网络新媒体视阈下客家议题在日传播特征分析——基于日本Google News的考察

以日本Google News中的客家相关日文报道为语料,通过文本挖掘工具KH Coder进行文本数据挖掘,发现日本网络新闻媒体的客家议题日文报道具有分布零散性及以图片报道为主的特点。报道关注的客家区域主要涉及台湾地区、福建、香港地区、广东等地,其中,台湾地区的客家关注度最高。报道聚焦客家的观光旅游、产业振兴、客家书籍出版等相关内容,形成了客家传统建筑类、台湾地区客家文化交流类、客家文学作品推介类、客家观光旅游类等四大主要议题。本文提出要充分运用Google News等网络新媒体强大的技术功能和传播优势加大客家文化的传播力度。

Multimodal Social Media Fake News Detection Based on Similarity Inference and Adversarial Networks

As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.

Clinical manifestation,lifestyle,and treatment patterns of chronic erosive gastritis:A multicenter real-world study in China

BACKGROUND Although chronic erosive gastritis(CEG)is common,its clinical characteristics have not been fully elucidated.The lack of consensus regarding its treatment has resulted in varied treatment regimens.AIM To explore the clinical characteristics,treatment patterns,and short-term outcomes in CEG patients in China.METHODS We recruited patients with chronic non-atrophic or mild-to-moderate atrophic gastritis with erosion based on endoscopy and pathology.Patients and treating physicians completed a questionnaire regarding history,endoscopic findings,and treatment plans as well as a follow-up questionnaire to investigate changes in symptoms after 4 wk of treatment.RESULTS Three thousand five hundred sixty-three patients from 42 centers across 24 cities in China were included.Epigastric pain(68.0%),abdominal distension(62.6%),and postprandial fullness(47.5%)were the most common presenting symptoms.Gastritis was classified as chronic non-atrophic in 69.9%of patients.Among those with erosive lesions,72.1%of patients had lesions in the antrum,51.0%had multiple lesions,and 67.3%had superficial flat lesions.In patients with epigastric pain,the combination of a mucosal protective agent(MPA)and proton pump inhibitor was more effective.For those with postprandial fullness,acid regurgitation,early satiety,or nausea,a MPA appeared more promising.CONCLUSION CEG is a multifactorial disease which is common in Asian patients and has non-specific symptoms.Gastroscopy may play a major role in its detection and diagnosis.Treatment should be individualized based on symptom profile.

Venous Doppler flow patterns,venous congestion,heart disease and renal dysfunction:A complex liaison

The World Journal of Cardiology published an article written by Kuwahara et al that we take the pleasure to comment on.We focused our attention on venous congestion.In intensive care settings,it is now widely accepted that venous congestion is an important clinical feature worthy of investigation.Evaluating venous Doppler profile abnormalities at multiple sites could suggest adequate treatment and monitor its efficacy.Renal dysfunction could trigger or worsen fluid overload in heart disease,and cardio-renal syndrome is a well-characterized spectrum of disorders describing the complex interactions between heart and kidney diseases.Fluid overload and venous congestion,including renal venous hypertension,are major determinants of acute and chronic renal dysfunction arising in heart disease.Organ congestion from venous hypertension could be involved in the development of organ injury in several clinical situations,such as critical diseases,congestive heart failure,and chronic kidney disease.Ultrasonography and abnormal Doppler flow patterns diagnose clinically significant systemic venous congestion.Cardiologists and nephrologists might use this valuable,noninvasive,bedside diagnostic tool to establish fluid status and guide clinical choices.

Improving Diversity with Multi-Loss Adversarial Training in Personalized News Recommendation

Users’interests are often diverse and multi-grained,with their underlying intents even more so.Effectively captur-ing users’interests and uncovering the relationships between diverse interests are key to news recommendation.Meanwhile,diversity is an important metric for evaluating news recommendation algorithms,as users tend to reject excessive homogeneous information in their recommendation lists.However,recommendation models themselves lack diversity awareness,making it challenging to achieve a good balance between the accuracy and diversity of news recommendations.In this paper,we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity.Unlike most existing works that solely optimize accuracy or employ more features to meet diversity,the proposed algorithm leverages the diversity-aware capability of the model.First,we introduce an augmented user model to fully capture user intent and the behavioral guidance they might undergo as a result.Specifically,we focus on the relationship between the original clicked news and the augmented clicked news.Moreover,we propose an effective adversarial training method for diversity(AT4D),which is a pluggable component that can enhance both the accuracy and diversity of news recommendation results.Extensive experiments on real-world datasets confirm the efficacy of the proposed algorithm in improving both the accuracy and diversity of news recommendations.

Tuning the diffusion constant to optimize the readout of positional information of spatial concentration patterns

Positional information encoded in spatial concentration patterns is crucial for the development of multicellular organisms.However,it is still unclear how such information is affected by the physically dissipative diffusion process.Here we study one-dimensional patterning systems with analytical derivation and numerical simulations.We find that the diffusion constant of the patterning molecules exhibits a nonmonotonic effect on the readout of the positional information from the concentration patterns.Specifically,there exists an optimal diffusion constant that maximizes the positional information.Moreover,we find that the energy dissipation due to the physical diffusion imposes a fundamental upper limit on the positional information.

Temporal and spatial evolution of global major grain trade patterns

The complex and volatile international landscape has significantly impacted global grain supply security. This study uses a complex network analysis model to examine the evolution and trends of the global major grain trade from 1990 to 2020, focusing on network topology, centrality ranking, and community structure. There are three major findings. First, the global major grain trade network has expanded in scale, with a growing emphasis on diversification and balance. During the study period, the United States, Canada, China, and Brazil were the core nodes of the network. Grain-exporting countries were mainly situated in Asia, the Americas, and Europe, and importing countries in Asia, Africa, and Europe. Second, a significant increase in the number of high centrality countries with high export capacity occurred, benefiting from natural advantages such as fertile land and favorable climates. Third, the main global grain trade network is divided into four communities, with the Americas-Europe community being the largest and most widespread. The formation of the community pattern was influenced by geographic proximity, driven by the core exporting countries. Therefore, the world needs to enhance the existing trade model, promote the multi-polarization of the grain trade network, and establish a global vision for the future community. Countries and regions should participate actively in global grain trade security governance and institutional reform, expand trade links with other countries, and optimize import and export policies to reduce trade risks.

LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.

A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.

Fake News Detection Based on Cross-Modal Message Aggregation and Gated Fusion Network

Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion and daily life.Compared to pure text content,multmodal content significantly increases the visibility and share ability of posts.This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection.To effectively address the critical challenge of accurately detecting fake news on social media,this paper proposes a fake news detection model based on crossmodal message aggregation and a gated fusion network(MAGF).MAGF first uses BERT to extract cumulative textual feature representations and word-level features,applies Faster Region-based ConvolutionalNeuralNetwork(Faster R-CNN)to obtain image objects,and leverages ResNet-50 and Visual Geometry Group-19(VGG-19)to obtain image region features and global features.The image region features and word-level text features are then projected into a low-dimensional space to calculate a text-image affinity matrix for cross-modal message aggregation.The gated fusion network combines text and image region features to obtain adaptively aggregated features.The interaction matrix is derived through an attention mechanism and further integrated with global image features using a co-attention mechanism to producemultimodal representations.Finally,these fused features are fed into a classifier for news categorization.Experiments were conducted on two public datasets,Twitter and Weibo.Results show that the proposed model achieves accuracy rates of 91.8%and 88.7%on the two datasets,respectively,significantly outperforming traditional unimodal and existing multimodal models.

Memristors-coupled neuron models with multiple firing patterns and homogeneous and heterogeneous multistability

Memristors are extensively used to estimate the external electromagnetic stimulation and synapses for neurons.In this paper,two distinct scenarios,i.e.,an ideal memristor serves as external electromagnetic stimulation and a locally active memristor serves as a synapse,are formulated to investigate the impact of a memristor on a two-dimensional Hindmarsh-Rose neuron model.Numerical simulations show that the neuronal models in different scenarios have multiple burst firing patterns.The introduction of the memristor makes the neuronal model exhibit complex dynamical behaviors.Finally,the simulation circuit and DSP hardware implementation results validate the physical mechanism,as well as the reliability of the biological neuron model.

Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.

Intravitreal injection practice patterns among Chinese ophthalmologists

AIM:To describe the practice patterns of intravitreal injections(IVIs)among ophthalmologists in China.METHODS:This was a cross-sectional online survey.Ophthalmologists who had performed accumulated more than 100 injections were contacted by the Brightness Center,a hospital-based national network,to complete an anonymous,24-question,internet-based survey.They were surveyed on practices in injection techniques,pre-,and post-injections procedures.RESULTS:A total of 333 ophthalmologists from 28 provinces/municipalities/autonomous regions responded to the survey(50.68%response rate).The 91.29%of the respondents evaluated systemic risk factors by medical history,electrocardiogram(ECG)and blood test.All the respondents used pre-injection prophylactic antibiotics.Most checked intraocular pressure(IOP,99.1%)and blood pressure(96.1%)before injections.A majority of the respondents performed injections in the operating room(98.8%),wore masks(99.7%),gloves(99.4%)and sterile surgical clothing(96.1%),performed topical anesthetics(97.9%),and applied povidone-iodine(95.8%)pre-injection.The 61.26%of the respondents dilated pupil.About half of the respondents(51.05%)performed bilateral injections in the same setting.Superior temporal quadrant(40.54%)was the most frequent site of injection.Around three quarters used 30-gauge needles.Most respondents(97.9%)measured the site of injection from limbus.More than half(53.45%)performed conjunctiva displacement prior to injection.The 32.43%of the respondents checked IOP post-injection and 87.99%physicians checked hand motion(HM)or counting fingers(CF)after injection,while 36.94%observed optic nerve perfusion.All participants used topical antibiotics post-injections.Most physicians(91.89%)reviewed patients on the following day.CONCLUSION:This study provides a description of the real-world practice patterns in IVIs in China and offers critical information regarding education and training of ophthalmologists and amendment of local society guidelines.

Plant-based dietary patterns and risk of esophageal cancer:A prospective cohort study spanning 17 years

Objective: Plant-based diets have multiple health benefits for cancers;however, little is known about the association between plant-based dietary patterns and esophageal cancer(EC).This study presents an investigation of the prospective associations among three predefined indices of plant-based dietary patterns and the risk of EC.Methods: We performed endoscopic screening for 15,709 participants aged 40-69 years from two high-risk areas of China from January 2005 to December 2009 and followed the cohort until December 31, 2022. The overall plant-based diet index(PDI), healthful plant-based diet index(h PDI), and unhealthful plant-based diet index(u PDI), were calculated using survey responses to assess dietary patterns. We applied Cox proportional hazard regression to estimate the multivariable hazard ratios(HRs) and 95% confidence intervals(95% CIs) of EC across 3plant-based diet indices and further stratified the analysis by subgroups.Results: The final study sample included 15,184 participants in the cohort. During a follow-up of 219,365person-years, 176 patients with EC were identified. When the highest quartile was compared with the lowest quartile, the pooled multivariable-adjusted HR of EC was 0.50(95% CI, 0.32-0.77) for h PDI. In addition, the HR per 10-point increase in the h PDI score was 0.42(95% CI, 0.27-0.66) for ECs. Conversely, u PDI was positively associated with the risk of EC, and the HR was 1.80(95% CI, 1.16-2.82). The HR per 10-point increase in the u PDI score was 1.90(95% CI, 1.26-2.88) for ECs. The associations between these scores and the risk of EC were consistent in most subgroups. These results remained robust in sensitivity analyses.Conclusions: A healthy plant-based dietary pattern was associated with a reduced risk of EC. Emphasizing the healthiness and quality of plant-based diets may be important for preventing the development of EC.

Elevational and temporal patterns of pollination success in distylous and homostylous buckwheats(Fagopyrum)in the Hengduan Mountains

Reproductive strategies of sexually dimorphic plants vary in response to the environment.Here,we ask whether the sexual systems of Fagopyrum species(i.e.,selfing homostylous and out-crossing distylous)represent distinct adaptive strategies to increase reproductive success in changing alpine environments.To answer this question,we determined how spatial and temporal factors(e.g.,elevation and peak flowering time)affect reproductive success(i.e.,stigmatic pollen load)in nine wild Fagopyrum species(seven distylous and two homostylous)among 28 populations along an elevation gradient of 1299-3315 m in the Hengduan Mountains,southwestern China.We also observed pollinators and conducted hundreds of hand pollinations to investigate inter/intra-morph compatibility,self-compatibility and pollen limitation in four Fagopyrum species(two distylous and two homostylous).We found that Fagopyrum species at higher elevation generally had bigger flowers and more stigmatic pollen loads;lateflowering individuals had smaller flowers and lower pollen deposition.Stigmatic pollen deposition was more variable in distylous species than in homostylous species.Although seed set was not pollenlimited in all species,we found that fruit set was much lower in distylous species,which rely on frequent pollinator visits,than in homostylous species capable of autonomous self-pollination.Our findings that pollination success increases at high elevations and decreases during the flowering season suggest that distylous and homostylous species have spatially and temporally distinct reproductive strategies related to environment-dependent pollinator activity.

Maternal dietary patterns associated with bone density in Chinese lactating women and infants at 6 months postpartum:a prospective study using data from 2018-2019

This cohort study was designed to explore the relationship between maternal dietary patterns(DPs)and bone health in Chinese lactating mothers and infants.We recruited 150 lactating women at 1-month postpartum.The estimated bone mineral density(eBMD)of subjects’calcanei and the information on dietary intake were collected.After 5-month follow-up,the eBMD of mothers and their infants were measured again.Factor analysis was applied to determine maternal DPs.General linear models were used to evaluate the association between maternal DPs and maternal eBMD loss or infants’eBMD.With all potential covariates adjusted,Factor 2(high intake of whole grains,tubers,mixed beans,soybeans and soybean products,seaweeds,and nuts)showed a positive association with the changes of maternal eBMD(β=0.16,95%CI:0.005,0.310).Factor 3(high intake of soft drinks,fried foods,and puffed foods)was inversely correlated with the changes of maternal eBMD(β=-0.22,95%CI:-0.44,0.00).The changes of maternal eBMD were positively associated with 6-month infants’eBMD(β=0.34,95%CI:0.017,0.652).In conclusion,Factor 2 might contribute to the maintenance of eBMD in lactating women,while Factor 3 could exacerbate maternal eBMD loss.Additionally,the changes of maternal eBMD presented a positive correlation with 6-month infants’eBMD.

Three-dimensional numerical analysis of plant-soil hydraulic interactions on pore water pressure of vegetated slope under different rainfall patterns

Understanding the pore water pressure distribution in unsaturated soil is crucial in predicting shallow landslides triggered by rainfall,mainly when dealing with different temporal patterns of rainfall intensity.However,the hydrological response of vegetated slopes,especially three-dimensional(3D)slopes covered with shrubs,under different rainfall patterns remains unclear and requires further investigation.To address this issue,this study adopts a novel 3D numerical model for simulating hydraulic interactions between the root system of the shrub and the surrounding soil.Three series of numerical parametric studies are conducted to investigate the influences of slope inclination,rainfall pattern and rainfall duration.Four rainfall patterns(advanced,bimodal,delayed,and uniform)and two rainfall durations(4-h intense and 168-h mild rainfall)are considered to study the hydrological response of the slope.The computed results show that 17%higher transpiration-induced suction is found for a steeper slope,which remains even after a short,intense rainfall with a 100-year return period.The extreme rainfalls with advanced(PA),bimodal(PB)and uniform(PU)rainfall patterns need to be considered for the short rainfall duration(4 h),while the delayed(PD)and uniform(PU)rainfall patterns are highly recommended for long rainfall durations(168 h).The presence of plants can improve slope stability markedly under extreme rainfall with a short duration(4 h).For the long duration(168 h),the benefit of the plant in preserving pore-water pressure(PWP)and slope stability may not be sufficient.

From furnace up to freezer:Elevational patterns of plant diversity in Mount Palvar,a semi-arid Irano-Turanian mountain range of southwest Asia

Much of the world's biodiversity lies in heterogeneous mountain areas with their diverse environments.As an example,Iranian montane ranges are highly diverse,particularly in the Irano-Turanian phytogeographical region.Understanding plant diversity patterns with increasing elevation is of high significance,not least for conservation planning.We studied the pattern of species richness,Shannon diversity,endemic richness,endemics ratio,and richness of life forms along a 3900 m elevational transect in Mount Palvar,overlooking the Lut Desert in Southeast Iran.We also analyzed the effect of environmental variables on species turnover along the vertical gradient.A total of 120 vegetation plots(10 m×10 m)were sampled along the elevational transect containing species and environmental data.To discover plant diversity pattern along the elevational gradient,generalized additive model(GAM)was used.Non-metric multidimensional scaling(NMDS)was applied for illustrating the correlation between species composition and environmental variables.We found hump-shaped pattern for species richness,Shannon diversity,endemic richness,and species richness of different life forms,but a monotonic increasing pattern for ratio of endemic species from low to high elevations.Our study confirms the humped pattern of species richness peaking at intermediate elevations along a complete elevational gradient in a semi-arid mountain.The monotonic increase of endemics ratio with elevation in our area as a case study is consistent with global increase of endemism with elevation.According to our results,temperature and precipitation are two important climatic variables that drive elevational plant diversity,particularly in seasonally dry areas.Our study suggests that effective conservation and management are needed for this low latitude mountain area along with calling for long-term monitoring for species redistribution.

Real-time generation of circular patterns in electron beam lithography

Electron beam lithography(EBL)involves the transfer of a pattern onto the surface of a substrate byfirst scanning a thin layer of organicfilm(called resist)on the surface by a tightly focused and precisely controlled electron beam(exposure)and then selectively removing the exposed or nonexposed regions of the resist in a solvent(developing).It is widely used for fabrication of integrated cir-cuits,mask manufacturing,photoelectric device processing,and otherfields.The key to drawing circular patterns by EBL is the graphics production and control.In an EBL system,an embedded processor calculates and generates the trajectory coordinates for movement of the electron beam,and outputs the corresponding voltage signal through a digital-to-analog converter(DAC)to control a deflector that changes the position of the electron beam.Through this procedure,it is possible to guarantee the accuracy and real-time con-trol of electron beam scanning deflection.Existing EBL systems mostly use the method of polygonal approximation to expose circles.A circle is divided into several polygons,and the smaller the segmentation,the higher is the precision of the splicing circle.However,owing to the need to generate and scan each polygon separately,an increase in the number of segments will lead to a decrease in the overall lithography speed.In this paper,based on Bresenham’s circle algorithm and exploiting the capabilities of afield-programmable gate array and DAC,an improved real-time circle-producing algorithm is designed for EBL.The algorithm can directly generate cir-cular graphics coordinates such as those for a single circle,solid circle,solid ring,or concentric ring,and is able to effectively realizes deflection and scanning of the electron beam for circular graphics lithography.Compared with the polygonal approximation method,the improved algorithm exhibits improved precision and speed.At the same time,the point generation strategy is optimized to solve the blank pixel and pseudo-pixel problems that arise with Bresenham’s circle algorithm.A complete electron beam deflection system is established to carry out lithography experiments,the results of which show that the error between the exposure results and the preset pat-terns is at the nanometer level,indicating that the improved algorithm meets the requirements for real-time control and high precision of EBL.

Acute effect of foot strike patterns on in vivo tibiotalar and subtalar joint kinematics during barefoot running

Background:Foot kinematics,such as excessive eversion and malalignment of the hindfoot,are believed to be associated with running-related injuries.The maj ority of studies to date show that different foot strike patterns influence these specific foot and ankle kinematics.However,technical deficiencies in traditional motion capture approaches limit knowledge of in vivo joint kinematics with respect to rearfoot and forefoot strike patterns(RFS and FFS,respectively).This study uses a high-speed dual fluoroscopic imaging system(DFIS)to determine the effects of different foot strike patterns on 3D in vivo tibiotalar and subtalar joints kinematics.Methods:Fifteen healthy male recreational runners underwent foot computed tomography scanning for the construction of 3-dimensional models.A high-speed DFIS(100 Hz)was used to collect 6 degrees of freedom kinematics for participants’tibiotalar and subtalar joints when they adopted RFS and FFS in barefoot condition.Results:Compared with RFS,FFS exhibited greater internal rotation at 0%-20%of the stance phase in the tibiotalar joint.The peak internal rotation angle of the tibiotalar joint under FFS was greater than under RFS(p<0.001,Cohen’s d=0.92).RFS showed more dorsiflexion at 0%-20%of the stance phase in the tibiotalar joint than FFS.RFS also presented a larger anterior translation(p<0.001,Cohen’s d=1.28)in the subtalar joint at i nitial contact than FFS.Conclusion:Running with acute barefoot FFS increases the internal rotation of the tibiotalar joint in the early stance.The use of high-speed DFIS to quantify the movement of the tibiotalar and subtalar joint was critical to revealing the effects of RF S and FFS during running.