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Abstract Controlled release formulation (CRF) of herbicide is an effective weed management technique with less eco-toxicity than other available commercial formulations. To maximise the effectiveness of CRFs however, it is crucial to understand the herbicide-releasing behaviour at play, which predominately depends on the interaction mechanisms between active ingredients and carrier materials during adsorption. In this study, we investigated and modelled the adsorption characteristics of model herbicide 2,4-D onto two organo-montmorillonites (octadecylamine- and aminopropyltriethoxysilane-modified) to synthesise polymer-based CRFs. Herbicide-releasing behaviour of the synthesised CRF microbeads was then analysed under various experimental conditions, and weed control efficacy determined under glasshouse conditions. Results revealed that adsorption of 2,4-D onto both organo-montmorillonites follows the pseudo-second-order kinetics model and is predominately controlled by the chemisorption process. However, multi-step mechanisms were detected in the adsorption on both organoclays, hence intra-particle diffusion is not the sole rate-limiting step for the adsorption process. Both organoclays followed the Elovich model, suggesting they have energetically heterogeneous surfaces. Herbicide-releasing behaviours of synthesised beads were investigated at various pH temperatures and ionic strengths under laboratory and glasshouse conditions. Furthermore, weed control efficacy of synthesised beads were investigated using pot studies under glasshouse condition. Desorption studies revealed that both synthesised microbeads have slow releasing behaviour at a wide range of pHs (5–9), temperatures (25–45 °C), and ionic strengths. The results also revealed that synthesised microbeads have excellent weed control efficacy on different broad-leaf weed species under glasshouse conditions.

The complete chloroplast genome sequence of Clematis mandshurica Ruprecht (1867), a specie of the Ranunculaceae family, and its phylogenetic relationships with other species have been reported in this study. The complete chloroplast genome of C. mandshurica is 159,563 bp in length, including a large single-copy (LSC) region of 79,360 bp, a small single-copy (SSC) region of 18,121 bp, and a pair of identical inverted repeat regions (IRs) of 31,041 bp. The genome encodes a total of 132 genes, including 90 protein-coding genes, 34 transfer RNA (tRNA) genes, and eight ribosomal RNA (rRNA) genes. The phylogenetic analysis reveals that C. mandshurica was found to be closest to Clematis taeguensis. The complete chloroplast genome of C. mandshurica contributes to a better understanding of phylogenetic relationships among Clematis species.

Abstract In this work, we present a comprehensive study of holographic stress tensor correlators on general Riemann surfaces, extending beyond the previously well-studied torus cases to explore higher genus conformal field theories (CFTs) within the framework of the Anti-de Sitter/conformal field theory (AdS/CFT) correspondence. We develop a methodological approach to compute holographic stress tensor correlators, employing the Schottky uniformization technique to address the handlebody solutions for higher genus Riemann surfaces. Through rigorous calculations, we derive four-point stress tensor correlators, alongside recurrence relations for higher-point correlators, within the AdS3/CFT2 context. Additionally, our research delves into the holography of cutoff AdS3 spaces, offering novel insights into the lower-point correlators of the T T ¯ $$ T\overline{T} $$ -deformed theories on higher genus Riemann surfaces up to the first deformation order.

Abstract Background Benign prostatic hyperplasia (BPH) is a prevalent urological disease in elderly males. However, few studies have estimated the temporal and spatial distributions of the BPH burden in male adults aged 60 years and over at the global, national, and regional scales. Methods Leveraging the Global Burden of Disease, Injuries, and Risk Factors Study, we estimated the global epidemiological trends in the prevalence, incidence, and disability-adjusted life-years (DALYs) of BPH in 204 countries and 21 regions and 5 sociodemographic index (SDI) regions in males aged 60 years and over between 1990 and 2019. The average annual percentage changes (AAPCs) in age-specific rates were estimated to quantify overall trends. We estimated the contribution of population aging and epidemiological alterations in disease burden via composition analysis. Results Over the past three decades, the global prevalent cases, incident cases and DALYs of BPH have increased, ranging from 118.78 to 121.22%. The global number of prevalent BPH cases reached 79 million in people aged 60 years and older in 2019. The prevalence, incidence, and DALYs rates gradually increased, with AAPCs of 0.02, 0.02, and 0.01, respectively. Low-middle, middle, and low SDI regions experienced rapid increases in the number of prevalent cases of BPH. In 2019, China, India, and United States of America bore the largest burden of prevalent cases among people aged 60 years and over. The three regions with the highest prevalence rates of BPH were Eastern Europe, Central Latin America, and Andean Latin America. The increased prevalence was attributed to population growth (94.93%), epidemiological changes (3.45%), and aging (1.62%), globally. Conclusions BPH is a global health issue that imposes substantial economic burdens on most countries, particularly males aged 60 years and over. Effective health decisions are imperative for BPH prevention and treatment.

Molecule interacting with CasL 1 (MICAL1) is a crucial protein involved in cell motility, axon guidance, cytoskeletal dynamics, and gene transcription. This pan-cancer study analyzed MICAL1 across 33 cancer types using bioinformatics and experiments. Dysregulated expression, diagnostic potential, and prognostic value were assessed. Associations with tumor characteristics, immune factors, and drug sensitivity were explored. Enrichment analysis revealed MICAL1’s involvement in metastasis, angiogenesis, metabolism, and immune pathways. Functional experiments demonstrated its impact on renal carcinoma cells. These findings position MICAL1 as a potential biomarker and therapeutic target in specific cancers, warranting further investigation into its role in cancer pathogenesis.

Abstract Purpose Prostate cancer (PCa) is a common malignancy in men, with an escalating mortality rate attributed to Recurrence and metastasis. Recent studies have illuminated collagen’s critical regulatory role within the tumor microenvironment, significantly influencing tumor progression. Accordingly, this investigation is dedicated to examining the relationship between genes linked to collagen and the prognosis of PCa, with the objective of uncovering any possible associations between them. Methods Gene expression data for individuals with prostate cancer were obtained from the TCGA repository. Collagen-related genes were identified, leading to the development of a risk score model associated with biochemical recurrence-free survival (BRFS). A prognostic nomogram integrating the risk score with essential clinical factors was crafted and evaluated for efficacy. The influence of key collagen-related genes on cellular behavior was confirmed through various assays, including CCK8, invasion, migration, cell cloning, and wound healing. Immunohistochemical detection was used to evaluate PLOD3 expression in prostate cancer tissue samples. Results Our study identified four key collagen-associated genes (PLOD3, COL1A1, MMP11, FMOD) as significant. Survival analysis revealed that low-risk groups, based on the risk scoring model, had significantly improved prognoses. The risk score was strongly associated with prostate cancer prognosis. Researchers then created a nomogram, which demonstrated robust predictive efficacy and substantial clinical applicability.Remarkably, the suppression of PLOD3 expression notably impeded the proliferation, invasion, migration, and colony formation capabilities of PCa cells. Conclusion The risk score, derived from four collagen-associated genes, could potentially act as a precise prognostic indicator for BRFS of patients. Simultaneously, our research has identified potential therapeutic targets related to collagen. Notably, PLOD3 was differentially expressed in cancer and para-cancer tissues in clinical specimens and it also was validated through in vitro studies and shown to suppress PCa tumorigenesis following its silencing.

Abstract We derive methods for risk-neutral pricing of multi-asset options, when log-returns jointly follow a multivariate tempered stable distribution. These lead to processes that are more realistic than the better known Brownian motion and stable processes. Further, we introduce the diagonal tempered stable model, which is parsimonious but allows for rich dependence between assets. Here, the number of parameters only grows linearly as the dimension increases, which makes it tractable in higher dimensions and avoids the so-called “curse of dimensionality.” As an illustration, we apply the model to price multi-asset options in two, three, and four dimensions. Detailed goodness-of-fit methods show that our model fits the data very well.

Abstract Objective To explore the association between cardiovascular health (CVH) measured by Life’s Essential 8 (LE8) and the prevalence of urinary incontinence (UI). Method A cross-section study was conducted using data from the National Health and Nutrition Examination Survey 2007–2012. 22,609 people aged ≥ 20 years with complete information on LE8 metrics and UI questionnaires were enrolled. Participants were divided into three groups (low:

Abstract Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a deep learning with a multilayer neural network (the NMTLR model) for predicting the survival rate of patients with Primary Hepatocellular Carcinoma. HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and fivefold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. The study included 2197 HCC patients, randomly divided into a training cohort (70%, n = 1537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model demonstrated enhanced accuracy and well-calibrated survival estimates, achieving an Area Under the Curve (AUC) of 0.824 for 1-year survival predictions, 0.813 for 3-year, and 0.803 for 5-year survival rates. This model's superior calibration and discriminative ability enhance its utility for clinical prognostication in Primary Hepatocellular Carcinoma. We deployed the NMTLR model as a web application for clinical practice. The NMTLR model have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with primary liver cancer.

Abstract Electrochromic smart windows (ESWs) are an effective energy‐saving technology for near‐zero energy buildings. They consume electric energy unidirectionally during a round‐trip coloring‐bleaching process, with the energy involved in the bleaching process being wasted. It is highly desirable to reuse this wasted electric energy directly and/or transfer it into other energy storage equipment, further enhancing the overall efficiency of electric energy usage. Herein, a zinc anode‐based ESW (ESW‐PZ) is reported that not only has fascinating visible–near‐infrared (VIS‐NIR) dual‐band electrochromic performance (a high optical contrast of 63%) but also showcases good energy storage characteristics (a wide voltage window of 2.6 V and a high energy density of 127.5 µWh cm−2). The buildings utilizing ESW‐PZ to modulate indoor environments demonstrated an average annual energy saving of 366 MJ m−2 based on energy simulations, which is about 16% of the total energy consumption. Impressively, a high utilization efficiency of 90% (855 mWh m−2) of the wasted electric energy is realized through an ingenious circuit‐switching strategy, which can be reused to power small household appliances.

Antiferromagnetic Weyl semimetal Mn3Sn exhibiting strong magneto-optical Kerr effect (MOKE) due to non-zero Berry curvature is attractive for spintronic and photonic device applications. Despite many reports on the anisotropic anomalous Hall effect (AHE), so far, there have been few studies on its anisotropic optical properties. In this work, we experimentally characterized the anisotropic optical and magneto-optical (MO) properties of Mn3Sn(20)/MgO(110) and Mn3Sn(0001)/Al2O3(0001) epitaxial films using ellipsometry in the wavelength range from 300 to 1690 nm. By measuring the Mueller matrix of magnetized Mn3Sn, the anisotropic permittivity tensor is determined using the 4 × 4 transfer matrix method. Temperature dependent MOKE measurement confirmed the origin of the anisotropic MO effect to the non-zero Berry curvature of the chiral magnetic phase. The measured permittivity also agrees well with first-principles calculations. The anisotropic optical and MO properties determined in this work can be useful for Mn3Sn based spintronic device characterization and photonic device development.

This paper reports a strategy for more reliably obtaining sputtered thin yttrium–iron–garnet (YIG) films possessing both low magnetic damping (α) and a highly spin transparent surface, which represent two of the most important properties for YIG films applied in spintronic devices. The two key points of this strategy, concluded from our systematical studies, are as follows: oxygen reactive sputtering of a slight Y-rich YIG target to avoid the over-stoichiometry of the Fe component and at the same time minimize oxygen vacancy density; and employing phosphoric acid wet etching to remove the inevitable thin magnetic dead layer formed on the surface. The feasibility of this strategy was proved by the achievement of a high quality 30 nm-thick YIG film. It possesses a ferromagnetic resonance linewidth of only 3.4 Oe at 8 GHz, α of only 4.6 × 10−4, and a very spin-transparent surface, as proved by the measured extremely large spin pumping voltage of 650.1 µV after depositing a 3 nm Pt layer.

Summary: Background: The prevalence of metabolic-associated steatotic liver disease (MASLD) is rising precipitously among children, particularly in regions or countries burdened with high prevalence of obesity. However, identifying those at high risk remains a significant challenge, as the majority do not exhibit distinct symptoms of MASLD. There is an urgent need for a widely accepted non-invasive predictor to facilitate early disease diagnosis and management of the disease. Our study aims to 1) evaluate and compare existing predictors of MASLD, and 2) develop a practical screening strategy for children, tailored to local prevalence of obesity. Methods: We utilized a school-based cross-sectional survey in Beijing as the training dataset to establish predictive models for screening MASLD in children. An independent school-based study in Ningbo was used to validate the models. We selected the optimal non-invasive MASLD predictor by comparing logistic regression model, random forest model, decision tree model, and support vector machine model using both the Beijing and Ningbo datasets. This was followed by serial testing using the best performance index we identified and indices from previous studies. Finally, we calculated the potential MASLD screening recommendation categories and corresponding profits based on national and subnational obesity prevalence, and applied those three categories to 200 countries according to their obesity prevalence from 1990 to 2022. Findings: A total of 1018 children were included (NBeijing = 596, NNingbo = 422). The logistic regression model demonstrated the best performance, identifying the waist-to-height ratio (WHtR, cutoff value ≥0.48) as the optimal noninvasive index for predicting MASLD, with strong performance in both training and validation set. Additionally, the combination of WHtR and lipid accumulation product (LAP) was selected as an optimal serial test to improve the positive predictive value, with a LAP cutoff value of ≥668.22 cm × mg/dL. Based on the obesity prevalence among 30 provinces, three MASLD screening recommendations were proposed: 1) “Population-screening-recommended”: For regions with an obesity prevalence ≥12.0%, where MASLD prevalence ranged from 5.0% to 21.5%; 2) “Resources-permitted”: For regions with an obesity prevalence between 8.4% and 12.0%, where MASLD prevalence ranged from 2.3% to 4.4%; 3) “Population-screening-not-recommended”: For regions with an obesity prevalence

High performance concrete (HPC) has the characteristics of high strength, high brittleness and low toughness, so it can not be widely used in engineering field. The rubber particles themselves have good elasticity and excellent wear resistance. To this end, rubber particles were used to prepare high performance rubber concrete (HPRC) instead of fine aggregate, and compressive strength and splitting tensile strength tests were carried out according to standard test methods. These data were evaluated, and it was found that adding different mesh number (10 mesh, 20 mesh, 30 mesh) and different content (10%, 20%, 30%) of rubber particles reduced the compressive and tensile properties of high-performance rubber concrete to different degrees. The rubber particles with l size of 30 mesh and content of 10% have the least influence on the mechanical properties of high-performance rubber concrete, and the compressive strength and tensile strength of HPC 28 days only decrease by 18.19% and 5.56%, respectively. From the damage form, the addition of rubber particles makes the high performance concrete change from brittle to ductile. The research shows that recycling rubber from waste tires into concrete manufacturing is an environmentally friendly and feasible waste management strategy. These results have the potential to replace concrete in construction and promote sustainable growth.

Background: Individual dietary antioxidants have been shown to be protective factors against kidney stone disease (KSD). However, the correlation between the composite dietary antioxidant index (CDAI), a reliable indicator for the total dietary antioxidant intake, and KSD has not yet been investigated. Objectives: To evaluate the association between the CDAI and prevalence of KSD in American adults. Methods: This cross-sectional analysis utilized data from the National Health and Nutrition Examination Surveys (NHANES) 2007–2018. Participants aged ≥ 20 years with a self-reported history of KSD and CDAI were included. Weighted proportions and multivariate regression models were used to study the independent relationship between KSD and CDAI. Generalized additive model (GAM) and smooth curve fitting were performed to explore the potential non-linear correlation, and a two-piecewise linear regression model was then applied to examine the threshold effect and to calculate the turning point. Results: A total of 29 763 adults were included, with a mean CDAI of 0.64. The prevalence of KSD was 10.02 %. Multivariate regression analysis indicated that the CDAI was negatively correlated with KSD [OR(95 %CI) = 0.979 (0.962, 0.996),p = 0.018]. Compared to participants in the lowest CDAI quartile, those in the highest CDAI quartile had a significantly lower kidney stone prevalence [OR(95 %CI) = 0.786 (0.667, 0.926),p = 0.004]. Smooth curve fitting and piecewise linear regression suggested that the turning point of the CDAI was 4.146. When the CDAI was less than 4.146, the prevalence of KSD decreased by 4.5 % for every 1 unit increase in CDAI [OR(95 %CI) = 0.955 (0.933, 0.978), p

Abstract Background Erectile dysfunction (ED) is closely associated with dyslipidemia; however, it is yet unknown how ED and remnant cholesterol (RC) are related. As such, this research sought to explore the correlation between RC and ED among individuals with diagnosed with diabetes. Methods This cross-sectional study used information from 215 males from National Health and Nutrition Examination Survey (NHANES) from 2001 to 2004. RC was calculated as follows: the values of high-density lipoprotein cholesterol (HDL-c) and low-density lipoprotein cholesterol (LDL-c) were subtracted from the total cholesterol (TC) value, while ED diagnoses were based on self-reports. Weighted logistic regression analyses using both univariate and multivariate approaches were conducted to assess the correlation between RC and ED. Results After comprehensive adjustment, multivariable logistic regression models revealed a strong correlation between RC and ED in subjects with diabetes (with an odds ratio (OR) of 7.49 and a 95% confidence interval (CI) of 1.98–28.37; P = 0.004). On categorizing RC into 3 grades (T1-T3), the OR corresponding to higher RC grade increased. Despite the results not reaching statistical significance upon categorization, a consistent and statistically significant trend (P for trend

Abstract Purpose Prostate cancer (PCa) is one of the major tumor diseases that threaten men’s health globally, and biochemical recurrence significantly impacts its prognosis. Disulfidptosis, a recently discovered cell death mechanism triggered by intracellular disulfide accumulation leading to membrane rupture, is a new area of research in the context of PCa. Currently, its impact on PCa remains largely unexplored. This study aims to investigate the correlation between long non-coding RNAs (lncRNAs) associated with disulfidptosis and the prognosis of PCa, seeking potential connections between the two. Methods Transcriptomic data for a PCa cohort were obtained from the Cancer Genome Atlas database. Disulfidptosis-related lncRNAs (DDRLs) were identified through differential expression and Pearson correlation analysis. DDRLs associated with biochemical recurrence-free survival (BRFS) were precisely identified using univariate Cox and LASSO regression, resulting in the development of a risk score model. Clinical factors linked to BRFS were determined through both univariate and multivariate Cox analyses. A prognostic nomogram combined the risk score with key clinical variables. Model performance was assessed using Receiver Operating Characteristic (ROC) curves, Decision Curve Analysis (DCA), and calibration curves. The functional impact of a critical DDRL was substantiated through assays involving CCK8, invasion, migration, and cell cloning. Additionally, immunohistochemical (IHC) staining for the disulfidptosis-related protein SLC7A11 was conducted. Results The prognostic signature included AC026401.3, SNHG4, SNHG25, and U73166.1 as key components. The derived risk score from these signatures stood as one of the independent prognostic factor for PCa patients, correlating with poorer BRFS in the high-risk group. By combining the risk score with clinical variables, a practical nomogram was created, accurately predicting BRFS of PCa patients. Notably, silencing AC026401.3 significantly hindered PCa cell proliferation, invasion, migration, and colony formation. IHC staining revealed elevated expression of the dithiosulfatide-related protein SLC7A11 in tumor tissue. Conclusions A novel prognostic signature for PCa DDRLs, possessing commendable predictive power, has been constructed, simultaneously providing potential therapeutic targets associated with disulfidptosis, among which AC026401.3 has been validated in vitro and demonstrated inhibition of PCa tumorigenesis after its silencing.

The Japanese garden Saihō-ji is used as a case study to evaluate the spatial-visual characteristics of sequential landscapes using eye-tracking technology. We present objective data and quantitative analysis of subjective behavior, addressing the lack of research in landscape perception. In this study, we combine virtual reality and eye-tracking technologies to collect statistical data for an entire garden through static stimulus images. The data produced are objective and enable the direct assessment of landscape elements and objects. The correlation between spatial compositions indicators and the eye-tracking data reveals that different spatial compositions have varying effects on distinct gaze elements. While this research provides a preliminary validation, it still has certain limitations. Factors such as visual fatigue and demographic differences should be considered in future studies. Further, our findings emphasize the viability of eye-tracking technology for investigating the spatial visual characteristics of sequential landscapes, prompting further research in this field.

Abstract Background and Purpose To identify anatomic prognostic factors and their potential roles in refining M1 classification for de novo metastatic nasopharyngeal carcinoma (M1‐NPC). Materials and Methods All M1‐NPC treated with chemotherapy and/or radiotherapy between 2010 and 2019 from two centers (training and validation cohort) were included. The prognostic value of metastatic disease extent and involved organs for overall survival (OS) were assessed by several multivariable analyses (MVA) models. A new M1 classification was proposed and validated in a separate cohort who received immuno‐chemotherapy. Results A total of 197 M1‐NPC in the training and 307 in the validation cohorts were included for M1 subdivision study with median follow‐up of 46 and 57 months. MVA model with “≤2 organs/≤5 lesions” as the definition of oligometastasis had the highest C‐index (0.623) versus others (0.606–0.621). Patients with oligometastasis had better OS versus polymetastasis (hazard ratio [HR] 0.47/0.63) while liver metastases carried worse OS (HR 1.57/1.45) in MVA in the training/validation cohorts, respectively. We proposed to divide M1‐NPC into M1a (oligometastasis without liver metastases) and M1b (liver metastases or polymetastasis) with 3‐year OS of 66.5%/31.7% and 64.9%/35.0% in the training/validation cohorts, respectively. M1a subset had a better median progress‐free survival (not reach vs. 17 months, p

AbstractIn this work, Cu (II) ion was chosen as an analyte because it is an important trace element in the human body to maintain organism health. AuNCs (gold nanoclusters) with a mean diameter of ∼2 nm were prepared by a microwave-assisted method, using homocysteine (Hcy) as a reducer and stabilizer. The optimal sample showed a red emission peaking at 624 nm with an emission quantum yield of 17.1%, which was assigned as a ligand to metal nanoparticle core charge transfer (LMNCT). This red emission was effectively quenched by Cu (II) but immune to competing metal cations and anions, showing good selectivity. The quenching mechanism was found as the Cu (II) quenching on the LMNCT procedure to AuNCs. The primary advantage of Hcy-modified AuNCs was the easy and quick synthetic procedure, along with acceptable sensing performance, including good selectivity, linear response, and wide working region.

ABSTRACTAs a type of environmentally deleterious compound, 4-nitrophenol (4-NP) has become a global concern due to its extreme toxicity to aquatic organisms and humans. Herein, we used naturally available crayfish shells as precursors to prepare a new type of carbon dots (CDs) by the hydrothermal method. The as-synthesized CDs exhibited strong blue fluorescence emission which can be sensitively quenched by 4-NP through the static quenching effect because of the formation of a ground state complex between CDs and 4-NP. Accordingly, a facile and rapid fluorescence sensor was developed to detect 4-NP from 0 μM to 50 μM with a limit of detection (LOD) of 0.16 μM. Furthermore, the proposed sensor was successfully applied for the detection of 4-NP in fresh crayfish meat and aquatic water samples.

Based on GNSS observations, the co-seismic and post-seismic slip of the 2024 Noto Peninsula earthquake and the spatio-temporal pattern of afterslip are investigated in this paper. The co-seismic slip is mainly distributed in the depth range of 2 to 15 km with the maximum value of 5.94 m. Compared with the co-seismic rupture pattern, a shallow afterslip can be observed after the earthquake, and the afterslip patch is formed northeast of the epicenter. The maximum value of afterslip during the post-seismic 180 days is 1.13 m, which is situated at the longitude of 137.53°, latitude of 37.75°, and epth of 5.43 km. The spatio-temporal evolution of afterslip indicates that the fault activity has continued throughout the post-seismic 180 days, and the coverage and magnitude of afterslip have gradually increased. As time goes on, the fault activity tends to weaken, as evidenced by a decrease in slip rate. The daily images of afterslip demonstrate that the fault activity is particularly strong in the early time period following the earthquake. The maximum value of afterslip in the first week accounts for about 18% of that in the post-seismic 180 days, and the maximum slip rate reaches 0.043 m/day. In addition, the Coulomb stress analysis indicates that afterslip and most aftershocks appear in the positive Coulomb stress region, suggesting that co-seismic Coulomb stress changes may be the driving mechanism of afterslip and aftershocks.