Limited Resolution Research Articles

Photon counting CT versus energy‐integrating CT: A comparative evaluation of advances in image resolution, noise, and dose efficiency

AbstractBackgroundPhoton counting computed tomography (PCCT) employs direct and spectrally resolved counting of individual x‐ray quanta, enhancing image quality compared to the standard energy‐integrating CT (EICT).PurposeTo evaluate the quantitative improvements in CT image quality metrics by comparing the first medical PCCT with a state‐of‐the‐art EICT.MethodsThe PCCT versus EICT noise improvement ratio R was derived from the quantum statistics of the measurement process and measured across the clinical x‐ray flux range for both systems. Detector and system modulation transfer functions (MTFs) were obtained using tilted‐slit and wire phantom measurements. Image root mean square (RMS) noise, noise power spectrum (NPS), and x‐ray patient dose were compared using a CatPhan phantom at two identical clinical target resolutions.ResultsThe measurement of the PCCT noise improvement ratio R showed an elimination of electronic noise and a 10% noise transfer advantage. The PCCT detector MTF exhibited 3x higher angular resolution limits in comparison to EICT and close to ideal sinc behavior due to the electromagnetic formation of pixels in the PCCT semiconductor detector. This translated to 3.5x enhancements in CT system MTF ratios at 10 LP/cm, reflecting a significant improvement in millimeter range CT imaging. Both the improved quantum detection and the system MTF ratio improvement contribute to the measured 3x enhancements in image NPS at 10 LP/cm for identical image target resolution. An improvement of up to 1.7x in RMS image noise was observed accordingly. For low and ultra‐low dose imaging with image filtering, dose efficiency increased between 2x and 10x, demonstrating the PCCT's capability to advance CT ultra‐low dose imaging.ConclusionThe direct counting detection in PCCT has been shown to significantly improve sinogram noise and detector MTF ratios compared to energy integrating EICT. The observed translations into CT system MTF, image NPS, image noise, and dose ratios reflect a paradigm shift for CT image quality and dose efficiency.

Molecular Imaging of Ovarian Follicles and Tumors With Near-Infrared II Bioconjugates.

Follicular tracking is typically conducted using ultrasound technology, but its effectiveness is constrained by limited resolution. High-resolution imaging of deep tissues can be accomplished using luminescence imaging in the near-infrared II window (NIR-II, 1000-1700nm); however, the contrast agents that are used lack specificity. Here, it is reported that the FDA-approved indocyanine green (ICG)-conjugated recombinant human chorionic gonadotropin (hCG) protein can target early follicles with long-term effectiveness. A novel high-resolution NIR-II imaging approach is developed for monitoring follicular development as well as ovulation using multi-color imaging of ovarian vessels with a combination of non-overlapping downconversion nanoparticles (DCNPs). The results showed that the ability to monitor early follicles of around 50µm in diameter exceeded the spatial and temporal resolution of ultrasound or MRI without the reproductive damage associated with computed tomography radiation, and this enabled the clinical identification of the follicular reserve in patients with infertility diseases such as polycystic ovary syndrome (PCOS). In addition, NIR-II imaging clearly targeted ovarian tumors and showed micro-metastatic lesions, thus providing a new tool for monitoring tumors in vivo and guiding surgical resection.

Long-term (1951–2023) surface changes of the Belvedere Glacier observed through aerial and UAV orthophotos

Glacier retreat is a key indicator of climate change, with significant implications for geomorphological hazards and ecosystem stability. This article focuses on the surface evolution of the Belvedere Glacier from 1951 to 2023. Using high-resolution orthophotos and manual mapping, we tracked changes in the glacier’s area and shape over time. The results show three significant phases of change: the separation of the Nordend Glacier from the Belvedere Glacier (1951–1991), the partial separation of the central accumulation basin from the debris-covered tongue (2006–2015), and the separation of the Locce Nord Glacier (2018–2021). These changes, combined with a surge event from 1999 to 2002, have significantly altered the glacier’s dynamics and accelerated its retreat. Manual mapping was accurate in areas with scarce debris cover but faced challenges in debris-covered areas due to limited image resolution, snow cover, and debris characteristics. Despite these difficulties, we observed that the glacier remained stable until the late 1990s, when it began a rapid retreat. This recent retreat is consistent with rates observed in the early 20th century. The study highlights the importance of surface mapping to quantify the areal loss and to understand broader changes in glacier structure and mass flow that drive its retreat. Our results provide key data for future studies and highlight the need for continued monitoring of Alpine glaciers in the context of accelerating climate change.

Deep learning enhanced quantum holography with undetected photons

Holography is an essential technique of generating three-dimensional images. Recently, quantum holography with undetected photons (QHUP) has emerged as a groundbreaking method capable of capturing complex amplitude images. Despite its potential, the practical application of QHUP has been limited by susceptibility to phase disturbances, low interference visibility, and limited spatial resolution. Deep learning, recognized for its ability in processing complex data, holds significant promise in addressing these challenges. In this report, we present an ample advancement in QHUP achieved by harnessing the power of deep learning to extract images from single-shot holograms, resulting in vastly reduced noise and distortion, alongside a notable enhancement in spatial resolution. The proposed and demonstrated deep learning QHUP (DL-QHUP) methodology offers a transformative solution by delivering high-speed imaging, improved spatial resolution, and superior noise resilience, making it suitable for diverse applications across an array of research fields stretching from biomedical imaging to remote sensing. DL-QHUP signifies a crucial leap forward in the realm of holography, demonstrating its immense potential to revolutionize imaging capabilities and pave the way for advancements in various scientific disciplines. The integration of DL-QHUP promises to unlock new possibilities in imaging applications, transcending existing limitations and offering unparalleled performance in challenging environments.

Evidence of cortical vascular impairments in early stage of Alzheimer's transgenic mice: Optical imaging.

Alzheimer's disease (AD), a neurodegenerative disorder with progressive cognitive decline, remains clinically challenging with limited understanding of etiology and interventions. Clinical studies have reported vascular defects prior to other pathological manifestations of AD, leading to the "Vascular Hypothesis" for the disorder. However, in vivo assessments of cerebral vasculature in AD rodent models have been constrained by limited spatiotemporal resolution or field of view of conventional imaging. We herein employed two in vivo imaging technologies, Dual-Wavelength Imaging and Optical Coherence Doppler Tomography, to evaluate cerebrovascular reactivity (CVR) to vasoconstrictive cocaine and vasodilatory hypercapnia challenges and to detect resting 3D cerebral blood flow (CBF) in living transgenic AD mice at capillary resolution. Results showed that CVR to cocaine and hypercapnia was significantly attenuated in 7-10 months old AD mice vs controls, indicating reduced vascular flexibility and reactivity. Additionally, in the AD mice, arterial CBF velocities were slower and the microvascular density in cortex was decreased compared to controls. These results reveal significant vascular impairments including reduced CVR and resting CBF in early-staged AD mice. Hence, this cutting-edge in vivo optical imaging offers an innovative venue for detecting early neurovascular dysfunction in AD brain with translational potential.

Transcending Resolution Limits in HPLC and Diffusion NMR.

Mixture analysis is crucial in many areas of chemistry, and a wide variety of separation methods are in use. A common method for physical separation is high-performance liquid chromatography (HPLC), but resolution is a problem: chemically similar species coelute. An alternative approach is diffusion-ordered NMR spectroscopy (DOSY), in which the signals of mixture components are separated according to the diffusion coefficient. Again, separation is limited if species diffuse similarly or have overlap in their NMR spectra. Using the two techniques in combination can resolve both NMR spectra and the elution profiles of individual components, even where both techniques fail when used in isolation. Recording diffusion NMR data as a function of HPLC retention time gives a three-dimensional (3D) data set that can be analyzed using multiway statistical methods. PARAFAC analysis of diffusion NMR data measured from HPLC eluate for commercial "monoacetin" (a mixture of glycerol and its mono-, di-, and triacetates) yielded fully resolved and quantitative NMR spectra and elution profiles for all four components, whereas neither HPLC nor diffusion NMR applied independently was able to resolve the components.

Improving Daily Precipitation Estimates by Merging Satellite and Reanalysis Data in Northeast China

Precipitation plays a key control in the water, energy, and carbon cycles, and it is also an important driving force for land surface modeling. This study provides an optimal least squares merging approach to merge precipitation data sets from multiple sources for an accurate daily precipitation estimate in Northeast China (NEC). Precipitation estimates from satellite-based IMERG and SM2RAIN-ASCAT, as well as reanalysis data from MERRA-2, were used in this study. The triple collocation (TC) approach was used to quantify the error uncertainties in each input data set, which are associated with the weights assigned to each data set in the merging procedure. The results revealed that IMERG provides a better consistency with the other two input data sets and thus was more relied on during the merging process. The accuracy of both SM2RAIN-ASCAT and MERRA-2 showed obvious spatio-temporal patterns due to their retrieval algorithms and resolution limits. The merged TC-based daily precipitation provides the highest correlation coefficient with ground-based measurements (R = 0.52), suggesting its capability to represent the temporal variation in daily precipitation. However, it largely overestimated the precipitation intensity in the summer, leading to a large positive bias.

Unmixing Autoencoder for Image Reconstruction from Hyperspectral Data.

Due to the complexity of samples and the limitations in spatial resolution, the spectra in hyperspectral imaging (HSI) are generally contributed to by multiple components, making univariate analysis ineffective. Although feature extraction methods have been applied, the chemical meaning of the compressed variables is difficult to interpret, limiting their further applications. An unmixing autoencoder (UAE) was developed in this work for the separation of the mixed spectra in HSI. The proposed model is composed of an encoder and a fully connected (FC) layer. The former is used to compress the input spectrum into several variables, and the latter is employed to reconstruct the spectrum. Combining reconstruction loss and sparse regularization, the weights and the spectral profiles of the components will be encoded in the compressed variables and the connection weights of FC, respectively. A simulated and three experimental HSI data sets were adopted to investigate the performance of the UAE model. The spectral components were successfully obtained, from which the handwriting under papers was revealed from the image of near-infrared (NIR) diffusive reflectance spectroscopy, and the images of lipids, proteins, and nucleic acids were reconstructed from the Raman and stimulated Raman scattering (SRS) images.

Soil Moisture Content Prediction in Loam Soil with RFR Model

Soil moisture content (SMC) is an important factor in agricultural productivity; it has an impact on crop growth, water use efficiency, and soil health. However, accurately predicting SMC, especially at deeper soil layers, remains challenging due to high variability and limited spatiotemporal data resolution. This study developed and evaluated a Random Forest Regression (RFR) model to predict SMC in loam soil at five different depths (5, 20, 40, 60, and 80 cm) utilizing meteorological data (temperature, humidity, precipitation, wind speed, and solar radiation) and vegetation indices: the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI). Data were collected during the maize vegetation season in 2023 in Mosonmagyaróvár, Hungary. The results showed that the mean SMC ranged from 12.61% to 16.19%. Correlation analysis demonstrated that precipitation and NDMI had the strongest positive correlation with SMC, especially at shallower depths r = 0.78 at 5 cm depth, Solar radiation had a moderate correlation with SMC, especially at the deeper depths. The RFR model performed well at all depths, achieving an R² of 0.86 at 5 cm depth; the model accuracy enhanced at deeper layers, achieving R² values of 0.91 and 0.94 at 60 and 80 cm depths, respectively. The most significant predictors according to the feature importance analysis were precipitation, humidity, and NDMI, with NDMI playing a crucial role in subsurface moisture retention at deeper depths. These findings highlight the potential for machine learning algorithms to optimize irrigation approaches and improve water management in precision agriculture.

A Simulation Study of Low-Intensity Focused Ultrasound for Modulating Rotational Sense Through Acoustic Streaming in Semicircular Canal: A Pilot Study

This study explores the feasibility of using low-intensity focused ultrasound (LIFU) to induce rotational sensations in the human semicircular canal (SCC) through the acoustic streaming effect. Existing vestibular stimulation methods, such as galvanic vestibular stimulation (GVS), caloric vestibular stimulation (CVS), and magnetic vestibular stimulation (MVS), face limitations in spatial and temporal resolution, with unclear mechanisms. This study investigates whether LIFU can overcome these limitations by modulating endolymph motion within SCC. A 3D finite element model was constructed to simulate the effects of LIFU-induced acoustic streaming on SCC (particularly the endolymph), with thermal effects evaluated to ensure safety. Fluid–structure interaction (FSI) was used to analyze the relationship between endolymph flow and cupula deformation. By adjusting the focal point of the ultrasound transducer, we were able to alter fluid flow pattern, which resulted in variations in cupula displacement. The results demonstrated that LIFU successfully induces fluid motion in SCC without exceeding thermal safety limits (<1 °C), suggesting its potential for controlling rotational sensations, with cupula displacement exceeding 1 μm. This novel approach enhances the understanding of LIFU’s thermal and neuromodulatory effects on the vestibular system, and thereby offers promising implications for future therapeutic applications.

Apparent specific surface area as an indicator of the degree of cellulose microfibrillation

AbstractTracking mechanical microfibrillation in nanocellulose production is time-consuming due to a lack of quick characterization methods. This study investigates optical monitoring of the mechanical microfibrillation process by determining the dimensions of microfibrillated cellulose (MFC) particles on micron scale. Bleached hardwood pulp was microfibrillated using three sets of grinding discs in a six-stage pilot process, analyzing MFC characteristics as a function of specific energy consumption via image analysis. A laboratory-scale ultrafine grinder was also used for comparison. The degree of microfibrillation was assessed over a broad energy range using the equivalent diameter derived from the MFC length and width through image processing. The microfibrillation process adhered to Rittinger’s law, i.e., changes in the apparent specific surface area (SSA) were linearly proportional to the applied grinding energy. SSA, being inversely proportional to equivalent diameter, predicted MFC quality in terms of nanofilm strength properties. The optical fiber image analyzer proved suitable for online monitoring and control of microfibrillation processes. Despite resolution limits in detecting sub-micron particles, their proportion interrelates to the size of optically visible particles, covering industrial needs for mechanical microfibrillation.

Designing unit Ising models for logic gate simulation through integer linear programming

Ising models minimizing a quadratic objective function with spin variables of either − 1 or + 1 are instrumental in tackling combinatorial optimization problems by programmable Quantum Annealers. This paper introduces unit Ising models, where non-zero coefficients are restricted to − 1 or + 1 . Due to the limited resolution of quantum annealers, unit Ising models are more suitable for quantum annealers. A fixed unit Ising model for logic circuits could lead to Application-Specific Unit Quantum Annealers (ASUQAs) for inverse function computation, similar to ASICs. Our findings suggest a powerful new method for compromising the RSA cryptosystem by leveraging ASUQAs for factorization.

Development of a finer-resolution multi-year emission inventory for open biomass burning in Heilongjiang Province, China

Open biomass burning (OBB) is a significant source of air pollutants, profoundly impacting regional air quality and global climate change. However, due to the lack of high-resolution burned area products, limited biomass data resolution, and inappropriate emission factors (EFs), current OBB emission inventories have significant uncertainties. In this study, we integrated the FireCCI51 burned area product (250 m), high-resolution gridded biomass data, localized EFs, and various statistical survey data to compile a finer-resolution (250 m) and long-term (2001–2020) inventory of 11 pollutants for Heilongjiang Province (an important agricultural and forestry region in China). The results indicated that the annual average OBB emissions in Heilongjiang Province were 12.26, 63.70, 931.60, 17,203.35, 16.09, 171.25, 39.83, 71.22, 184.33, 129.16, and 6.35 Gg for BC, CH4, CO, CO2, NH3, NMVOC, NOx, OC, PM10, PM2.5, and SO2, respectively. Taking PM2.5 as an example, emissions from cropland, forest, and grassland burning accounted for 73%, 26%, and 1% respectively. Geographically, emissions were primarily concentrated in the western, southwestern, and northeastern regions of Heilongjiang. The peak PM2.5 emissions exhibited notable seasonal variation, with maxima occurring in April and October. This is primarily due to the extensive burning of straw during the spring and autumn ploughing seasons. The results of this study provide a detailed multi-year inventory crucial for atmospheric transport models and can support the development of effective pollution control strategies and greenhouse gas mitigation measures.

Direct observation of the evolution behavior of micro to nanoscale precipitates in austenitic heat-resistant steel via electron channeling contrast imaging

Precipitation directly determines the high-temperature properties of the austenitic heat-resistant steels. The precipitations of MX, Z-phase, M23C6 and σ-phase, ranging from micrometers to nanometers, are commonly characterized using the scanning electron microscopy (SEM) and transmission electron microscopy (TEM). However, details of their evolution behavior are still unclear due to the limited SEM resolution using the traditional sampling method and the limited spatial resolution of TEM. In this work, field emission SEM-based electron channel contrast imaging (ECCI) techniques with flexible sampling routes were introduced to observe the precipitation evolution behavior of HR3C steel after aging at 700 °C for 8095 h. We showed that the coarse primary-MX and Z-phase in the as-received steel are relatively stable during aging. The coarse M23C6 and tiny secondary Z-phase dispersions were rapidly formed along the grain/twin boundaries and within dislocation arrays inside the grain interior, respectively. We further found that the M23C6 at grain boundaries would change from continuous to semi-continuous due to the formation of σ-phases, while in twin boundaries, it would become continuous over aging. Moreover, we showed that the σ-phases were in-situ transformed from M23C6 at the grain boundaries via its dissolution, facilitating the nucleation of tiny Z-phase inside the σ-phase grains, and this phenomenon has not been reported so far. We have demonstrated that ECCI with an electrolytic-polishing-based sampling route is effective in revealing multi-scale precipitation with high-throughput efficiency, and it allows the direct observation of the complex behavior of precipitates down to the nanoscale using a bulk sample. This method can be used as an efficient way for the quantitative microstructure study.

The Averaged Hydrostatic Boussinesq Ocean Equations in Generalized Vertical Coordinates

AbstractDue to their limited resolution, numerical ocean models need to be interpreted as representing filtered or averaged equations. How to interpret models in terms of formally averaged equations, however, is not always clear, particularly in the case of hybrid or generalized vertical coordinate models, which limits our ability to interpret the model results and to develop parameterizations for the unresolved eddy contributions. We here derive the averaged hydrostatic Boussinesq equations in generalized vertical coordinates for an arbitrary thickness‐weighted average. We then consider various special cases and discuss the extent to which the averaged equations are consistent with existing ocean model formulations. As previously discussed, the momentum equations in existing depth‐coordinate models are best interpreted as representing Eulerian averages (i.e., averages taken at fixed depth), while the tracer equations can be interpreted as either Eulerian or thickness‐weighted isopycnal averages. Instead we find that no averaging is fully consistent with existing formulations of the parameterizations in semi‐Lagrangian discretizations of generalized vertical coordinate ocean models such as MOM6. A coordinate‐following average would require “coordinate‐aware” parameterizations that can account for the changing nature of the eddy terms as the coordinate changes. Alternatively, the model variables can be interpreted as representing either Eulerian or (thickness‐weighted) isopycnal averages, independent of the model coordinate that is being used for the numerical discretization. Existing parameterizations in generalized vertical coordinate models, however, are not always consistent with either of these interpretations, which, respectively, would require a three‐dimensional divergence‐free eddy tracer advection or a form‐stress parameterization in the momentum equations.

Combined analysis of cross-population healthy adult human microbiome reveals consistent differences in gut microbial characteristics between Western and non-Western countries

Despite extensive research on the gut microbiome of healthy individuals from a single country, there are still a limited number of population-level comparative studies. Moreover, the sequencing approach used in most related studies involves 16S ribosomal RNA (rRNA) sequencing with a limited resolution, which cannot provide detailed functional profiles. In the present study, we applied a combined analysis approach to analyze whole metagenomic shotgun sequencing data from 2035 healthy adult samples from six countries across four continents. Analysis of core species revealed that 13 species were present in more than 90% of all investigated individuals, the majority of which produced short-chain fatty acids (SCFA)-producing bacteria. Our analysis revealed consistently significant differences in gut microbial species and pathways between Western and non-Western countries, such as Escherichia coli and the relation of MetaCyc pathways to the TCA cycle. Specific changes in microbial species and pathways are potentially related to lifestyle and diet. Furthermore, we identified several noteworthy microbial species and pathways that exhibit distinct characteristics specific to China. Interestingly, we observed that China (CHN) was more similar to the United States (USA) and United Kingdom (GBR) in terms of the taxonomic and functional composition of the gut microbiome than India (IND) and Madagascar (MDG), which were more similar to the China (CHN) diet. The current study identified consistent microbial features associated with population and geography, which will inspire further clinical translations that consider paying attention to differences in microbiota backgrounds and confounding factors.

Self-aligned fabrication of vertical, fin-based structures

Modern power devices rely on complex, three-dimensional, vertical designs to increase their power density, ease their thermal management, and improve their reliability. However, fabrication techniques have historically relied on 2D processes for patterning lateral features. This work presents a new technology that uses multiple steps of angled depositions to fabricate self-aligned vertical, fin-based devices that avoid fundamental lithography resolution and alignment limitations. The fabrication flows of two devices, the self-aligned vertical finFET and the high-κ dielectric fin diode, are presented to demonstrate how angled depositions can readily achieve transistors with submicrometer, vertical gates in a source-first process and also create high-aspect ratio GaN fins with a record 70:1 aspect ratio.

Challenges and opportunities when studying movement ecology in science and practical conservation

Movement is a key mechanism influencing biodiversity patterns and ecosystem processes. Movement ecology science aims to understand the causal relationships between environmental conditions, animal movements, interactions and coexistence of species, as well as effects of movement patterns on ecosystem processes. In contrast, practical conservation primarily aims to understand organisms' movements to improve species management, protection, legal monitoring or risk assessment, and species-habitat interactions. Despite the many studies of movement ecology in basic and applied sciences as well as in practical conservation in terrestrial ecosystems, knowledge gain and transfer between disciplines are limited. Better integration and linking of both disciplines would result in diverse science-practice synergies, but these are currently constrained by numerous challenges that need to be overcome. From a scientific perspective, knowledge gain from practice is limited by multitude of case studies with limited spatial and temporal resolution. This can be overcome by improving access and combining the diversity of data for a research area that often deals with small sample sizes. From a practical perspective, the movement ecology framework which is often dedicated to basic research, and access and language barriers of scientific publications limit the application of scientific results in practice. Here movement ecologists should be encouraged to consider conservation issues more frequently in addition to basic research. The transfer of scientific results could be improved by scientists providing sufficient details for practitioners to extract relevant information and publish at least an open-access abstract in local language with clear management recommendations. Further, the use of open-access repositories allows both, scientists and practitioners an overview of the multitude of studies and helps to share data in order to derive general conclusions. Challenges impacting science and practice can be conceptual, organisational and technical in nature. Such constrains can be overcome, for example, by providing verified trapping protocols, using recent technological developments and analytical methods combined with trainings on these state-of-the-art tracking and analysing tools. In particular, collaborative project planning between scientists and practitioners can help to improve the sampling design of applied studies and broaden the data base for science in order to significantly advance the movement ecology framework and gain comprehensive knowledge for practical conservation.

The Field Automatic Insect Recognition-Device-A Non-Lethal Semi-Automatic Malaise Trap for Insect Biodiversity Monitoring: Proof of Concept.

Field monitoring plays a crucial role in understanding insect dynamics within ecosystems. It facilitates pest distribution assessment, control measure evaluation, and prediction of pest outbreaks. Additionally, it provides important information on bioindicators with which the state of biodiversity and ecological integrity in specific habitats and ecosystems can be accurately assessed. However, traditional monitoring systems face various difficulties, leading to a limited temporal and spatial resolution of the obtained information. Despite recent advancements in automatic insect monitoring traps, also called e-traps, most of these systems focus exclusively on studying agricultural pests, rendering them unsuitable for monitoring diverse insect populations. To address this issue, we introduce the Field Automatic Insect Recognition (FAIR)-Device, a novel nonlethal field tool that relies on semi-automatic image capture and species identification using artificial intelligence via the iNaturalist platform. Our objective was to develop an automatic, cost-effective, and nonspecific monitoring solution capable of providing high-resolution data for assessing insect diversity. During a 26-day proof-of-concept evaluation, the FAIR-Device recorded 24.8 GB of video, identifying 431 individuals from 9 orders, 50 families, and 69 genera. While improvements are possible, our device demonstrated its potential as a cost-effective, nonlethal tool for monitoring insect biodiversity. Looking ahead, we envision new monitoring systems such as e-traps as valuable tools for real-time insect monitoring, offering unprecedented insights for ecological research and agricultural practices.

A review of paleofloods in the middle-lower reaches of the Yangtze River during the Holocene: Processes, causes and effects

The understanding of the flooding processes in the entire basin of the middle-lower reaches of the Yangtze River (MLRYR) during the Holocene remains elusive. This is primarily attributed to the constraints posed by site-scale data characterized by limited spatiotemporal resolutions, compounded by conflicting results of the reconstructed Holocene paleoflood records in some of the previous studies. In this study, by synthesizing 114 paleoflood data with robust evidence of flood occurrence (including timing and location of flood occurrence), we comprehensively reconstructed the first continuous Holocene paleoflood record that cover the entire basin of the MLRYR. The results show that at the onset of the Holocene, flood frequency peaked notably in the middle reaches of the Yangtze River (MRYR), with no corresponding peak observed in the lower reaches of the Yangtze River (LRYR). However, in both the MRYR and LRYR, a significant flood frequency peak emerged around 8.0 ± 0.5 ka BP, and scarce flood occurrences appeared approximately 7.5–5.0 ka BP. This scarcity shifted abruptly to a surge in flood frequency from 5.0–4.0 ka BP. Following this, the MRYR witnessed three successive peaks in flood frequency, occurring at approximately 3.0 ka BP, 1.8 ka BP, and 1.0 ka BP, respectively. In contrast, flood events in the LRYR were infrequent during the 3.0–2.0 ka BP period, followed by a surge in frequency from 2.0 ka BP onwards. We further explored the driving mechanisms of paleofloods in the MLRYR and found that floods were more likely to occur during periods of weakened EASM, characterized by wet and unstable climate conditions in the MLRYR. Marine erosion may be also a key factor in the lack of geological evidence for palaeofloods during the early Holocene. Additionally, variations in the ISM and human activities during the late Holocene have significantly influenced the occurrence of floods in the MLRYR. By comparing the paleoflood frequency with the spatial distribution and number of archaeological sites in the MLRYR, respectively, we propose that the period of flood scarcity during the middle Holocene may have facilitated the development of rice agriculture and the prosperity of ancient settlements. In contrast, during flood-prone period, ancient societies adapted and coped with floods by migrating to higher terrain during the early Holocene and implementing simple hydraulic engineering techniques during the late Holocene.