 4.31 %, while Kling-Gupta Efficiency (KGE) improved by 1.95 % to 3.16 %. In high-latitude areas, R2 improvements exceeded 40 % in specific regions. To further validate CAS under realistic conditions, we tested it using the Soil Moisture Active and Passive Level 3 (SMAP-L3) satellite data for 1 to 3-day lead-time global predictions, confirming its efficacy. The study substantiates the CAS strategy and introduces a novel training method for enhancing the generalization of DL models.

With the advancement of consumer-grade RGB-D cameras, obtaining depth information for indoor 3D spaces has become increasingly accessible. This paper systematically reviews 3D reconstruction algorithms for indoor scenes using these cameras, serving as a reference for future research. We cover reconstruction processes and optimization algorithms for both static and dynamic scenes. Additionally, we discuss commonly used datasets, evaluation metrics, and the performance of various reconstruction algorithms. Findings indicate that the balance between reconstruction quality and speed in static scene reconstruction, as well as deformation, occlusion, and fast motion of objects in dynamic scenes are currently major concerns. Deep learning and Neural Radiance Fields (NeRF) are poised to provide new perspectives and methods to address these challenges.

Coastal development such as sea reclamation, port terminals, and breakwater construction has significantly altered the southwestern coastline of Laizhou Bay, changing the regional hydrodynamic environment. To explore how tidal range and tidal prism respond to these 20-year coastline changes, this paper selected the southwestern coastline data of Laizhou Bay in 2000 and 2020, established a 2-D tidal model, and studied the impact of the 20-year coastline changes on tidal range and tidal prism in this sea area. The results show that during the 20 years from 2000 to 2020, most of the bay area’s coastline was in a growth trend of advancing toward the sea, 85.3% of the cross-sections were growth areas, 11.9% were dynamic stable areas, and there was almost no erosion area. Affected by reclamation activities, the area of the southwestern part of Laizhou Bay decreased by 11.66%, the coastline increased by 63.27%, and the center of mass moved 2.22 km to the northeast. The reclamation of tidal flats caused the potential energy of tidal waves in the bay to increase, the maximum possible tidal wave in the southwestern part of the bay showed an increasing trend, and the bay top between Weifang Port and Guangli Port increased particularly significantly, with a maximum increase of 22 cm. The spring tide period, neap tide period and average tidal prism in the southwestern bay decreased by 4.79%, 3.29%, and 4.24%, respectively. The reclamation of tidal flats is the main reason for the decrease in tidal prism in the southwestern part of the bay.

Abstract Solid-state electrolytes with high ionic conductivities are crucial for the development of all-solid-state lithium batteries, and there is a strong correlation between the ionic conductivities and underlying lattice structures of solid-state electrolytes. Here, we report a lattice manipulation method of replacing [Li2OH]+ clusters with potassium ions in antiperovskite solid-state electrolyte (Li2OH)0.99K0.01Cl, which leads to a remarkable increase in ionic conductivity (4.5 × 10‒3 mS cm‒1, 25 °C). Mechanistic analysis indicates that the lattice manipulation method leads to the stabilization of the cubic phase and lattice contraction for the antiperovskite, and causes significant changes in Li-ion transport trajectories and migration barriers. Also, the Li||LiFePO4 all-solid-state battery (excess Li and loading of 1.78 mg cm‒2 for LiFePO4) employing (Li2OH)0.99K0.01Cl electrolyte delivers a specific capacity of 116.4 mAh g‒1 at the 150th cycle with a capacity retention of 96.1% at 80 mA g‒1 and 120 °C, which indicates potential application prospects of antiperovskite electrolyte in all-solid-state lithium batteries.

Abstract Recently, topological insulators (TIs) KHgX (X = As, Sb, Bi) with hourglass-shaped dispersion have attracted great interest. Different from the TIs protected by either time-reversal or mirror crystal symmorphic symmetry tested in previous experiments, these materials were proposed as the first material class whose band topology relies on nonsymmorphic symmetries. As a result, KHgX shows many exotic properties, such as hourglass-shaped electronic channels and three-dimensional doubled quantum spin Hall effects. To date, high-pressure experimental studies on these nonsymmorphic TIs are minimal. Here, we carried out high-pressure electrical measurements up to 55 GPa, together with high-pressure X-ray diffraction measurements and high-pressure structure prediction on KHgAs. We found a pressure-induced semiconductor-metal transition between ~16 and 20 GPa, followed by the appearance of superconductivity with a T c of ~3.5 K at approximately 21 GPa. The superconducting transition temperature was enhanced to a maximum of ~6.6 K at 31.8 GPa and then slowly decreased until 55 GPa. Furthermore, three high-pressure phases within 55 GPa were observed, and their crystal structures were established. Our results showed the high-pressure phase diagram of KHgAs and determined the pressure-induced superconductivity in nonsymmorphic TIs. Thus, our study can be used to facilitate further research on superconductivity and topologically nontrivial features protected by nonsymmorphic symmetries.

Strain engineering has been extensively explored to modulate the various intrinsic properties of flexible inorganic semiconductor films. However, experimental characterization of tensile and compressive strain-induced modulation of optoelectronic properties and their differences has not been easily implemented in flexible inorganic semiconductor films. Herein, the strain-dependent structural, optical, and optoelectronic properties of flexible ZnO films under pre-tensile and pre-compressive strains are systemically investigated by a Mueller matrix ellipsometry-based quantitative characterization method combined with x-ray diffraction and first-principle calculation. With extended prestress-driven deposition processing under bi-direction bending modes, pre-tensile and pre-compressive strains with symmetric magnitudes can be achieved in flexible ZnO films, which allows precise observation of the strain-driven asymmetric modulation of optoelectronic properties. When the applied prestrain varies approximately equally from 0% (baseline) to −0.99% (compression) and 1.07% (tensility), respectively, the relative changes for the c-axis lattice constant are 0.0133 and 0.0104 Å, respectively. Meanwhile, the dependence factors of the bandgap energy on the pre-compression and pre-tensile strains were determined as −0.0099 and −0.0156 eV/%, respectively, and the complex refractive index also presents an asymmetric varying trend. With the help of the strain–stress analysis and the first-principle calculation, the intriguing asymmetric strain-optical modulation effect could be attributed to the biaxial strain mechanism and the difference in the deformation potential between the two prestrain modes. These systematic investigation consequences are thus promising as a basis for the booming applications of the flexible inorganic semiconductor ensemble.

Abstract CuInP2S6 with robust room-temperature ferroelectricity has recently attracted much attention due to the spatial instability of its Cu cations and the van der Waals (vdW) layered structure. Herein, we report a significant enhancement of its remanent polarization by more than 50% from 4.06 to 6.36 µC cm−2 under a small pressure between 0.26 to 1.40 GPa. Comprehensive analysis suggests that even though the hydrostatic pressure suppresses the crystal distortion, it initially forces Cu cations to largely occupy the interlayer sites, causing the spontaneous polarization to increase. Under intermediate pressure, the condensation of Cu cations to the ground state and the polarization increase due cell volume reduction compensate each other, resulting in a constant polarization. Under high pressure, the migration of Cu cations to the center of the S octahedron dominates the polarization decrease. These findings improve our understanding of this fascinating vdW ferroelectric material, and suggest new ways to improve its properties.

Nanoparticles exhibit broad applications in materials mechanics, medicine, energy and other fields. The ordered arrangement of nanoparticles is very important to fully understand their properties and functionalities. However, in materials science, the acquisition of training images requires a large number of professionals and the labor cost is extremely high, so there are usually very few training samples in the field of materials. In this study, a segmentation method of nanoparticle topological structure based on synthetic data (SD) is proposed, which aims to solve the issue of small data in the field of materials. Our findings reveal that the combination of SD generated by rendering software with merely 15% Authentic Data (AD) shows better performance in training deep learning model. The trained U-Net model shows that Miou of 0.8476, accuracy of 0.9970, Kappa of 0.8207, and Dice of 0.9103, respectively. Compared with data enhancement alone, our approach yields a 1% improvement in the Miou metric. These results show that our proposed strategy can achieve better prediction performance without increasing the cost of data acquisition.

Soil temperature (ST) is a crucial parameter in Earth system science. Accurate ST predictions provide invaluable insights; however, the “black box” nature of many deep learning approaches limits their interpretability. In this study, we present the Encoder-Decoder Model with Interpretable Spatio-Temporal Component (ISDNM) to enhance both ST prediction accuracy and its spatio-temporal interpretability. The ISDNM combines a CNN-encoder-decoder and an LSTM-encoder-decoder to improve spatio-temporal feature representation. It further uses linear regression and Uniform Manifold Approximation and Projection (UMAP) techniques for clearer spatio-temporal visualization of ST. The results show that the ISDNM model had the highest R2 ranging from 0.886 to 0.963 and the lowest RMSE ranging from 6.086 m3/m3 to 12.533 m3/m3 for different climate regions, and demonstrated superior performance than all the other DL models like CNN, LSTM, ConvLSTM models. The predictable component highlighted the remarkable similarity between Medium fine and Very fine soils in China. Additional, May and November emerged as crucial months, acting as inflection points in the annual ST cycle, shaping ISDNM model’s prediction capabilities.

Abstract Nitride perovskite LaWN3 has been predicted to be a promising ferroelectric material with unique properties for diverse applications. However, due to the challenging sample preparation at ambient pressure, the crystal structure of this nitride remains unsolved, which results in many ambiguities in its properties. Here, the authors report a comprehensive study of LaWN3 based on high‐quality samples synthesized by a high‐pressure method, leading to a definitive resolution of its crystal structure involving nitrogen deficiency. Combined with theoretical calculations, these results show that LaWN3 adopts an orthorhombic Pna21 structure with a polar symmetry, possessing a unique atomic polarization along the c‐axis. The associated atomic polar distortions in LaWN3 are driven by covalent hybridization of W: 5d and N: 2p orbitals, opening a direct bandgap that explains its semiconducting behaviors. The structural stability and electronic properties of this nitride are also revealed to be closely associated with its nitrogen deficiency. The success in unraveling the structural and electronic ambiguities of LaWN3 would provide important insights into the structures and properties of the family of nitride perovskites.

Solid-state electrolytes (SSEs) hold the key position in the progress of cutting-edge all-solid-state batteries (ASSBs). The ionic conductivity of solid-state electrolytes is linked to the presence of both amorphous and crystalline phases. This study employs the synthesis method of mechanochemical milling on binary xLi2S-(100-x)LiI system to investigate the effect of amorphization on its ionic conductivity. Powder X-ray diffraction (PXRD) shows that the stoichiometry of Li2S and LiI has a significant impact on the amorphization of xLi2S-(100-x)LiI system. Furthermore, the analysis of electrochemical impedance spectroscopy (EIS) indicates that the amorphization of xLi2S-(100-x)LiI system is strongly correlated with its ionic conductivity, which is primarily attributed to the effect of grain boundary resistance. These findings uncover the latent connections between amorphization, grain boundary resistance, and ionic conductivity, offering insight into the design of innovative amorphous SSEs.

Coastal reclamation has negative impacts on the marine eco-environment and leads to the loss of marine ecosystem services, and it is very important to evaluate the value of marine ecosystem services for the intensive and economical utilization of marine resources and the sustainable development. Based on the characteristics of ecosystem services of Zhifu Bay (ZFB) of Yantai in Shandong Province, a functional value method was adopted, and four ecosystem service indicators were selected, including provisioning service, regulating service, supporting service and cultural service to establish an evaluation model. The evaluation model of the coastal ecosystem services was optimized, and the value loss of marine ecosystem services caused by coastal reclamation in the ZFB was evaluated. The results showed that the total loss of marine ecosystem service value caused by the coastal reclamation of the ZFB for 40 years was 2.95×107 yuan/a, with unit area 3.72×104 yuan/ (hm2·a), of which the annual loss of provisioning service was the largest, accounting for 56.95% of the total loss, followed by the cultural service, the supporting service and the regulating service, accounting for 27.28%, 8.10% and 7.67%, respectively. The rich fishery resources and developed tourism in the ZFB were the main factors that caused the high value loss of provisioning service and cultural service. The evaluation of marine ecosystem services in the present study would provide the scientific basis for ecological compensation of reclamation in the ZFB, and contribute to improving the utilization efficiency of marine space resource. It would also provide scientific support for the rational development and utilization of marine natural resources.

To enhance the reliability of traditional current protection systems in the evolving landscape of future power grids, this paper proposes a differential protection data synchronization approach that leverages satellite and 5G network timing. This method addresses the risk of inadvertent operation due to 5G communication jitter. It employs the BeiDou satellite system for synchronizing the timing of 5G base stations and terminals. This synchronization extends to the 5G terminal and the protection device, thereby mitigating time deviations caused by air interface delays and reducing the 5G network’s end-to-end delay by approximately 4 ms on average. A simulation was conducted to assess the reliability and effectiveness of the proposed method under normal satellite signal conditions. The results indicated a significant improvement in protection accuracy, with the method reducing the protection activation time by approximately 5–7 ms compared to the 5G timing data synchronization method. This advancement offers technical support for the safe and stable operation of new power systems.

Multi-level programmable photonic integrated circuits (PICs) and optical metasurfaces have gained widespread attention in many fields, such as neuromorphic photonics, optical communications, and quantum information. In this paper, we propose pixelated programmable Si _3 N _4 PICs with record-high 20-level intermediate states at 785 nm wavelength. Such flexibility in phase or amplitude modulation is achieved by a programmable Sb _2 S _3 matrix, the footprint of whose elements can be as small as 1.2 μ m, limited only by the optical diffraction limit of an in-house developed pulsed laser writing system. We believe our work lays the foundation for laser-writing ultra-high-level (20 levels and even more) programmable photonic systems and metasurfaces based on phase change materials, which could catalyze diverse applications such as programmable neuromorphic photonics, biosensing, optical computing, photonic quantum computing, and reconfigurable metasurfaces.

In integrated circuit (IC) manufacturing, fast, nondestructive, and precise detection of defects in patterned wafers, realized by bright-field microscopy, is one of the critical factors for ensuring the final performance and yields of chips. With the critical dimensions of IC nanostructures continuing to shrink, directly imaging or classifying deep-subwavelength defects by bright-field microscopy is challenging due to the well-known diffraction barrier, the weak scattering effect, and the faint correlation between the scattering cross-section and the defect morphology. Herein, we propose an optical far-field inspection method based on the form-birefringence scattering imaging of the defective nanostructure, which can identify and classify various defects without requiring optical super-resolution. The technique is built upon the principle of breaking the optical form birefringence of the original periodic nanostructures by the defect perturbation under the anisotropic illumination modes, such as the orthogonally polarized plane waves, then combined with the high-order difference of far-field images. We validated the feasibility and effectiveness of the proposed method in detecting deep subwavelength defects through rigid vector imaging modeling and optical detection experiments of various defective nanostructures based on polarization microscopy. On this basis, an intelligent classification algorithm for typical patterned defects based on a dual-channel AlexNet neural network has been proposed, stabilizing the classification accuracy of λ /16-sized defects with highly similar features at more than 90%. The strong classification capability of the two-channel network on typical patterned defects can be attributed to the high-order difference image and its transverse gradient being used as the network’s input, which highlights the polarization modulation difference between different patterned defects more significantly than conventional bright-field microscopy results. This work will provide a new but easy-to-operate method for detecting and classifying deep-subwavelength defects in patterned wafers or photomasks, which thus endows current online inspection equipment with more missions in advanced IC manufacturing.

Background: Hypertension is one of the main causes of cardiovascular death. Inflammation was considered influential factors of cardiovascular (CVD) death in patients with hypertension. Advanced lung cancer inflammation index (ALI) is an index to assess inflammation, few studies have investigated the relationship between advanced lung cancer inflammation index and cardiovascular death in hypertensive patients.Objective: The aim of this study was to investigate the association between advanced lung cancer inflammation index and long-term cardiovascular death in hypertensive patients.Method: Data from the National Health and Nutrition Examination Survey (NHANES) 1999–2018 with mortality follow-up through 31 December 2019 were analyzed. Advanced lung cancer inflammation index was calculated as BMI (kg/㎡) × serum albumin level (g/dL)/neutrophil to lymphocyte ratio (NLR). A total of 20,517 participants were evaluated. Patients were divided into three groups based on tertiles of advanced lung cancer inflammation index as follows: T1 (n = 6,839), T2 (n = 6,839), and T3 (n = 6,839) groups. The relationship between advanced lung cancer inflammation index and long-term cardiovascular death was assessed by survival curves and Cox regression analysis based on the NHANES recommended weights.Results: The median advanced lung cancer inflammation index value in this study was 61.9 [44.4, 84.6]. After full adjustment, the T2 group (hazard ratio [HR]: 0.59, 95% confidence interval [CI]: 0.50–0.69; p < 0.001) and T3 group (HR: 0.48, 95% CI: 0.39–0.58; p < 0.001) were found to have a significantly lower risk of cardiovascular death compared to the T1 group.Conclusion: High levels of advanced lung cancer inflammation index were associated with reduced risk of cardiovascular death in hypertensive patients.

ObjectiveInflammation plays an important role in the pathophysiology of hypertension (HTN). Aggregate index of systemic inflammation (AISI), as a new inflammatory and prognostic marker has emerged recently. Our goal was to determine whether there was a relationship between HTN and AISI.MethodsWe analyzed patients with HTN from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. The primary end point was cardiovascular mortality. A total of 23,765 participants were divided into four groups according to the AISI quartile level. The association between AISI and cardiovascular mortality in patients with HTN was assessed by survival curves and Cox regression analyses based on NHANES recommended weights.ResultsHigh levels of AISI were significantly associated with cardiovascular mortality in patients with HTN. After full adjustment for confounders, there was no significant difference in the risk of cardiovascular mortality in Q2 and Q3 compared to Q1, while Q4 (HR: 1.91, 95% CI: 1.42–2.58; P

The distribution of elite soybean (Glycine max) cultivars is limited due to their highly sensitive to photoperiod, which affects the flowering time and plant architecture. The recent emergence of CRISPR/Cas9 technology has uncovered new opportunities for genetic manipulation of soybean. The major maturity gene E1 of soybean plays a critical role in soybean photoperiod response. Here, we performed CRISPR/Cas9-mediated targeted mutation of E1 gene in soybean cultivar Tianlong1 carrying the dominant E1 to investigate its precise function in photoperiod regulation, especially in plant architecture regulation. Four types of mutations in the E1 coding region were generated. No off-target effects were observed, and homozygous trans-clean mutants without T-DNA were obtained. The photoperiod sensitivity of e1 mutants decreased relative to the wild type plants; however, e1 mutants still responded to photoperiod. Further analysis revealed that the homologs of E1, E1-La, and E1-Lb, were up-regulated in the e1 mutants, indicating a genetic compensation response of E1 and its homologs. The e1 mutants exhibited significant changes in the architecture, including initiation of terminal flowering, formation of determinate stems, and decreased branch numbers. To identify E1-regulated genes related to plant architecture, transcriptome deep sequencing (RNA-seq) was used to compare the gene expression profiles in the stem tip of the wild-type soybean cultivar and the e1 mutants. The expression of shoot identity gene Dt1 was significantly decreased, while Dt2 was significantly upregulated. Also, a set of MADS-box genes was up-regulated in the stem tip of e1 mutants which might contribute to the determinate stem growth habit.

Composite polymer electrolytes (CPEs) show significant advantages in developing solid-state batteries due to their high flexibility and easy processability. In CPEs, solid fillers play a considerable effect on electrochemical performances. Recently, metal-organic frameworks (MOFs) are emerging as new solid fillers and show great promise to regulate ion migration. Herein, by using a Co-based MOF, a high-performance CPE is initially prepared and studied. Benefiting from the sufficient interactions and pore confinement from MOF, the obtained CPE shows both high ionic conductivity and a high Li+ transference number (0.41). The MOF-incorporated CPE then enables a uniform Li deposition and stable interfacial condition. Accordingly, the as-assembled solid batteries demonstrate a high reversible capacity and good cycling performance. This work verifies the practicability of MOFs as solid fillers to produce advanced CPEs, presenting their promising prospect for practical application.

Abstract At present, there are many studies on the residual stress field and plastic strain field introduced by surface strengthening, which can well hinder the initiation of early fatigue cracks and delay the propagation of fatigue cracks. However, there are few studies on the effects of these key factors on fretting wear. In the paper, shot-peening (SP) and ultrasonic surface rolling process (USRP) were performed on Ti-6Al-4V plate specimens. The surface hardness and residual stresses of the material were tested by vickers indenter and X-ray diffraction residual stress analyzer. Microhardness were measured by HXD-1000MC/CD micro Vickers hardness tester. The effects of different surface strengthening on its fretting fatigue properties were verified by fretting fatigue experiments. The fretting fatigue fracture surface and wear morphology of the specimens were studied and analyzed by means of microscopic observation, and the mechanism of improving fretting fatigue life by surface strengthening process was further explained. After USRP treatment, the surface roughness of Ti-6Al-4V is significantly improved. In addition, the microhardness of the specimen after SP reaches the maximum at 80 μm from the surface, which is about 123% higher than that of the AsR specimen. After USRP, it reaches the maximum at 150 μm from the surface, which is about 128% higher than that of AsR specimen. It is also found that the residual compressive stress of the specimens treated by USRP and SP increases first and then decreases with the depth direction, and the residual stress reaches the maximum on the sub surface. The USRP specimen reaches the maximum value at 0.18 mm, about − 550 MPa, while the SP specimen reaches the maximum value at 0.1 mm, about − 380 MPa. The fretting fatigue life of Ti-6Al-4V effectively improved after USRP and SP. The surface integrity of specimens after USRP is the best, which has deeper residual compressive stress layer and more refined grain. In this paper, a fretting wear device is designed to carry out fretting fatigue experiments on specimens with different surface strengthening.

In order to meet the requirements of dynamic monitoring from a bird’s eye view for typical rapidly changing processes such as mechanical rotation and photoresist exposure reaction, we propose a vertical high-speed Mueller matrix ellipsometer that consists of a polarization state generator (PSG) based on the time-domain polarization modulation and a polarization state analyzer (PSA) based on division-of-amplitude polarization demodulation. The PSG is realized using two cascaded photoelastic modulators, while the PSA is realized using a six-channel Stokes polarimeter. On this basis, the polarization effect introduced by switching the optical-path layout of the instrument from the horizontal transmission to the vertical transmission is fully considered, which is caused by changing the incidence plane. An in situ calibration method based on the correct definition of the polarization modulation and demodulation reference plane has been proposed, enabling the precise calibration of the instrument by combining it with a time-domain light intensity fitting algorithm. The measurement experiments of SiO2 films and an air medium prove the accuracy and feasibility of the proposed calibration method. After the precise calibration, the instrument can exhibit excellent measurement performance in the range of incident angles from 45° to 90°, in which the measurement time resolution is maintained at the order of 10 μs, the measurement accuracy of Mueller matrix elements is better than 0.007, and the measurement precision is better than 0.005.

There are few reports of ferroelectricity due to symmetry breaking transition in A-site-ordered quadruple perovskites. Here, the authors find one with phase transition from a high-temperature centrosymmetric paraelectric phase to a low-temperature non-centrosymmetric ferroelectric phase in a high pressure synthesized compound.

The authors introduce the concept of electromagnetic canyons and non-resonance amplification for optical detection of nanoscale objects. They demonstrate that a pair of nanowire sensors enable detection of 25-nm radii objects with a standard widefield microscope.

Abstract HgTe film is widely used for quantum Hall well studies and devices, as it has unique properties, like band gap inversion, carrier‐type switch, and topological evolution depending on the film thickness modulation near the so‐called critical thickness (63.5 Å), while its counterpart bulk materials do not hold these nontrivial properties at ambient pressure. Here, much richer transport properties emerging in bulk HgTe crystal through pressure‐tuning are reported. Not only the above‐mentioned abnormal properties can be realized in a 400 nm thick bulk HgTe single crystal, but superconductivity is also discovered in a series of high‐pressure phases. Combining crystal structure, electrical transport, and Hall coefficient measurements, a p‐n carrier type switching is observed in the first high‐pressure cinnabar phase. Superconductivity emerges after the semiconductor‐to‐metal transition at 3.9 GPa and persists up to 54 GPa, crossing four high‐pressure phases with an increased upper critical field. Density functional theory calculations confirm that a surface‐dominated topologic band structure contributes these exotic properties under high pressure. This discovery presents broad and efficient tuning effects by pressure on the lattice structure and electronic modulations compared to the thickness‐dependent critical properties in 2D and 3D topologic insulators and semimetals.

Close to half of the world population have smartphones, while a flagship smartphone today has been integrated with more than 20 smart components and sensors, making a smartphone a highly integrated platform that can potentially mimic the five senses of humans. Recent advancement in achieving high compactness, high performance computing, high flexibility, and multiplexed functionality in smartphones have enabled them for many cutting‐edge healthcare applications, such as single‐molecule imaging, medical diagnosis, and biosensing, which were conventionally done with bulky devices. Herein, recent advances in sensors in modern smartphones are reviewed and how they can potentially offer widespread and easy‐to‐implement solutions to emerging healthcare applications, including biosensing, point‐of‐care testing, pollution monitoring, etc., are discussed.

The mammalian brain, with its complexity and intricacy, poses significant challenges for researchers aiming to understand its inner workings. Optical multilayer interference tomography (OMLIT) is a novel, promising imaging technique that enables the mapping and reconstruction of mesoscale all-cell brain atlases and is seamlessly compatible with tape-based serial scanning electron microscopy (SEM) for microscale mapping in the same tissue. However, currently, OMLIT suffers from imperfect coatings, leading to background noise and image contamination. In this study, we introduced a new imaging configuration using carbon spraying to eliminate the tape-coating step, resulting in reduced noise and enhanced imaging quality. We demonstrated the improved imaging quality and validated its applicability through a correlative light–electron imaging workflow. Our method successfully reconstructed all cells and vasculature within a large OMLIT dataset, enabling basic morphological classification and analysis. We also show that this approach can perform effectively on thicker sections, extending its applicability to sub-micron scale slices, saving sample preparation and imaging time, and increasing imaging throughput. Consequently, this method emerges as a promising candidate for high-speed, high-throughput brain tissue reconstruction and analysis. Our findings open new avenues for exploring the structure and function of the brain using OMLIT images.

Although the era of big data has brought convenience to daily life, it has also caused many problems. In the field of scenic tourism, it is increasingly difficult for people to choose the scenic spot that meets their needs from mass information. To provide high-quality services to users, a recommended tourism model is introduced in this paper. On the one hand, the tourism system utilises the users' historical interactions with different scenic spots to infer their short- and long-term favorites. Among them, the users' short-term demands are modelled through self-attention mechanism, and the proportion of short- and long-term favorites is calculated using the Euclidean distance. On the other hand, the system models the relationship between multiple scenic spots to strengthen the item relationship and further form the most relevant tourist recommendations.

Abstract The unique electronic structure and crystal structure driven by external pressure in transition metal tellurides (TMTs) can host unconventional quantum states. Here, the discovery of pressure‐induced phase transition at ≈2 GPa, and dome‐shaped superconducting phase emerged in van der Waals layered NbIrTe4 is reported. The highest critical temperature (Tc) is ≈5.8 K at pressure of ≈16 GPa, where the interlayered Te–Te covalent bonds form simultaneously derived from the synchrotron diffraction data, indicating the hosting structure of superconducting evolved from low‐pressure two‐dimensional (2D) phase to three‐dimensional (3D) structure with pressure higher than 30 GPa. Strikingly, the authors have found an anisotropic transport in the vicinity of the superconducting state, suggesting the emergence of a “stripe”‐like phase. The dome‐shaped superconducting phase and anisotropic transport are possibly due to the spatial modulation of interlayer Josephson coupling .

Solid-state lithium metal batteries have attracted broad interest as a promising energy storage technology because of the high energy density and enhanced safety that are highly desired in the markets of consumer electronics and electric vehicles. However, there are still many challenges before the practical application of the new battery. One of the major challenges is the poor interface between lithium metal electrodes and solid electrolytes, which eventually lead to the exceptionally high internal resistance of the cells and limited output. The interface issue arises largely due to the poor contact between solid and solid, and the mechanical/electrochemical instability of the interface. In this work, an in situ “welding” strategy is developed to address the interfacial issue in solid-state batteries. Microliter-level of liquid electrolyte is transformed into an organic–inorganic composite buffer layer, offering a flexible and stable interface and promoting enhanced electrochemical performance. Symmetric lithium–metal batteries with the new interface demonstrate good cycling performance for 400 h and withstand the current density of 0.4 mA cm−2. Full batteries developed with lithium–metal anode and LiFePO4 cathode also demonstrate significantly improved cycling endurance and capacity retention.

Flowering time, plant height, branch number, node numbers of main stem and pods per plant are important agronomic traits related to photoperiodic sensitivity, plant type and yield of soybean, which are controlled by multiple genes or quantitative trait loci (QTL). The main purpose of this study is to identify new QTL for five major agronomic traits, especially for flowering time. Three biparental populations were developed by crossing cultivars from northern and central China. Specific loci amplified fragment sequencing (SLAF-seq) was used to construct linkage map and QTL mapping was carried out. A total of 10 QTL for flowering time were identified in three populations, some of which were related to E1 and E2 genes or the other reported QTL listed in Soybase. In the Y159 population (Xudou No.9 × Kenfeng No.16), QTL for flowering time on chromosome 4, qFT4_1 and qFT4_2 were new. Compared with the QTL reported in Soybase, 1 QTL for plant height (PH), 3 QTL for branch number (BR), 5 QTL for node numbers of main stem, and 3 QTL for pods per plant were new QTL. Major E genes were frequently detected in different populations indicating that major the E loci had a great effect on flowering time and adaptation of soybean. Therefore, in order to further clone minor genes or QTL, it may be of great significance to carefully select the genotypes of known loci. These results may lay a foundation for fine mapping and clone of QTL/genes related to plant-type, provided a basis for high yield breeding of soybean.

The soil displacement load induced by the metro tunneling is applied to the adjacent pipelines, which leads to the deformation of the pipelines, and affects the safe use of the pipeline. According to the displacement of the soil caused by the excavation of the twin-tunnel, considering the shear deformation of the soil and the angle between the tunnel and the pipeline, the analysis method of the Pasternak foundation beam for the longitudinal displacement of the pipeline caused by tunneling was established. The calculation result was relatively close to that of the numerical simulation based on displacement controlled method (DCM) and the field measured data. The influences of the angle, the distance between tunnel axes, the soil volume loss rate and the relative bending stiffness on the pipeline deformation were analyzed. Finally, the influence of the shearing stiffness of the pipeline was researched based on the Timoshenko beam model.

A multi-line structured light measurement method that combines a single-line and a three-line laser, in which precision sliding rails and displacement measurement equipment are not required, is proposed in this paper. During the measurement, the single-line structured light projects onto the surface of an object and the three-line structured light remains fixed. The single-line laser is moved and intersects with the three-line laser to form three intersection points. The single-line light plane can be solved using the camera coordinates of three intersection points, thus completing the real-time calibration of the scanned light plane. The single-line laser can be scanned at any angle to determine the overall complete three-dimensional (3D) shape of the object during the process. Experimental results show that this method overcomes the difficulty of obtaining information about certain angles and locations and can effectively recover the 3D shape of the object. The measurement system’s repetition error is under 0.16 mm, which is sufficient to measure the 3D shapes of complicated workpieces.

Crop yield has been maintaining its attraction for researchers because of the demand of global population growth. Mutation of flowering activators, such as florigen, increases plant biomass at the expense of later flowering, which prevents crop maturity in the field. As a result, it is difficult to apply flowering activators in agriculture production. Here, we developed a strategy to utilize florigen to significantly improve soybean yield in the field. Through the screening of transgenic lines of RNAi-silenced florigen homologs in soybean (Glycine-max-Flowering Locus T Like, GmFTL), we identified a line, GmFTL-RNAi#1, with minor changes in both GmFTL expression and flowering time but with notable increase in soybean yield. As expected, GmFTL-RNAi#1 matured normally in the field and exhibited markedly high yield over multiple locations and years, indicating that it is possible to reach a trade-off between flowering time and high yield through the fine-tuning expression of flowering activators. Further studies uncovered an unknown mechanism by which GmFTL negatively regulates photosynthesis, a substantial source of crop yield, demonstrating a novel function of florigen. Thus, because of the highly conserved functions of florigen in plants and the classical RNAi approach, the findings provide a promising strategy to harness early flowering genes to improve crop yield.

The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R2 >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contrast, support vector regression and multi-layer perceptron show that R2 is >70% with RMSE of ~0.41 eV and 0.43 eV, respectively. Finally, when the bandgap calculated without spin-orbit coupling (SOC) is used as a feature, the RMSEs of the four ML models decrease greatly to 0.09 eV, 0.10 eV, 0.17 eV, and 0.12 eV, respectively. The R2 of all the models is >94%. These results show that the properties of 2D materials can be rapidly obtained by ML prediction with high precision.

Abstract Environmental scanning electron microscopy (ESEM) is a powerful technique that enables imaging of diverse specimens (e.g., biomaterials, chemical materials, nanomaterials) in a hydrated or native state while simultaneously maintaining micro‐to‐nanoscale resolution. However, it is difficult to achieve high signal‐to‐noise and artifact‐free secondary electron images in a high‐pressure gaseous environment due to the intensive electron‐gas collisions. In addition, nanotextured substrates can mask the signal from a weakly scattering sample. These drawbacks limit the study of material dynamics under extreme conditions and correspondingly our understanding in many fields. In this work, an imaging framework called Quasi‐Newtonian ESEM is proposed, which introduces the concepts of quasi‐force and quasi‐work by referencing the scattering force in light–matter interactions, to break these barriers without any hardware changes. It is shown that quasi‐force is a more fundamental quantity that has a more significant connection with the sample morphology than intensity in the strongly scattering regime. Experimental and theoretical studies on the dynamics of droplet condensation in a high‐pressure environment (up to 2500 Pa) successfully demonstrate the effectiveness and robustness of the framework and that the overwhelmed signal of interest in ESEM images can be reconstructed through information stored in the time domain, i.e., frames captured at different moments.

To reveal genetic factors or pathways involved in the pod degreening, we performed transcriptome and metabolome analyses using a yellow pod cultivar of the common bean “golden hook” ecotype and its green pod mutants yielded via gamma radiation. Transcriptional profiling showed that expression levels of red chlorophyll catabolite reductase (RCCR, Phvul.008G280300) involved in chlorophyll degradation was strongly enhanced at an early stage (2 cm long) in wild type but not in green pod mutants. The expression levels of genes involved in cellulose synthesis was inhibited by the pod degreening. Metabolomic profiling showed that the content of most flavonoid, flavones, and isoflavonoid was decreased during pod development, but the content of afzelechin, taxifolin, dihydrokaempferol, and cyanidin 3-O-rutinoside was remarkably increased in both wild type and green pod mutant. This study revealed that the pod degreening of the golden hook resulting from chlorophyll degradation could trigger changes in cellulose and flavonoids biosynthesis pathway, offering this cultivar a special color appearance and good flavor.

Neutron diffraction studies have been carried out on carbon monoxide (CO) in the temperature range of 14–68 K. From the data of the temperature dependent crystal parameters, the volume thermal expansion β (T), C–O bond length, and intermolecular distance of CO have been determined and compared with calculations. The volume evolution of cubic CO solids indicates a normal thermal expansion. However, the bond length of CO molecules contracts with the increase in temperature. Correspondingly, the distance between CO molecules increases much faster with an increase in the temperature. We find that intermolecular and intramolecular interactions account for these abnormal temperature behaviors of CO molecular crystals. The abnormal change observed in the curve β (T), evolution of C–O bond length, and distance between two CO molecules indicate an order–disorder phase transition induced by head-to-tail reorientations of CO dipoles.

Abstract Background Lung adenocarcinoma is the most common type of lung cancer and one of the most lethal and prevalent cancers. Aberrant glycosylation was common and essential in tumorigenesis, with fucosylation as one of the most common types disrupted in cancers. However, it is still unknown whether genes involved in fucosylation are important for lung adenocarcinoma development and process. Methods GMDS is involved in cellular fucosylation. Here we examined GMDS expression level at both mRNA and protein level in lung adenocarcinoma. The impact of GMDS knockdown on lung adenocarcinoma in vitro and in vivo was investigated. Transcriptome changes with GMDS knockdown in lung adenocarcinoma cells were also examined to provide insights into related molecular mechanisms. Results GMDS expression is significantly upregulated in lung adenocarcinoma at both mRNA and protein levels. Lentivirus-mediated shRNA strategy inhibited GMDS expression efficiently in human lung adenocarcinoma cells A549 and H1299, and GMDS knockdown impaired cell proliferation, colony formation ability, induced cell cycle arrest, and apoptosis in both cell lines. Furthermore, GMDS knockdown inhibited tumorigenesis in a xenograft mice model of lung adenocarcinoma. Microarray analysis explored the GMDS-mediated molecular network and revealed that the CASP8-CDKN1A axis might be critical for lung adenocarcinoma development. Conclusions These findings suggest that GMDS upregulation is critical for cell proliferation and survival in human lung adenocarcinoma and might serve as a potential biomarker for lung adenocarcinoma diagnosis and treatment.

Nitride materials including conventional manmade superhard light-element nitrides, such as cubic boron nitride (cBN), cubic silicon nitride (γ-Si3N4), and carbonitrides, have been extensively used for machining (e.g., turning, cutting, grinding, boring, drilling) and coating of ferr ous alloys due to their remarkable performances of high rigidity, high melting-point, and prominent chemical and thermal stabilities. However, to some degree, superhard nitrides merely compensate for the adverse limitations of diamond: reaction (with iron), oxidation, and graphitization at moderate temperatures; they are still unable to dominate the market owing to their relatively low hardness when compared to diamond. Therefore, recent efforts toward the preparation of nitride materials with outstanding mechanical performance and chemical inertness have focused on synthesizing ternary light-element nitride compounds and harvesting the effect of work hardening through microstructure manipulations. These new light-element nitrides are potential candidates to displace diamond in the cutting business. On the other hand, incorporation of transition-metal atoms into the dinitrogen triple-bond can form novel hard transition-metal nitride alloys (TMNAs), such as Mo-N, W-N, Pt-N, Ir-N, Os-N, etc., which are potential candidates for the cutting, coating, and polishing of iron-group metals. However, synthesis of high-crystallinity and stoichiometric TMNAs via traditional routes is challenging, since the embedded nitrogen in the transition-metal lattice is thermodynamically unfavorable at ambient condition. A novel approach involving ion-exchange reactions under moderate pressure and temperature has been developed in recent years for preparation of well-crystallized stoichiometric TMNAs, which have quickly been realized as emergent materials in electronics, catalysts, and superconductors as well.

Pressure can either make materials more disordered, like amorphization, or more thermodynamic stable, yet the switching between the two metastable phases has not been described. Wang et al. study it in vanadium oxide nanosheets and highlight the role played by spin and charge during the transition.

The FLOWERING LOCUS T (FT) gene is a highly conserved florigen gene among flowering plants. Soybean genome encodes six homologs of FT, which display flowering activity in Arabidopsis thaliana. However, their contributions to flowering time in different soybean cultivars, especially in field conditions, are unclear. We employed six soybean cultivars with different maturities to extensively investigate expression patterns of GmFTLs (Glycine max FT-like) and GmCOLs (Glycine max CO-like) in the field conditions. The results show that GmFTL3 is an FT homolog with the highest transcript abundance in soybean, but other GmFTLs may also contribute to flower induction with different extents, because they have more or less similar expression patterns in developmental-, leaf-, and circadian-specific modes. And four GmCOL genes (GmCOL1/2/5/13) may confer to the expression of GmFTL genes. Artificial manipulation of GmFTL expression by transgenic strategy (overexpression and RNAi) results in a distinct change in soybean flowering time, indicating that GmFTLs not only impact on the control of flowering time, but have potential applications in the manipulation of photoperiodic adaptation in soybean. Additionally, transgenic plants show that GmFTLs play a role in formation of the first flowers and in vegetative growth.