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Global inland surface water bodies such as lakes and reservoirs, important components of the hydrosphere and ecosphere, are increasingly affected by climate change. Generating bathymetric volume-area-height (BVAH) curves for global inland surface water bodies can enhance our understanding of their topography and climate impacts. However, accurately quantifying the topographic patterns of these water bodies remains challenging due to the difficulties in collecting comprehensive bathymetric data. Therefore, we collected and processed over 2000 bathymetric maps of global water bodies from over 50 different data sources and then developed the BVAH model. Finally, the BVAH hydrological curves of 16671 global inland surface water bodies (larger than 10 km2) were generated. The results include but are not limited to (1) For most targeted water bodies, area (A) and volume (V) exhibit significant power function relationships with surface heights (H), with optimal power values quantified as 1.42 for A and 2.42 for V. (2) The BVAH model outperforms GLOBathy in estimating area and volume changes, achieving higher correlation coefficients (CC) of approximately 0.962 for the area and 0.991 for volume, and demonstrating lower percentages of root mean squared errors (PRMSE) around 10.9% for the area and 4.8% for volume. (3) In the case study of the Xizang Plateau and various large global reservoirs, the BVAH curve database can capture dynamic volume changes. As a unified simulation of the bathymetric topographical patterns, our bathymetric dataset and corresponding BVAH curve database have great potential to contribute to effective water resource management and ecological conservation efforts worldwide.

In this paper, the effects of Hf addition on the mechanical properties, electrical conductivity and microstructure of Cu-Sn-P alloy were investigated. The results show that the Hf addition can enhance the mechanical properties and electrical conductivity of Cu-Sn-P alloy. The alloy with 0.05 wt% Hf exhibits the best comprehensive performance with yield strength of 625 MPa, tensile strength of 661 MPa and electrical conductivity of 16.8 %IACS. Compared with the Cu-Sn-P alloy without Hf addition, the yield strength and tensile strength are increased by 8.4% and 7.8%, and the electrical conductivity is increased by 24.4%. The increase in the strength of Hf-added alloy can be attributed to the synergistic effect of multiple strengthening mechanisms, including fine grain strengthening, twin strengthening, dislocation strengthening, precipitation strengthening and Σ3n (n = 1, 2, 3) grain boundary strengthening. Moreover, the Hf-P precipitates can be observed in the Hf-added alloy. According to the amount of Hf addition, these precipitates can manifest as the fine HfP2 phase and the coarse HfP phase. The formation of Hf-P precipitates contributes to the precipitation strengthening and the purification of Cu matrix, thus contributing to the improvement of strength and conductivity.

Abstract Quantum teleportation is the process of transferring quantum information using classical communication and pre-shared entanglement. This process can benefit from the use of catalysts, which are ancillary entangled states that can enhance teleportation without being consumed. While chemical catalysts undergoing deactivation invariably exhibit inferior performance compared to those unaffected by deactivation, quantum catalysts, termed embezzling catalysts, that are subject to deactivation, may outperform their non-deactivating counterparts. In this work, we present teleportation protocols with embezzling catalysts that can achieve arbitrarily high fidelity. This enables the teleported state to closely approximate the original message state with arbitrary precision, while maintaining arbitrarily small variations in the catalytic system through the use of finite-dimensional embezzling catalysts. We show that some embezzling catalysts are universal, meaning that they can improve the teleportation fidelity for any pre-shared entanglement. We also explore methods to reduce the dimension of catalysts without increasing catalyst consumption, an essential step towards realizing quantum catalysis in practice.

Ammonium dinitramide (ADN) is a new type of green energetic oxidizer with excellent energy density and low pollution combustion characteristics. However, the strong hygroscopicity has a significant impact on its practical application. To assist in the research on moisture-proof modification of ADN materials, an innovative hygroscopic modeling approach was proposed to evaluate the hygroscopicity of ADN at various temperatures and humidities. By investigating the diffusion coefficient of water molecules in molecular dynamics processes, a visual insight into the hygroscopic process of ADN was gained. Furthermore, analyzing the non-covalent interactions between ADN and water molecules, the hygroscopicity of ADN could be evaluated qualitatively and quantitatively. The energy analysis revealed that electrostatic forces play a dominant role in the process of water adsorption by ADN, whereas van der Waals forces impede it. As a whole, the simulation results show that ADN presents the following hygroscopic law: At temperatures ranging from 273 K to 373 K and relative humidity (RH) from 10% to 100%, the hygroscopicity of ADN generally shows an increasing trend with the rise in temperature and humidity based on the results of three simulations. According to the non-hygroscopic point (298 K, 52% RH) of ADN obtained by experiment in the literature, a non-hygroscopic range of temperature and humidity for ADN can be depicted when the simulation results in relative hygroscopicity is less than or equal to 17%. This study can provide effective strategies for screening anti-hygroscopic modified materials of ADN.

Abstract Detecting buried active faults presents the challenge of precisely locating the upper breakpoint, the shallowest point in the Quaternary system where faults occur. Microtremor survey technology, unaffected by urban electromagnetic interference, offers an eco-friendly and efficient method for investigating buried faults and stratigraphic structures in urban areas. This research uses microtremor survey technology to identify the upper breakpoint of the buried Nankou-Sunhe Fault in Changping, Beijing. For data collection, 17 microtremor survey points were deployed across the northern section of the Nankou-Sunhe fault, employing a three-point nested circular array with a point spacing of approximately 200 m to form a profile spanning approximately 320 m. For data analysis, the spatial autocorrelation method was utilized. Each measurement point was divided into 9 sets of radii, ranging from a minimum of approximately 4 m to a maximum of 28 m. The correlation coefficients for each set were calculated, and the dispersion curve for each measurement point was generated by fitting the average coefficients with the Bessel function of the first kind of order zero. The apparent S-wave velocity was determined directly from the dispersion curve using empirical formulas and interpolated to generate the contour cross-section map. Integrating the section and inverted S-wave velocity data can significantly enhance interpretation accuracy, and based on these data, the spatial development characteristics and upper breakpoint locations of the Nankou-Sunhe fault zone were analyzed, and the strata shallower than 100 m were deduced. The results align well with known geological data, such as luminescence dating and 14C dating from boreholes at nearby locations. Graphical Abstract

Abstract Recently, a novel low‐cost coding digital receiving array based on machine learning (ML‐CDRA) has been proposed to reduce the required radio frequency channels in modern wireless systems. The spatial sensitivity of ML‐CDRA is studied which describes the spatial accumulation gain in different directions. It is demonstrated that the spatial sensitivity is determined by the encoding network, decoding network, and beamforming criterion. To obtain the desired spatial sensitivity, a spatial sensitivity synthesis method is proposed based on the alternate projection by optimising the encoding network with the constraint of amplitude‐phase quantisation. Simulation results show that the proposed method can significantly improve the spatial sensitivity of ML‐CDRA. Furthermore, in the directions of interest, the spatial accumulation gain of ML‐CDRA can exceed the full‐channel digital receiving array.

In this investigation, a Fe–20Mn–9Al-1.5C lightweight austenitic steel has been developed through strategic alloying with Ni and Cr elements coupled with the employment of a thermo-mechanical processing technique featuring ultra-fast cooling. This composition and treatment synergistically transcend the conventional trade-off between strength and ductility, positioning the mechanical performance of this steel notably above that of comparable low-density austenitic steels previously reported. The coordinated incorporation of Ni and Cr is key to modulating the precipitation threshold for nanoscale κ-carbides and LRO domains, achieving an optimal size for precipitate-induced strengthening. The incorporation of Cr specifically contributes to a dual enhancement mechanism: it fosters the generation of high-density dislocation arrays (HDDA) and refines the dynamic slip band (DSBR) during deformation, which in turn, elevates the strain hardenability and ductility. The remarkable synergy in strength and ductility is attributed to a combination of mechanisms including grain boundary reinforcement, solid solution hardening, the precipitation strengthening effect of nanoscale precipitates, and the activation of secondary slip system dislocations. This synergy is quantitatively reflected in the material's yield strength (YS) exceeding 1100 MPa, ultimate tensile strength (UTS) surpassing 1200 MPa, and a total elongation (TEL) exceeding 45%. The insights gained from this research provide critical guidance for the design and creation of austenitic low-density steels that do not compromise between strength and ductility, thereby offering superior overall performance.

Autonomous-rail Rapid Transit (ART) tram is a new type of multiple-articulated rubber-tire transit that utilizes intelligent perception, path tracking, and trajectory following control technologies to eliminate reliance on physical railway tracks. The adoption of power batteries, hydrogen energy, wheel-edge motor drive, and other technologies has comprehensively realized the dual advantages of large-capacity rail transportation, which is punctual, high volume, energy-saving, and environmentally friendly, as well as the flexibility and low comprehensive cost of traditional bus operations. This has created a brand-new urban rail transit model. This article first introduces the ART tram systems architecture, operating principles, applicable scenarios. Secondly, it introduces the core subsystems of ART tram vehicle structure, electrical system, and energy storage system. Thirdly, it focuses on analyzing the structure composition and control principles of the Automatic All-Wheel Steering System, which includes two key core subsystems: path tracking control subsystems and trajectory following control subsystems. Then, a horizontal comparison is made between the performance advantages and disadvantages of ART and other transportation systems, and the application status of ART tram is summarized. Finally, some common issues related to the development of ART tram are discussed, and a development plan for future ART systems is proposed to better integrate ART tram into urban transportation and meet people's demands for intelligent, comfortable, fast, and environmentally friendly urban public transportation.

Ellagic acid (EA) contributes to the immunity and anti-oxidant function of body, whereas there are few reports about its effect on the intestinal health and growth performance of broiler chickens. Hence, the present study was arranged to investigate the effect of dietary supplementary with EA on the intestinal barrier function and flora structure of broiler chickens challenged with Escherichia coli K88 (E. coli K88). A total of 216 healthy 1-day-old, Ross 308 broilers with uniform weight were randomly assigned into three treatment groups, six replicates in each group and twelve birds in each replicate. Broilers in the control (CTR) group and E. coli K88 infected group (ETEC) were fed with the basic diet, and 200 mg/kg EA was supplemented into the diet of the E. coli K88 infected group treated with EA (EAETEC). The animal trial had lasted for 42 days, and the outcomes showed that the ADG and ADFI during the animal trial, the jejunal villi height (VH) and the ratio of VH to crypt depth (CD) tended to be decreased with E. coli K88 treated (P< 0.05). Additionally, the level of serum diamine oxidase (DAO) and intestinal malondialdehyde (MDA) were elevated, the activity of intestinal total superoxide dismutase (T-SOD) and glutathione peroxidase (GSH-Px), the mRNA levels in jejunal claudin-1 and occludin were down-regulated with E. coli K88 treated as well as the transcription levels of ileal Mucin-2, aquaporin-3 (AQP-3) and Na+/H+ exchanger proteins-3 (NHE-3) (P< 0.05). In addition, E. coli K88 down-regulated the α-diversity index of cecal flora, the ratio of Bacteroidota to Firmicutes and the relative abundance of Barnesiella were up-regulated and it of Alistipes was descended with E. coli K88 treated (P< 0.05). Beyond that, the content of propionic acid in the cecal chyme was decreased and the amino acid metabolic pathways were inhibited with E. coli K88 challenged (P< 0.05). Additionally, there was a significant positive correlation between the relative abundance of Alistipes and the levels of butyric acid in the caecal chyme and the activity of GSH-Px in the intestine (P< 0.05). Interestingly, dietary supplementary with EA could reshape the intestinal flora structure and alleviate the above negative effects of E. coli K88 on broiler chickens. In conclusion, dietary supplementary with ellagic acid improved the intestinal barrier function and flora structure of broiler chickens challenged with E. coli K88.

Abstract It is still a huge challenge to introduce effective crack‐healing ability into energetic composites with a high oxidizer content. In this article, a poly(urea‐urethane) energetic elastomer was prepared by the polycondensation reaction of glycidyl azido polymer (GAP), isophorone diisocyanate (IPDI), and 2‐aminophenyl disulfide (2‐APD). In the poly(urea‐urethane) elastomer structure, the hybrid dynamic lock, including multilevel H‐bonds and disulfide bonds, not only provides abundant dynamic interactions and promotes chain diffusion, but also enhances physical crosslinking density. Such a unique design fabricated the energetic elastomer with robust tensile strength (0.72 MPa), high stretchability (1631%), and outstanding toughness (8.95 MJ/m3) in the field of energetic polymers. Meanwhile, this energetic elastomer exhibited high self‐healing efficiency (98.4% at 60 °C) and heat release (Q = 1750.46 J/g). Experimental and theoretical results adequately explain the self‐healing mechanism, particularly the role of azido units. The high‐solid content (80 wt%) energetic composites based on the energetic elastomer presented outstanding micro‐defect self‐healing (97.8%) and recycling without loss of mechanical performance. The development of smart energetic composites with excellent self‐healing and recyclable ability provides a meaningful way for a wide range of applications in the field of energetic materials.

Abstract Thermosensation is vital for the survival, propagation, and adaption of all organisms, but its mechanism is not fully understood yet. Here, we find that TMC6, a membrane protein of unknown function, is highly expressed in dorsal root ganglion (DRG) neurons and functions as a Gαq-coupled G protein-coupled receptor (GPCR)-like receptor to sense noxious heat. TMC6-deficient mice display a substantial impairment in noxious heat sensation while maintaining normal perception of cold, warmth, touch, and mechanical pain. Further studies show that TMC6 interacts with Gαq via its intracellular C-terminal region spanning Ser780 to Pro810. Specifically disrupting such interaction using polypeptide in DRG neurons, genetically ablating Gαq, or pharmacologically blocking Gαq-coupled GPCR signaling can replicate the phenotype of TMC6 deficient mice regarding noxious heat sensation. Noxious heat stimulation triggers intracellular calcium release from the endoplasmic reticulum (ER) of TMC6- but not control vector-transfected HEK293T cell, which can be significantly inhibited by blocking PLC or IP3R. Consistently, noxious heat-induced intracellular Ca2+ release from ER and action potentials of DRG neurons largely reduced when ablating TMC6 or blocking Gαq/PLC/IP3R signaling pathway as well. In summary, our findings indicate that TMC6 can directly function as a Gαq-coupled GPCR-like receptor sensing noxious heat.

Abstract Smartphone sensors have gained considerable traction in Human Activity Recognition (HAR), drawing attention for their diverse applications. Accelerometer data monitoring holds promise in understanding students’ physical activities, fostering healthier lifestyles. This technology tracks exercise routines, sedentary behavior, and overall fitness levels, potentially encouraging better habits, preempting health issues, and bolstering students’ well-being. Traditionally, HAR involved analyzing signals linked to physical activities using handcrafted features. However, recent years have witnessed the integration of deep learning into HAR tasks, leveraging digital physiological signals from smartwatches and learning features automatically from raw sensory data. The Long Short-Term Memory (LSTM) network stands out as a potent algorithm for analyzing physiological signals, promising improved accuracy and scalability in automated signal analysis. In this article, we propose a feature analysis framework for recognizing student activity and monitoring health based on smartphone accelerometer data through an edge computing platform. Our objective is to boost HAR performance by accounting for the dynamic nature of human behavior. Nonetheless, the current LSTM network’s presetting of hidden units and initial learning rate relies on prior knowledge, potentially leading to suboptimal states. To counter this, we employ Bidirectional LSTM (BiLSTM), enhancing sequence processing models. Furthermore, Bayesian optimization aids in fine-tuning the BiLSTM model architecture. Through fivefold cross-validation on training and testing datasets, our model showcases a classification accuracy of 97.5% on the tested dataset. Moreover, edge computing offers real-time processing, reduced latency, enhanced privacy, bandwidth efficiency, offline capabilities, energy efficiency, personalization, and scalability. Extensive experimental results validate that our proposed approach surpasses state-of-the-art methodologies in recognizing human activities and monitoring health based on smartphone accelerometer data.

Abstract Silicon interposers embedded with ultra-deep through-silicon vias (TSVs) are in great demand for the heterogeneous integration and packaging of opto-electronic chiplets and microelectromechanical systems (MEMS) devices. Considering the cost-effective and reliable manufacturing of ultra-deep TSVs, the formation of continuous barrier and seed layers remains a crucial challenge to solve. Herein, we present a novel dual catalysis-based electroless plating (ELP) technique by tailoring polyimide (PI) liner surfaces to fabricate dense combined Ni barrier/seed layers in ultra-deep TSVs. In additional to the conventional acid catalysis procedure, a prior catalytic step in an alkaline environment is proposed to hydrolyze the PI surface into a polyamide acid (PAA) interfacial layer, resulting in additional catalysts and the formation of a dense Ni layer that can function as both a barrier layer and a seed layer, particularly at the bottom of the deep TSV. TSVs with depths larger than 500 μm and no voids are successfully fabricated in this study. The fabrication process involves low costs and temperatures. For a fabricated 530-μm-deep TSV with a diameter of 70 μm, the measured depletion capacitance and leakage current are approximately 1.3 pF and 1.7 pA at 20 V, respectively, indicating good electrical properties. The proposed fabrication strategy can provide a cost-effective and feasible solution to the challenge of manufacturing ultra-deep TSVs for modern 3D heterogeneous integration and packaging applications.

Objective To achieve carbon peak and carbon neutrality goals, carbon dioxide flooding and storage are the main technical means for carbon emission reduction. It is crucial to clarify the main controlling factors of carbon dioxide flooding and storage under reservoir conditions, which provides the basis for realizing the efficient development of carbon dioxide flooding and storage. Methods In this study, with the widely used PUNQ-S3 case study as the basis, an integrated numerical simulation model of carbon dioxide flooding and geological storage is constructed. It considers the miscible interaction between carbon dioxide and crude oil as well as storage mechanisms, including structural, residual, dissolved, and mineral trapping. By employing the random forest intelligent algorithm, a feature importance analysis of reservoir and production parameters during the carbon dioxide flooding and storage process is carried out. The differences between carbon dioxide flooding and storage at different time scales are considered. A time series-based feature importance analysis method is established, and the main controlling factors in the different carbon dioxide flooding and storage stages are analysed. Through the fluctuation of the evaluation index, the influence of reservoir and production parameters on different stages of carbon dioxide flooding and storage is inferred. Results The results show that the time series-based random forest model for carbon dioxide flooding and storage has high accuracy. In the early stage of carbon dioxide flooding and storage, the amount of carbon dioxide structural storage is controlled by the reservoir water saturation, and the amount of dissolved storage is controlled by the salinity of the formation brine; In the middle and later stages of carbon dioxide flooding and storage, the amount of carbon dioxide structural storage is controlled by reservoir permeability, while the amount of dissolved storage is controlled by reservoir permeability and formation water salinity; The residual storage capacity is small in the early stage of carbon dioxide flooding and storage, resulting in unclear main controlling factors.In the later stage of carbon dioxide flooding and storage, it is controlled by reservoir permeability and water saturation; The amount of mineralization storage is controlled by the pH value and the salinity of the formation brine throughout the entire CO2 flooding and storage stage; The amount of carbon dioxide production is controlled by reservoir permeability and water saturation throughout the entire carbon dioxide flooding and storage stage. Conclusion The time-series-based random forest algorithm can identify the main controlling factors of different carbon dioxide flooding and storage stages and can provide support for cimproving crude oil recovery and implementing efficient geological storage with carbon dioxide.

Abstract The effective field theory approach parameterizes the low energy behaviors of all possible ultraviolet (UV) theories in a systematic way. One of the most important tasks is thus to find the connection between the effective operators and their UV origins. The redundancy relations among operators make the connection very subtle, hence we proposed the J-basis prescription to illuminate the correspondence between operators and their UV resonances in the bottom-up way. In this work, we work out the dimension-8 J-basis operators in the standard model effective field theory (SMEFT), and find all the 146 (82) tree-level UV resonances along with their couplings up to mass dimension 5 (4). Furthermore, we point out a few subtleties on operator generation via field redefinition and on the UV Lagrangian for generic spin resonances. We also provide a data base storing our results and a Mathematica notebook for extracting those results for the reader’s conveinence.

Abstract The dynamic behavior of a physical system often originates from its spectral properties. In open systems, where the effective non-Hermitian description enables a wealth of spectral structures in the complex plane, the concomitant dynamics are significantly enriched, whereas the identification and comprehension of the underlying connections are challenging. Here we experimentally demonstrate the correspondence between the transient self-acceleration of local excitations and the non-Hermitian spectral topology using lossy photonic quantum walks. Focusing first on one-dimensional quantum walks, we show that the measured short-time acceleration of the wave function is proportional to the area enclosed by the eigenspectrum. We then reveal a similar correspondence in two-dimension quantum walks, where the self-acceleration is proportional to the volume enclosed by the eigenspectrum in the complex parameter space. In both dimensions, the transient self-acceleration crosses over to a long-time behavior dominated by a constant flow at the drift velocity. Our results unveil the universal correspondence between spectral topology and transient dynamics, and offer a sensitive probe for phenomena in non-Hermitian systems that originate from spectral geometry.

To achieve high thermal efficiency, today's nuclear reactor structures are exposed to higher temperatures and pressures, which requires the use of high-strength steels with specific properties, such as ODS steels. There is a need to clarify the evolution of the microstructure and properties of steels at elevated temperatures. This study systematically investigates the evolution of microstructure, texture, and mechanical property variations of 12Cr-ODS steels after hot-rolling and subsequent annealing at 1000 °C and 1200 °C. The investigation utilizes optical microscopy, electron backscatter diffraction technique, and mechanical property measurements. The microstructure of hot-rolled samples shows a layered alternating distribution, which is distinguished by a notable presence of the α-fiber texture. Following annealing at 1000 °C, the martensite grains are smaller with a reduced hardness, while maintaining a strong α-fiber texture. After annealing at 1200 °C, there is a rapid increase in the growth of martensite grains, a significant rise in hardness, a reduction in the α-fiber texture characteristics, and an improvement in the γ-fiber texture characteristics. Moreover, the maximum intensity of the α-fiber texture diminishes as the annealing temperature increases. The mechanical properties of the samples deteriorated after annealing at 1200 °C, which can be attributed to the coarse martensite grains and the texture components containing the {001} cleavage plane dominating the occurrence of brittle cleavage fracture. The 0.1Y sample after annealing at 1000 °C exhibits an excellent combination of strength (1458 MPa) and ductility (20.3%), which is owing to the unique heterogeneous grain structure and the evolution of favorable texture.

Abstract The most pronounced neuropathological feature of Parkinson's disease (PD) is the loss of dopamine (DA) neurons in the substantia nigra compacta (SNc), which depletes striatal DA. Hypothalamic oxytocin is found to be reduced in PD patients and closely interacts with the DA system, but the role of oxytocin in PD remains unclear. Here, the disturbances of endogenous oxytocin level and the substantia nigra (SN) oxytocin receptor expression in the 1‐methyl‐4‐phenyl‐1,2,3,6‐tetrahydropyridine (MPTP)‐induced PD mouse model is observed, correlated with the striatal tyrosine hydroxylase (TH) expression reduction. Killing/silencing hypothalamic oxytocin neurons aggravates the vulnerability of nigrostriatal DA signal to MPTP, whereas elevating oxytocin level by intranasal delivery or microinjecting into the SN promotes the resistance. In addition, knocking out SN oxytocin receptors induces the time‐dependent reductions of SNc DA neurons, striatal TH expression, and striatal DA level by increasing neuronal excitotoxicity. These results further uncover that oxytocin dampens the excitatory synaptic inputs onto DA neurons via activating oxytocin receptor‐expressed SN GABA neurons, which target GABA(B) receptors expressed in SNc DA neuron‐projecting glutamatergic axons, to reduce excitotoxicity. Thus, besides the well‐known prosocial effect, oxytocin acts as a key endogenous factor in protecting the nigrostriatal DA system.

In this work, the relationship and kinetics of biodegradation and bio-adsorption of benzo[a]pyrene (BaP) by Bacillus and Ascomycota were explored, and the metabolites of BaP under mixed microbial coculture were analyzed and characterized. The results show that BaP was removed through both biosorption and biodegradation. Under mixed microbial coculture, biosorption played a significant role in the early stage and biodegradation was predominant in the later stage. During the removal of BaP, the fungi exhibited remarkable adsorption capabilities for BaP with an adsorption efficiency (AE) of 38.14 %, while bacteria had a best degradation for BaP with a degradation efficiency (DE) of 56.13 %. Under the mixed microbial culture, the removal efficiency (RE) of BaP by the synergistic action of fungi and bacteria reached up to 76.12 % within 15 days. Kinetics analysis illustrated that the degradation and adsorption process of BaP were well fit to the first-order and the pseudo-second-order kinetic models, respectively. The research on the relationship between degradation and adsorption during microbial removal of BaP, as well as the synergistic effects of fungi and bacteria, will provide a theoretical guidance for two or even synthetic microbial communities.

BackgroundAtherosclerotic plaque rupture is a major cause of heart attack. Previous studies have shown that immune cells are involved in the development of atherosclerosis, but different immune cells play different roles. The aim of this study was to investigate the causal relationship between immunological traits and myocardial infarction (MI).MethodsTo assess the causal association of immunological profiles with myocardial infarction based on publicly available genome-wide studies, we used a two-sample mendelian randomization (MR) approach with inverse variance weighted (IVW) as the main analytical method. Sensitivity analyses were used to assess heterogeneity and horizontal pleiotropy.ResultsA two-sample MR analysis was conducted using IVW as the primary method. At a significance level of 0.001, we identified 47 immunophenotypes that have a significant causal relationship with MI. Seven of these were present in B cells, five in cDC, four in T cells at the maturation stage, six in monocytes, five in myeloid cells, 12 in TBNK cells, and eight in Treg cells. Sensitivity analyses were performed to confirm the robustness of the MR results.ConclusionsOur results provide strong evidence that multiple immune cells have a causal effect on the risk of myocardial infarction. This discovery provides a new avenue for the development of therapeutic treatments for myocardial infarction and a new target for drug development.