Pressure Coupled Lanthanide Ion Doping to Enhance Optical Properties in BaTiO3.

Self-trapped excitons (STEs) typically give broadband photoluminescence emission with a large Stokes shift, which is important for the enhancement of the optical properties of materials. Here, low-dimensional La-doped BaTiO3 nanocrystals with defects are prepared using supercritical CO2 (SC CO2). The generation of the STEs is facilitated by doping La3+ ions and introducing CO2 pressure, which effectively enhance the luminescence intensity of BaTiO3. This discovery shows that the La ion doping concentration can modulate the photoluminescence of BaTiO3 nanocrystals under pressure. This work deepens the understanding of the influence of rare-earth-doped luminescent materials under pressure and provides insight to improve the capabilities of optical devices.

2D Heterostructure of CoCl2 /Co3 O4 Built for Strong Enhanced Magnetism.

As a remarkable structure, 2D magnetic heterojunctions have attracted researchers' attention owing to their controlled manipulation in the electronic device. However, successful fabrication as well as modulation of their structure and compound remain challenging. Herein, a novel method is designed to obtain a CoCl2 /Co3 O4 heterojunction on Si/SiO2 substrate with the assistance of supercritical CO2 (SC CO2 ), and the as-fabricated sample has significantly increased coercivity and saturation magnetization, which is 11 times higher than pure Co3 O4 . Further, it can be found that the CO2 pressure has the decisive effect on the saturation magnetization of the sample. Therefore, it suggests that the tunable electronic-magnetic device can be anticipated to be obtained in the future.

CO2 Stress-Driven Room Temperature Ferromagnetism of Ultrathin 2D Gallium Oxide.

Spintronic devices work by manipulating the spin of electrons other than charge transfer, which is of revolutionary significance and can largely reduce energy consumption in the future. Herein, ultrathin two-dimensional (2D) non-van der Waals (non-vdW) γ-Ga2O3 with room temperature ferromagnetism is successfully obtained by using supercritical CO2 (SC CO2). The stress effect of SC CO2 under different pressures selectively modulates the orientation and strength of covalent bonds, leading to the change of atomic structure including lattice expansion, introduction of O vacancy, and transition of Ga-O coordination (GaO4 and GaO6). Magnetic measurements show that pristine γ-Ga2O3 is nonferromagnetic, whereas the SC CO2 treated γ-Ga2O3 exhibits obvious ferromagnetic behavior with an optimal magnetization of 0.025 emu g-1 and a Curie temperature of 300 K.

Fe and Cu Intercalations Enhance SERS of MoO3 through Different Mechanistic Pathways.

Surface Enhanced Raman spectroscopy (SERS) is a molecular-specific analytical technique with various applications. Although electromagnetic (EM) and chemical (CM) mechanisms have been proposed to be the main origins of SERS, exploring highly sensitive SERS substrates with well-defined mechanistic pathways remains challenging. Since surface and electronic structures of substrates were crucial for SERS activity, zero-valent transition metals (Fe and Cu) were intercalated into MoO3 to modulate its surface and electronic structures, leading to unexceptional high enhancement factors (1.0×108 and 1.1×1010 for Fe-MoO3 and Cu-MoO3 , respectively) with decent reproducibility and stability. Interestingly, different mechanistic pathways (CM and EM) were proposed for Fe-MoO3 and Cu-MoO3 according to mechanistic investigations. The different mechanisms of Fe-MoO3 and Cu-MoO3 were rationalized by the electronic structures of the intercalated Fe(0) and Cu(0), which modulates the surface and electronic structures of Fe-MoO3 and Cu-MoO3 to differentiate their SERS mechanisms.

Linkage-Mediated Electronic Structure Modulation in Multicomponent Covalent Organic Frameworks for Dramatically Promoted Photocatalytic Hydrogen Evolution.

Linkage chemistry is an essential aspect to covalent organic framework (COF) applications; it is highly desirable to precisely modulate electronic structure mediated directly by linkage for efficient COF-based photocatalytic hydrogen evolution, which however, remains substantially challenging. Herein, as a proof of concept, a collection of robust multicomponent pyrene-based COFs with abundant donor-acceptor (D-A) interactions has been judiciously designed and synthesized through molecularly engineering linkage for photogeneration of hydrogen. Controlled locking and conversion of linkage critically contribute to continuously regulating COFs' electronic structures further to optimize photocatalytic activities. Remarkably, the well-modulated optoelectronic properties turn on the average hydrogen evolution rate from zero to 15.67 mmol g-1 h-1 by the protonated quinoline-linked COF decorated with the trifluoromethyl group (TT-PQCOF-CF3). Using diversified spectroscopy and theoretical calculations, we show that multiple modifications toward linkage synergistically lead to the redistribution of charge on COFs with extended π-conjugation and reinforced D-A effect, making TT-PQCOF-CF3 a promising material with significantly boosted carrier separation and migration. This study provides important guidance for the design of high-performance COF photocatalysts based on the strategy of linkage-mediated electronic structure modulation in COFs.

Interfacial Bonding Induced Charge Transfer in Two-Dimensional Amorphous MoO3-x/Graphdiyne Oxide Non-Van der Waals Heterostructures for Dominant SERS Enhancement.

Two-dimensional semiconductor-based nanomaterials have shown to be an effective substrate for Surface-enhanced Raman Scattering (SERS) spectroscopy. However, the enhancement factor (EF) tends to be relatively weak compared to that of noble metals and does not allow for trace detection of molecules. In this work, we report the successful preparation of two-dimensional (2D) amorphous non-van der Waals heterostructures MoO3-x/GDYO nanomaterials using supercritical CO2. Due to the synergistic effect of the localized surface plasmon resonance (LSPR) effect and the charge transfer effect, it exhibits excellent SERS performance in the detection of methylene blue (MB) molecules, with a detection limit as low as 10-14 M while the enhancement factor (EF) can reach an impressive 2.55×1011. More importantly, the chemical bond bridging at the MoO3-x/GDYO heterostructures interface can accelerate the electron transfer between the interfaces, and the large number of defective surface structures on the heterostructures surface facilitates the chemisorption of MB molecules. And the charge recombination lifetime can be proved by a ~1.7-fold increase during their interfacial electron-transfer process for MoO3-x/GDYO@MB mixture, achieving highly sensitive SERS detection.

The Health and Economic Burden of Ozone Pollution on Alzheimer's Disease and Mild Cognitive Impairment in China.

Ozone pollution is increasingly recognized as a serious environmental threat that exacerbates dementia risks, including Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). Amid rapid industrialization, China faces significant air quality challenges. However, there has been a scarcity of detailed studies assessing the health and economic impacts of ozone pollution on these conditions. This study aims to address this gap by utilizing the BenMap-CE tool and incorporating parameters obtained from systematic reviews of epidemiologic studies, official statistics, and weighted averages, to accurately quantify the effects of ozone exposure in China. This research evaluated the health and economic burdens at both national and provincial levels, focusing on the additional impacts attributed to increased ozone levels. The results reveal that in 2023, compared to 2015, ozone pollution contributed to approximately 110,000 new cases (5.6 per 10,000) of AD and 1.6 million new cases (81.7 per 10,000) of MCI, imposing significant economic costs of about 1,200 million USD for AD and 18,000 million USD for MCI, based on 2015 dollar values. Additionally, our projections indicate that reducing the 2023 ozone concentrations to 70 µg/m3 could significantly curb these conditions, potentially preventing over 210,000 new AD cases (10.7 per 10,000) and 2.9 million (148.1 per 10,000) MCI cases. Such reductions are projected to yield substantial economic benefits, estimated at 2,200 million USD for AD and 34,000 million USD for MCI (2015 dollar values). These findings underscore the profound implications of ozone pollution on public health and the economy in China, highlighting the urgent need for effective ozone management strategies to mitigate these impacts.

Heavy metals and elderly kidney health: A multidimensional study through Enviro-target Mendelian Randomization.

Chronic Kidney Disease (CKD), closely linked to environmental factors, poses a significant public health challenge. This study, based on 529 triple-repeated measures from key national environmental pollution area and multiple gene-related public databases, employs various epidemiological and bioinformatics models to assess the impact of combined heavy metal exposure (Chromium [Cr], Cadmium [Cd], and Lead [Pb]) on early renal injury and CKD in the elderly. Introducing the novel Enviro-Target Mendelian Randomization method, our research explores the causal relationship between metals and CKD. The findings indicate a positive correlation between increased levels of metal and renal injury, with combined exposure caused renal damage more significantly than individual exposure. The study reveals that metals primarily influence CKD development through oxidative stress and metal ion resistance pathways, focusing on three related genes (SOD2, MPO, NQO1) and a transcription factor (NFE2L2). Metals were found to regulate oxidative stress levels in the body by increasing the expression of SOD2, MPO, NQO1, and decreasing NFE2L2, leading to CKD onset. Our research establishes a new causal inference framework linking environmental pollutants-pathways-genes-CKD, assessing the impact and mechanisms of metal exposure on CKD. Future studies with more extensive in vitro evidence and larger population are needed to validate.

Complex interplay of heavy metals and renal injury: New perspectives from longitudinal epidemiological evidence.

BACKGROUND: Epidemiological studies have reported associations between heavy metals and renal function. However, longitudinal studies are required to further validate these associations and explore the interactive effects of heavy metals on renal function and their directional influence. METHOD: This study, conducted in Northeast China from 2016 to 2021, included a four-time repeated measures design involving 384 participants (1536 observations). Urinary concentrations of chromium (Cr), cadmium (Cd), manganese (Mn), and lead (Pb) were measured, along with renal biomarkers including urinary microalbumin (umAlb), urinary albumin-to-creatinine ratio (UACR), N-acetyl-ß-D-glucosaminidase (NAG), and ß2-microglobulin (ß2-MG) levels. Estimated glomerular filtration rate (eGFR) was calculated. A Linear Mixed Effects Model (LME) examined the association between individual metal exposure and renal biomarkers. Subsequently, Quantile g-computation and Bayesian Kernel Machine Regression (BKMR) models assessed the overall effects of heavy metal mixtures. Marginal Effect models examined the directional impact of metal interactions in the BKMR on renal function. RESULT: Results indicate significant impacts of individual and combined exposures of Cr, Cd, Pb, and Mn on renal biomarkers. Metal interactions in the BKMR model were observed, with synergistic effects of Cd-Cr on NAG, umAlb, UACR; Cd-Pb on NAG, UACR; Pb-Cr on umAlb, UACR, eGFR-MDRD, eGFR-EPI; and an antagonistic effect of Mn-Pb-Cr on UACR. CONCLUSION: Both individual and combined exposures to heavy metals are associated with renal biomarkers, with significant synergistic interactions leading to renal damage. Our findings elucidate potential interactions among these metals, offering valuable insights into the mechanisms linking multiple metal exposures to renal injury.

Joint and interactive effects of metal mixtures on liver damage: Epidemiological evidence from repeated-measures study.

BACKGROUND: The impact of heavy metals on liver function has been examined in numerous epidemiological studies. However, these findings lack consistency and longitudinal validation. METHODS: In this study, we conducted three follow-up surveys with 426 participants from Northeast China. Blood and urine samples were collected, along with questionnaire information. Urine samples were analyzed for concentrations of four metals (chromium [Cr], cadmium [Cd], lead [Pb], and manganese [Mn]), while blood samples were used to measure five liver function indicators (alanine aminotransferase [ALT], aspartate aminotransferase [AST], albumin [ALB], globulin [GLB], and total protein [TP]). We utilized a linear mixed-effects model (LME) to explore the association between individual heavy metal exposure and liver function. Joint effects of metal mixtures were investigated using quantile g-computation and Bayesian kernel machine regression (BKMR). Furthermore, we employed BKMR and Marginal Effect models to examine the interaction effects between metals on liver function. RESULTS: The LME results demonstrated a significant association between urinary heavy metals (Cr, Cd, Pb, and Mn) and liver function markers. BKMR results indicated positive associations between heavy metal mixtures and ALT, AST, and GLB, and negative associations with ALB and TP, which were consistent with the g-comp results. Synergistic effects were observed between Cd-Cr on ALT, Mn-Cr and Cr-Pb on ALB, while an antagonistic effect was found between Mn-Pb and Mn-Cd on ALB. Additionally, synergistic effects were observed between Mn-Cr on GLB and Cd-Cr on TP. Furthermore, a three-way antagonistic effect of Mn-Pb-Cr on ALB was identified. CONCLUSION: Exposure to heavy metals (Cr, Cd, Mn, Pb) is associated with liver function markers, potentially leading to liver damage. Moreover, there are joint and interaction effects among these metals, which warrant further investigation at both the population and mechanistic levels.

Toward digitally screening and profiling AD: A GAMLSS approach of MemTrax in China.

PURPOSES: To establish a normative range of MemTrax (MTx) metrics in the Chinese population. METHODS: The correct response percentage (MTx-%C) and mean response time (MTx-RT) were obtained and the composite scores (MTx-Cp) calculated. Generalized additive models for location, shape and scale (GAMLSS) were applied to create percentile curves and evaluate goodness of fit, and the speed-accuracy trade-off was investigated. RESULTS: 26,633 subjects, including 13,771 (51.71%) men participated in this study. Age- and education-specific percentiles of the metrics were generated. Q tests and worm plots indicated adequate fit for models of MTx-RT and MTx-Cp. Models of MTx-%C for the low and intermediate education fit acceptably, but not well enough for a high level of education. A significant speed-accuracy trade-off was observed for MTx-%C from 72 to 94. CONCLUSIONS: GAMLSS is a reliable method to generate smoothed age- and education-specific percentile curves of MTx metrics, which may be adopted for mass screening and follow-ups addressing Alzheimer's disease or other cognitive diseases. HIGHLIGHTS: GAMLSS was applied to establish nonlinear percentile curves of cognitive decline. Subjects with a high level of education demonstrate a later onset and slower decline of cognition. Speed-accuracy trade-off effects were observed in a subgroup with moderate accuracy. MemTrax can be used as a mass-screen instrument for active cognition health management advice.

CD163-Mediated Small-Vessel Injury in Alzheimer's Disease: An Exploration from Neuroimaging to Transcriptomics.

Patients with Alzheimer's disease (AD) often present with imaging features indicative of small-vessel injury, among which, white-matter hyperintensities (WMHs) are the most prevalent. However, the underlying mechanism of the association between AD and small-vessel injury is still obscure. The aim of this study is to investigate the mechanism of small-vessel injury in AD. Differential gene expression analyses were conducted to identify the genes related to WMHs separately in mild cognitive impairment (MCI) and cognitively normal (CN) subjects from the ADNI database. The WMH-related genes identified in patients with MCI were considered to be associated with small-vessel injury in early AD. Functional enrichment analyses and a protein-protein interaction (PPI) network were performed to explore the pathway and hub genes related to the mechanism of small-vessel injury in MCI. Subsequently, the Boruta algorithm and support vector machine recursive feature elimination (SVM-RFE) algorithm were performed to identify feature-selection genes. Finally, the mechanism of small-vessel injury was analyzed in MCI from the immunological perspectives; the relationship of feature-selection genes with various immune cells and neuroimaging indices were also explored. Furthermore, 5×FAD mice were used to demonstrate the genes related to small-vessel injury. The results of the logistic regression analyses suggested that WMHs significantly contributed to MCI, the early stage of AD. A total of 276 genes were determined as WMH-related genes in patients with MCI, while 203 WMH-related genes were obtained in CN patients. Among them, only 15 genes overlapped and were thus identified as the crosstalk genes. By employing the Boruta and SVM-RFE algorithms, CD163, ALDH3B1, MIR22HG, DTX2, FOLR2, ALDH2, and ZNF23 were recognized as the feature-selection genes linked to small-vessel injury in MCI. After considering the results from the PPI network, CD163 was finally determined as the critical WMH-related gene in MCI. The expression of CD163 was correlated with fractional anisotropy (FA) values in regions that are vulnerable to small-vessel injury in AD. The immunostaining and RT-qPCR results from the verifying experiments demonstrated that the indicators of small-vessel injury presented in the cortical tissue of 5×FAD mice and related to the upregulation of CD163 expression. CD163 may be the most pivotal candidates related to small-vessel injury in early AD.

Monomeric CXCL12-Engineered Adipose-Derived Stem Cells Transplantation for the Treatment of Ischemic Stroke.

Adipose-derived stem cells (ASCs) possess therapeutic potential for ischemic brain injury, and the chemokine CXCL12 has been shown to enhance their functional properties. However, the cumulative effects of ASCs when combined with various structures of CXCL12 on ischemic stroke and its underlying molecular mechanisms remain unclear. In this study, we genetically engineered mouse adipose-derived ASCs with CXCL12 variants and transplanted them to the infarct region in a mice transient middle cerebral artery occlusion (tMCAO) model of stroke. We subsequently compared the post-ischemic stroke efficacy of ASC-mCXCL12 with ASC-dCXCL12, ASC-wtCXCL12, and unmodified ASCs. Neurobehavior recovery was assessed using modified neurological severity scores, the hanging wire test, and the elevated body swing test. Changes at the tissue level were evaluated through cresyl violet and immunofluorescent staining, while molecular level alterations were examined via Western blot and real-time PCR. The results of the modified neurological severity score and cresyl violet staining indicated that both ASC-mCXCL12 and ASC-dCXCL12 treatment enhanced neurobehavioral recovery and mitigated brain atrophy at the third and fifth weeks post-tMCAO. Additionally, we observed that ASC-mCXCL12 and ASC-dCXCL12 promoted angiogenesis and neurogenesis, accompanied by an increased expression of bFGF and VEGF in the peri-infarct area of the brain. Notably, in the third week after tMCAO, the ASC-mCXCL12 exhibited superior outcomes compared to ASC-dCXCL12. However, when treated with the CXCR4 antagonist AMD3100, the beneficial effects of ASC-mCXCL12 were reversed. The AMD3100-treated group demonstrated worsened neurological function, aggravated edema volume, and brain atrophy. This outcome is likely attributed to the interaction of monomeric CXCL12 with CXCR4, which regulates the recruitment of bFGF and VEGF. This study introduces an innovative approach to enhance the therapeutic potential of ASCs in treating ischemic stroke by genetically engineering them with the monomeric structure of CXCL12.

A Dataset on Population Activity Patterns in Typical Regions of North China.

This data article describes the "Typical Regional Activity Patterns" (TRAP) dataset, which is based on the Tackling Key Problems in Air Pollution Control Program. In order to explore the interaction between air pollution and physical activity, we collected activity patterns of 9,221 residents with different occupations and lifestyles for three consecutive days in typical regions (Jinan and Baoding) where air pollutant concentrations were higher than those in neighboring areas. The TRAP dataset consists of two aspects of information: demographic indicators (personal information, occupation, personal habits, and living situation) and physical activity pattern data (activity location and intensity); additionally, the exposure measures of physical activity patterns are included, which data users can match to various endpoints for their specific purpose. This dataset provides evidence for exploring the attributes of activity patterns of residents in northern China and for interdisciplinary researchers to develop strategies and measures for health education and health promotion.

Supercritical CO2 Directional-Assisted Synthesis of Low-Dimensional Materials for Functional Applications.

Supercritical CO2 (SC CO2 ), as one of the unique fluids that possess fascinating properties of gas and liquid, holds great promise in chemical reactions and fabrication of materials. Building special nanostructures via SC CO2 for functional applications has been the focus of intense research for the past two decades, with facile regulated reaction conditions and a particular reaction field to operate compared to the more widely used solvent systems. In this review, the significance of SC CO2 on fabricating various functional materials including modification of 1D carbon nanotubes, 2D materials, and 2D heterostructures is stated. The fundamental aspects involving building special nanostructures via SC CO2 are explored: how their structure, morphology, and chemical composition be affected by the SC CO2 . Various optimization strategies are outlined to improve their performances, and recent advances are combined to present a coherent understanding of the mechanism of SC CO2 acting on these functional nanostructures. The wide applications of these special nanostructures in catalysis, biosensing, optoelectronics, microelectronics, and energy transformation are discussed. Moreover, the current status of SC CO2 research, the existing scientific issues, and application challenges, as well as the possible future directions to advance this fertile field are proposed in this review.

Strong Ferromagnetic Manipulation of SrTiO3 from CO2 -Straining Effect on Electronic Structure Modulation.

2D magnetic materials are ideal to fabricate magneto-optical, magneto-electric, and data storage devices, which are proposed to be critical to the next generation of information technologies. Benefited from their labile structures, 2D perovskites are amenable for magnetic manipulation through structural optimization. In this work, 2D room-temperature ferromagnetic SrTiO3 is achieved through straining effect induced by supercritical carbon dioxide (SC CO2 ). According to experimental results, the cubic phase of SrTiO3 is converted to tetragonal with exposure of (110), (200), (111), and (211) planes over the SC CO2 treatment, leading to significant ferromagnetic enhancement. Theoretical calculations illustrate that over the conversion from cubic to tetragonal, the electronic structure of SrTiO3 is significantly modulated. Specifically, the spin density of planes of (200), (111), and (211) is enhanced, presumably due to the stabilization of the highest occupied molecular orbital over straining by SC CO2 , leading to magnetic optimizations. This work suggests that magnetic optimization can be achieved from SC CO2 -induced electronic structure modulation.

Porphyrin-based Bi-MOFs with Enriched Surface Bi Active Sites for Boosting Photocatalytic CO2 Reduction.

The inherent challenges in using metal-organic frameworks (MOFs) for photocatalytic CO2 reduction are the combination of wide-range light harvesting, efficient charge separation and transfer as well as highly exposed catalytic active sites for CO2 activation and reduction. We present here a promising solution to satisfy these requirements together by modulating the crystal facet and surface atomic structure of a porphyrin-based bismuth-MOF (Bi-PMOF). The series of structural and photo-electronic characterizations together with photocatalytic CO2 reduction experiment collectively establish that the enriched Bi active sites on the (010) surface prefer to promote efficient charge separation and transfer as well as the activation and reduction of CO2 . Specifically, the Bi-PMOFs-120-F with enriched surface Bi active sites exhibits optimal photocatalytic CO2 reduction performance to CO (28.61 µmol h-1 g-1 ) and CH4 (8.81 µmol h-1 g-1 ). This work provides new insights to synthesize highly efficient main group p-block metal Bi-MOF photocatalysts for CO2 reduction through a facet-regulation strategy and sheds light on the surface structure-activity relationships of the MOFs.

Construction of Long-Range Magnetic Sequences on Different Surfaces of BaTiO3.

Using the first-principles spin-density-functional theory calculations, we studied the origin of ferromagnetism from non-magnetic ferroelectric barium titanate (BaTiO3 ) and found out vacancies in different surface can successfully contribute to the origin of ferromagnetism. Accurately, our findings demonstrate that both O and Ti vacancies induce ferromagnetism on the (001) and (010) surfaces of BaTiO3 , and the optimal Ti-O bond length can control the vacancy-induced spin density that is delocalized or concentrated in the real space outside the vacancy, and it helps to enhance our understanding on the long-range magnetic order induced by the vacancy. In addition, intrinsic magnetism is shown on the defect-free (110) surface, and the structure is found to be a near-ideal two-dimensional Ising ferromagnet with large magnetocrystalline anisotropy, and it supplies the platform for studying basic spin behavior of BaTiO3 and more according materials.

Supercritical CO2 -induced New Chemical Bond of C-O-Si in Graphdiyne to Achieve Robust Room-Temperature Ferromagnetism.

The realization of ferromagnetic ordering of two-dimensional (2D) carbon material graphdiyne (GDY) has attracted great attention due to its promising application in spin semiconductor devices. However, the absence of localized spins makes the pristine GDY intrinsically nonferromagnetic. Herein, we report the realization of robust room-temperature (RT) ferromagnetism (FM) with Curie temperature (TC ) up to 325 K for GDY Nanosheets (GDYNs) by supercritical CO2 (SC CO2 ). Experimental and theoretical calculations reveal that the new chemical bond of C-O-Si can be formed because of the unique effect of SC CO2 , which help to enhance the charge transfer and generates long-range ferromagnetic order. The RT saturation magnetization (MS ) reaches 1.125 emu/g, which is much higher than that of carbon-based materials reported up to now. Meanwhile, by changing the conditions of SC CO2 such as pressure, ferromagnetic responses can be manipulated, which is great for potential spintronics applications of GDY.

Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.).

Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01423-y.