Current Traces Research Articles

A Clickase-Mediated Immunoassay Based on Nanopore and Bionic Signal Labels for Ultrasensitive, Portable, and On-Site Detection of Ricin.

It is of particular importance to develop an effective method that possesses several merits simultaneously of rapid, ultrasensitive, portable, and on-site detection potential for food safety detection. Herein, we propose a clickase-mediated immunoassay based on nanopore and bionic signal labels for the detection of ricin. The introduction of Cu/Cys clickase and nanopore simultaneously effectively addressed the inherent limitations of natural enzymes and colorimetric signal output, respectively. Using this method, bionic signal labels can be easily formed through DNA and Gram-positive bacterial cell wall terminal peptide fragments (labeled by alkynyl and azide, respectively) and vancomycin. Translocation of the D-P@vancomycin through the nanopore generated highly specific oscillation current traces. This method showed a great on-site detection potential and superior analytical performance owing to the combination of the specificity of antibodies, high CuAAC click reaction catalytic efficiency of clickase, ultrasensitivity of the nanopore, and high signal resolution of D-P@vancomycin. Moreover, the practical applicability of the established method was also verified, achieving a limit of detection (LOD) down to 200.9 ag/mL with a wide linear relationship under the optimized conditions. In conclusion, this method is promising for rapid, portable, ultrasensitive, and on-site food safety detection.

Fault traceability of power grid dispatching system based on DPHS-MDS and LambdaMART algorithm

Under the background of increasing system service data, it is difficult to trace the faults of power dispatching system. The current fault tracing method has some problems, such as low precision, low efficiency and difficult troubleshooting. To solve this problem, a fault tracing method based on data partition hybrid sampling method and multiple incremental regression tree algorithm is proposed. In this paper, it first uses the hybrid sampling method of data partition and dynamic selection technology to detect the business anomaly, and then applies the clustering algorithm and the information difference graph model to realize the fault tracing of system components. The experimental results showed that the F-metric value and geometric mean value of the study design method were 0.964 and 0.685, respectively. In addition, normalized discounted cumulative gains were observed in the top 10, and the mean average precision of the top 10 was 0.752 and 0.186, respectively. The proposed method can effectively improve the fault tracing efficiency of power grid operation and maintenance personnel, and provide strong data support for the safety maintenance of power grid dispatching system.

Neuroelectrophysiology-compatible electrolytic lesioning.

Lesion studies have historically been instrumental for establishing causal connections between brain and behavior. They stand to provide additional insight if integrated with multielectrode techniques common in systems neuroscience. Here, we present and test a platform for creating electrolytic lesions through chronically implanted, intracortical multielectrode probes without compromising the ability to acquire neuroelectrophysiology. A custom-built current source provides stable current and allows for controlled, repeatable lesions in awake-behaving animals. Performance of this novel lesioning technique was validated using histology from ex vivo and in vivo testing, current and voltage traces from the device, and measurements of spiking activity before and after lesioning. This electrolytic lesioning method avoids disruptive procedures, provides millimeter precision over the extent and submillimeter precision over the location of the injury, and permits electrophysiological recording of single-unit activity from the remaining neuronal population after lesioning. This technique can be used in many areas of cortex, in several species, and theoretically with any multielectrode probe. The low-cost, external lesioning device can also easily be adopted into an existing electrophysiology recording setup. This technique is expected to enable future causal investigations of the recorded neuronal population's role in neuronal circuit function, while simultaneously providing new insight into local reorganization after neuron loss.

Dynamics of JET runaway electron beams in D2-rich shattered pellet injection mitigation experiments

Abstract The publication provide further insights into the dynamics of JET runaway electron (RE) beams mitigated by D2-rich shattered pellet injection (SPI) (Reux et al 2022 Plasma Phys. Control. Fusion 64 034002). Multi-diagnostic analyses show that mechanisms causing continuous RE losses and energy transfer from hot electrons to cold background plasma can act before the SPI. After the SPI, measurements are compatible with a reduction of the maximum energy and pitch angle of the RE distribution while the population of supra-thermal electrons increases. The RE population growth is likely due to electron avalanche. Dark island-like pattern chains, characterised by an integer poloidal mode number and a certain minor radius, are identified in the JET RE beam synchrotron radiation videos. The synchrotron island dynamics is studied via a newly developed computer vision code (Sommariva and Silburn https://c4science.ch/source/pSpiPTV/). The radial motion of synchrotron island chains is found to be consistent with the most plausible time evolution of the radial current density profile compatible with both the RE synchrotron videos and the total RE current time trace. Similarly, correlations are identified between the temporal progression of the synchrotron islands poloidal rotation frequency and sudden MHD relaxation events. Loss-of-RE events probably caused by non-linear interactions between synchrotron islands are observed for the first time. Experimental evidences suggest that synchrotron islands are possibly related to the existence of magnetic islands which may lead to the development of new RE beam mitigation strategies.

A ZVS Critical Current Tracing Modulation for Current-Fed Series Resonant Converters in Fuel Cell Vehicles

A ZVS Critical Current Tracing Modulation for Current-Fed Series Resonant Converters in Fuel Cell Vehicles

Rate of nosocomial MRSA transmission evaluated via contact screening

BackgroundThe prevention of methicillin-resistant S. aureus (MRSA) transmission in the healthcare setting is a priority in Infection Control practices. A cornerstone of this policy is contact tracing of nosocomial contacts after an unexpected MRSA finding. The objective of this retrospective study was to quantify the rates of MRSA transmission in different clinical settings.MethodsThis multi-centre study included MRSA contact screening results from two regional hospitals and one academic hospital. MRSA contact tracing investigations from 2000 until 2019 were reviewed and post-contact screening results were included of index patients with an MRSA-positive culture and their unprotected contacts. Available typing results were used to rule out incidental findings.ResultsOf 27,377 contacts screened after MRSA exposure, 21,488 were Health Care Workers (HCW) and 4816 patients. Post-contact screening was initiated for a total of 774 index cases, the average number of screened contacts per index case was 35.7 (range 1 to 640). MRSA transmission was observed in 0.15% (41) of the contacts, 19 (0.09%) HCW and 22 (0.46%) patients. The number needed to screen to detect one MRSA transmission was 667. The highest risk of MRSA transmission occurred during patient-to-patient contacts, with transmission rates varying from 0.32 to 1.32% among the participating hospitals. No transmissions were detected in HCW (n=2834) in the outpatient setting, and the rate of transmissions among HCW contacts on the wards was 0.13% (19 of 15,874). Among 344 contacts of patients with contact precautions, no transmissions were detected.ConclusionsReconsidering current MRSA contact tracing practices may lead to a more targeted approach with a lower number needed to screen.

Detection of Intrinsically Disordered Peptides by Biological Nanopore.

Intrinsically disordered protein regions (IDPRs) are pivotal in regulation of transcription and facilitation of signal transduction. Because of their multiple conformational states of structure, characterizing the highly flexible structures of IDPRs becomes challenging. Herein, we employed the wild-type (WT) aerolysin nanopore as a real-time biosensor for identification and monitoring of long peptides containing IDPRs. This sensor successfully identified three intrinsically disordered peptides, with the lengths up to 43 amino acids, by distinguishing the unique signatures of blockade current and duration time. The analysis of the binding constant revealed that interactions between the nanopore and peptides are critical for peptide translocation, which suggests that mechanisms beyond mere volume exclusion. Furthermore, we were able to compare the conformational stabilities of various IDPRs by examining the detailed current traces of blockade events. Our approach can detect the conformational changes of IDPR in a confined nanopore space. These insights broaden the understanding of peptide structural changes. The nanopore biosensor showed the potential to study the conformations change of IDPRs, IDPRs transmembrane interactions, and protein drug discovery.

Negative Differential Resistance in Single‐Molecule Junctions Based on Heteroepitaxial Spherical Au/Pt Nanogap Electrodes

AbstractSingle‐molecule junctions exploit the internal structure of molecular orbitals to construct a new class of functional quantum devices. The demonstration of negative differential resistance (NDR) in single‐molecule junctions is direct evidence of quantum mechanical tunneling through a molecular orbital. Here, a pronounced NDR effect is reported with a peak‐to‐valley ratio of 30.1 on a single‐molecule junction of π‐conjugated quinoidal‐fused oligosilole derivatives, Si2 × 2, embedded between the unique electroless gold‐plated heteroepitaxial spherical Au/Pt nanogap electrodes. This NDR feature persists in a consecutive endurance test of 180 current traces. the thermally stable NDR effects in the Si2 × 2 single‐molecule junctions between 9 and 300 K are demonstrated. The density functional theory calculations under electric fields indicate that the NDR effect can be ascribed to the bias‐dependent resonant tunneling transport via the polarized HOMO, which has asymmetrically changed electrode coupling with increased bias voltages. The results confirm a promising electrical platform for constructing functional quantum devices at the single‐molecule level.

NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation

Datasets of labeled network traces are essential for a multitude of machine learning (ML) tasks in networking, yet their availability is hindered by privacy and maintenance concerns, such as data staleness. To overcome this limitation, synthetic network traces can often augment existing datasets. Unfortunately, current synthetic trace generation methods, which typically produce only aggregated flow statistics or a few selected packet attributes, do not always suffice, especially when model training relies on having features that are only available from packet traces. This shortfall manifests in both insufficient statistical resemblance to real traces and suboptimal performance on ML tasks when employed for data augmentation. In this paper, we apply diffusion models to generate high-resolution synthetic network traffic traces. We present NetDiffusion, a tool that uses a finely-tuned, controlled variant of a Stable Diffusion model to generate synthetic network traffic that is high fidelity and conforms to protocol specifications. Our evaluation demonstrates that packet captures generated from NetDiffusion can achieve higher statistical similarity to real data and improved ML model performance than current state-of-the-art approaches (e.g., GAN-based approaches). Furthermore, our synthetic traces are compatible with common network analysis tools and support a myriad of network tasks, suggesting that NetDiffusion can serve a broader spectrum of network analysis and testing tasks, extending beyond ML-centric applications.

Passing Behavior of Oligonucleotides through a Stacked DNA Nanochannel with Featured Path Design.

DNA nanotechnology has emerged as a useful tool for constructing artificial channels penetrating the lipid bilayer. In this work, we introduce a stacked DNA origami nanochannel device characterized by a width-variable pathway, consisting of narrow entrance and exit channels coupled with a wide, modifiable lumen. This design modulates the translocation behavior of oligonucleotides, revealing distinct stages of signal patterns in the recorded current traces. The observed prolonged dwell times indicate oligonucleotide retention, specifically due to the transition from the wide lumen to the narrower exit channel, while variations in current recovery between events suggested intermediate channel states between conducting and blocking. Further, by incorporating sequence-specific overhangs within the channel lumen, we achieved unique asymmetric current profiles during ATP aptamer translocation events. Featured stages also highlighted the aptamer binding dynamics and ATP-induced release. The distinguished oligonucleotide passing behaviors afforded by the stacked DNA origami channel with interior decoration demonstrated the strategic and profitable attempts at DNA nanochannel engineering for nanodevice development and applications.

Human health risk assessment of microbial contamination and trace metals in water and soils of Chileka Township, Blantyre, Malawi

This study assessed nutrients and microbial contamination in water and soil samples from Chileka Township, Blantyre City, Malawi. Elevated total and fecal coliforms (1300 cfu/100 mL and 290 cfu/100 mL) in groundwater (GW), and (34,000 cfu/100 mL and 8000 cfu/100 mL) in surface water (SW) were found, representing a risk of exposure to water-borne disease. While the criteria in the Malawi Standard for raw groundwater was mostly met, water from only 20% of the boreholes complied with the WHO requirements. Nitrate (NO3─) and Cl─ (47.8 mg/L and 263 mg/L) exceeded the WHO limits in GW. Cadmium (Cd) occurred in a few cases at concentrations up to 0.217 mg/L and 0.138 mg/L in GW and SW. Lead (Pb) and Cr were below detection limits, while Mn (0.319 mg/L and 0.640 mg/L) in GW and SW, and Fe (6.92 mg/L) in SW compromised taste. Though bacteriologically unfit for raw consumption by humans, both GW and SW chemically met FAO-acceptable limits for irrigation, and standards for livestock watering. The NO3─ and PO43─ maximum concentrations in soil were 58.9 mg/kg and 506 mg/kg, respectively. Lead (Pb) and Cd were not detected whereas Cr, Zn, Cu, Mn and Fe in soil were 27.7 mg/kg, 190 mg/kg, 60.4 mg/kg, 1307 mg/kg and 6552 mg/kg, respectively. Magnesium (Mg), Ca, Na and K were 20,523 mg/kg, 22,334 mg/kg, 544 mg/kg and 5758 mg/kg, respectively in soil. The human health risk assessment results, on the other hand, showed that at least 30% (6 out of 20) of the GW samples and 60% (3 out 8) of the SW samples had HI > 1 for adults, children and infants, indicating existence of non-carcinogenic risk. Similarly, at least 15% (3 out 20) of the GW samples and 18% (1 out of 8) of the SW samples had CR > 0.001 for adults, children and infants, suggesting a risk of developing cancer during a lifetime due to Cd exposure. Though both GW and SW are generally of good chemical quality, chronic exposure to nitrate and cadmium is a health risk in the area. The current trace metal levels are not worrisome, but soil nitrate and phosphate may need regular monitoring.

MRNA psi profiling using nanopore DRS reveals cell type-specific pseudouridylation.

Pseudouridine (psi) is one of the most abundant human mRNA modifications generated from the isomerization of uridine via psi synthases, including TRUB1 and PUS7. Nanopore direct RNA sequencing combined with our recent tool, Mod-p ID, enables psi mapping, transcriptome-wide, without chemical derivatization of the input RNA and/or conversion to cDNA. This method is sensitive for detecting changes in positional psi occupancies across cell types, which can inform our understanding of the impact on gene expression. We sequenced, mapped, and compared the positional psi occupancy across six immortalized human cell lines derived from diverse tissue types. We found that lung-derived cells have the highest proportion of psi, while liver-derived cells have the lowest. Further, among a list of highly conserved sites across cell types, most are TRUB1 substrates and fall within the coding sequence. We find that these conserved psi positions correspond to higher levels of protein expression than expected, suggesting translation regulation. Interestingly, we identify cell type-specific sites of psi modification in ubiquitously expressed genes. We validate these sites by ruling out single-nucleotide variants, analyzing current traces, and performing enzymatic knockdowns of psi synthases. Finally, we characterize sites with multiple psi modifications on the same transcript (hypermodification type II) and found that these can be conserved or cell type specific. Among these, we discovered examples of multiple psi modifications within the same k-mer for the first time and analyzed the effect on current distribution. Our data support the hypothesis that motif sequence and the presence of psi synthase are insufficient to drive modifications, that psi modifications contribute to regulating translation and that cell type-specific trans-acting factors play a major role in driving pseudouridylation.

Resolving the Amino Acid Sequence of Aβ1‐42 at the Single‐Residue Level Using Subnanopores in Ultrathin Films

AbstractProteoforms are proteins derived from highly related genes or post translational modifications (PTMs) of the same protein. They share extremely similar primary structures but have varying functions. Unfortunately, protein de novo sequencing including specific PTM/mutation detection is still challenging. Herein, a nanopore‐based technique is reported to resolve the amino acid order of amyloid‐β (Aβ1‐42) with site specificity. Subnanopores are sputtered in 5 nm‐thick inorganic membranes with a sensing depth of 0.66 nm inferred by finite element analysis. Denatured molecules at 0.45 ng mL−1 translocate through subnanopores while the current traces are sampled at 500 kHz with rms noise <15 pA. Hundreds of blockades are clustered using machine learning, and multiple blockades are averaged to establish current consensus. Consensus traces strongly correlate with a linear model of amino acid volume of Aβ1‐42 at single residue resolution, with Pearson Correlation Coefficients (PCCs) of 0.81 ± 0.03 and 0.92 ± 0.03 before and after dynamic time warping (DTW). A scrambled version of Aβ1‐42 is tested for validation purposes. Deep learning classification reveals that different polypeptides generate distinct translocation fluctuating patterns, but variations become imperceptible for the same species measured across nanopores (Area Under the Curve, AUC 0.93 ± 0.05 vs 0.64 ± 0.12). Lastly, important PTMs and mutations are site‐specifically located along the primary structure, implying new potential clinical applications.

Analysis of vibrational dynamics in cell-substrate interactions using nanopipette electrochemical sensors

Cell-substrate interaction plays a critical role in determining the mechanical status of living cell membrane. Changes of substrate surface properties can significantly alter the cell mechanical microenvironment, leading to mechanical changes of cell membrane. However, it is still difficult to accurately quantify the influence of the substrate surface properties on the mechanical status of living cell membrane without damage. This study addresses the challenge by using an electrochemical sensor made from an ultrasmall quartz nanopipette. With the tip diameter less than 100 nm, the nanopipette-based sensor achieves highly sensitive, noninvasive and label-free monitoring of the mechanical status of single living cells by collecting stable cyclic membrane oscillatory signals from continuous current versus time traces. The electrochemical signals collected from PC12 cells cultured on three different substrates (bare ITO (indium tin oxides) glass, hydroxyl modified ITO glass, amino modified ITO glass) indicate that the microenvironment more favorable for cell adhesion can increase the membrane stiffness. This work provides a label-free electrochemical approach to accurately quantify the mechanical status of single living cells in real-time, which may help to better understand the relationship between the cell membrane and the extra cellular matrix.

Transient analysis of the electrochemical noise arising from the stainless steel local anodic events by the equivalent circuit approach

PurposeThis study aims to investigate the individual electrochemical transients arising from local anodic events on stainless steel, to uncover the potential mechanisms producing different types of transients and to derive appropriate parameters indicative of the corrosion severity of such transient events.Design/methodology/approachAn equivalent circuit model was used for the transient analysis, which was performed using a local current allocation rule based on the relative instant cathodic resistance of the coupled electrodes, as well as the kinetic parameters derived from the electrochemical polarization measurement.FindingsThe shape and size of the electrochemical current transients arising from SS 316 L were influenced by the film stability, local anodic dissolution kinetics and the symmetry of the cathodic kinetics between the coupled electrodes, where the ultralong transient might correspond to the propagation of film damage with a slow anodic dissolution rate. The dynamic cathodic resistance during the final stage of transient current growth can serve as a characteristic parameter that reflects the loss of passive film protection.Originality/valueEstimation of the local anodic current trace opens a new way for individual electrochemical transient analysis associated with the charges involved, local current densities and changes in film resistance throughout localized corrosion processes.

A conservative first-collision source treatment for ray effect mitigation in discrete-ordinate radiation transport solutions

Deterministic transport codes play a fundamental role in the modeling and simulation of neutron transport. One of the most common deterministic methods is the method of discrete ordinates, also known as the Sn method. While offering significant advantages over other deterministic methods or stochastic methods like Monte Carlo, the method of discrete ordinates suffers from non-physical artifacts in its local solution due to its discretization of angle. These artifacts, referred to as ray effects because of their ray-like appearance, tend to be worse in problems with small sources in areas with little scattering. Significant effort has gone into developing methods to mitigate ray effects, such as the first-collision source treatment, which separates the angular flux into the uncollided and collided fluxes and solves them using non-traditional techniques such as ray tracing. Current ray tracing methods typically trace to a set of points inside a zone to compute an overall flux. However, this approach has significant drawbacks, such as a low potential for optimization, as well as not being conservative. A new method has been developed that traces instead to a set of points on each of a zone's surfaces and computes the currents, before using these to obtain the flux. A comparison between these two ray tracing methods shows significant advantages to the new surface method, including inherent particle conservation, similar convergence rate and increased potential for optimization through ray sharing.

Neuronal glucose sensing mechanisms and circuits in the control of insulin and glucagon secretion.

Glucose homeostasis is mainly under the control of the pancreatic islet hormones insulin and glucagon, which, respectively, stimulate glucose uptake and utilization by liver, fat, and muscle and glucose production by the liver. The balance between the secretions of these hormones is under the control of blood glucose concentrations. Indeed, pancreatic islet β-cells and α-cells can sense variations in glycemia and respond by an appropriate secretory response. However, the secretory activity of these cells is also under multiple additional metabolic, hormonal, and neuronal signals that combine to ensure the perfect control of glycemia over a lifetime. The central nervous system (CNS), which has an almost absolute requirement for glucose as a source of metabolic energy and thus a vital interest in ensuring that glycemic levels never fall below ∼5 mM, is equipped with populations of neurons responsive to changes in glucose concentrations. These neurons control pancreatic islet cell secretion activity in multiple ways: through both branches of the autonomic nervous system, through the hypothalamic-pituitary-adrenal axis, and by secreting vasopressin (AVP) in the blood at the level of the posterior pituitary. Here, we present the autonomic innervation of the pancreatic islets; the mechanisms of neuron activation by a rise or a fall in glucose concentration; how current viral tracing, chemogenetic, and optogenetic techniques allow integration of specific glucose sensing neurons in defined neuronal circuits that control endocrine pancreas function; and, finally, how genetic screens in mice can untangle the diversity of the hypothalamic mechanisms controlling the response to hypoglycemia.

Target hierarchy-guided knowledge tracing : Fine-grained knowledge state modeling

Knowledge Tracing (KT) focuses on modeling the exercise process of students, assessing their knowledge state changes during the exercise process, and further providing targeted guidance for teaching and learning. Current KT models have made significant progress by utilizing deep learning technologies to model relevant attributes of the questions. However, in current knowledge tracing strategies, we have yet to exhaustively explore how to precisely evaluate the hierarchies of students’ knowledge mastery to better discern their current target hierarchy. To address the limitations of the KT model in accurately identifying and positioning target hierarchy, our research focuses on two key areas: first, we dynamically track students’ target hierarchies by evaluating their feedback on questions at different hierarchies ; second, we assess their actual question-solving abilities within these target hierarchies, thereby optimizing our evaluation of their knowledge status. Based on these insights, we propose the Target Hierarchy-guided Knowledge Tracing (THKT) model. In this model, we first incorporate the identification of question hierarchy into the model representation. Then, to avoid homogenization of the target hierarchy, we dynamically track the appropriate hierarchy for students based on their varying feedback to questions of the same hierarchy, thereby pinpointing their learning target hierarchies. Simultaneously, we develop a KCs applied ability learning module that works in conjunction with the target hierarchies to generate interpretable prediction results. The proposed THKT model has been tested and evaluated using three public, real-world educational datasets. The findings clearly demonstrate that our approach shines in the realm of KT prediction tasks, providing significantly interpretability. For broader research, we plan to provide the source code at: https://github.com/xinjiesun-ustc/THKT to ensure accessibility and promote further innovation in this field.

From Average Transient Transporter Currents to Microscopic Mechanism─A Bayesian Analysis.

Electrophysiology studies of secondary active transporters have revealed quantitative mechanistic insights over many decades of research. However, the emergence of new experimental and analytical approaches calls for investigation of the capabilities and limitations of the newer methods. We examine the ability of solid-supported membrane electrophysiology (SSME) to characterize discrete-state kinetic models with >10 rate constants. We use a Bayesian framework applied to synthetic data for three tasks: to quantify and check (i) the precision of parameter estimates under different assumptions, (ii) the ability of computation to guide the selection of experimental conditions, and (iii) the ability of our approach to distinguish among mechanisms based on SSME data. When the general mechanism, i.e., event order, is known in advance, we show that a subset of kinetic parameters can be "practically identified" within ∼1 order of magnitude, based on SSME current traces that visually appear to exhibit simple exponential behavior. This remains true even when accounting for systematic measurement bias and realistic uncertainties in experimental inputs (concentrations) are incorporated into the analysis. When experimental conditions are optimized or different experiments are combined, the number of practically identifiable parameters can be increased substantially. Some parameters remain intrinsically difficult to estimate through SSME data alone, suggesting that additional experiments are required to fully characterize parameters. We also demonstrate the ability to perform model selection and determine the order of events when that is not known in advance, comparing Bayesian and maximum-likelihood approaches. Finally, our studies elucidate good practices for the increasingly popular but subtly challenging Bayesian calculations for structural and systems biology.

NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation

Datasets of labeled network traces are essential for a multitude of machine learning (ML) tasks in networking, yet their availability is hindered by privacy and maintenance concerns, such as data staleness. To overcome this limitation, synthetic network traces can often augment existing datasets. Unfortunately, current synthetic trace generation methods, which typically produce only aggregated flow statistics or a few selected packet attributes, do not always suffice, especially when model training relies on having features that are only available from packet traces. This shortfall manifests in both insufficient statistical resemblance to real traces and suboptimal performance on ML tasks when employed for data augmentation. In this paper, we apply diffusion models to generate high-resolution synthetic network traffic traces. We present NetDiffusion1, a tool that uses a finely-tuned, controlled variant of a Stable Diffusion model to generate synthetic network traffic that is high fidelity and conforms to protocol specifications. Our evaluation demonstrates that packet captures generated from NetDiffusion can achieve higher statistical similarity to real data and improved ML model performance than current state-of-the-art approaches (e.g., GAN-based approaches). Furthermore, our synthetic traces are compatible with common network analysis tools and support a myriad of network tasks, suggesting that NetDiffusion can serve a broader spectrum of network analysis and testing tasks, extending beyond ML-centric applications.