Short Run Time Research Articles

Oil price forecasting: A hybrid GRU neural network based on decomposition–reconstruction methods

Significant fluctuations in the price of crude oil in recent years make accurate price estimations of critical importance. A reliable method for crude oil price forecasting is beneficial in guiding production and investment. This study presents two new hybrid predictors for forecasting oil prices based on recurrent neural networks using variational modal decomposition (VMD), sample entropy (SE), and gated recurrent units (GRUs). We consider the West Texas Intermediate daily closing oil prices from August 2, 2010, to December 31, 2019. The hybrid prediction methods first decompose the original time series and then reconstruct the decomposed data using SE. The reconstructed data are used to formulate independent price predictions, which are subsequently combined to produce an ensemble output. With the West Texas Intermediate dataset, the VMD-SE-GRU framework proposed in this paper gives a root mean square error of 0.6735, mean absolute error of 0.4585, mean absolute percentage error of 0.8059, and R2 value of 0.9272. Overall, the hybrid VMD-SE-GRU framework has several advantages over previous models and produces highly accurate forecasts with a shorter runtime. Thus, the new approach provides an effective tool for predicting oil prices.

Blind Video Quality Assessment for Ultra-High-Definition Video Based on Super-Resolution and Deep Reinforcement Learning.

Ultra-high-definition (UHD) video has brought new challenges to objective video quality assessment (VQA) due to its high resolution and high frame rate. Most existing VQA methods are designed for non-UHD videos-when they are employed to deal with UHD videos, the processing speed will be slow and the global spatial features cannot be fully extracted. In addition, these VQA methods usually segment the video into multiple segments, predict the quality score of each segment, and then average the quality score of each segment to obtain the quality score of the whole video. This breaks the temporal correlation of the video sequences and is inconsistent with the characteristics of human visual perception. In this paper, we present a no-reference VQA method, aiming to effectively and efficiently predict quality scores for UHD videos. First, we construct a spatial distortion feature network based on a super-resolution model (SR-SDFNet), which can quickly extract the global spatial distortion features of UHD videos. Then, to aggregate the spatial distortion features of each UHD frame, we propose a time fusion network based on a reinforcement learning model (RL-TFNet), in which the actor network continuously combines multiple frame features extracted by SR-SDFNet and outputs an action to adjust the current quality score to approximate the subjective score, and the critic network outputs action values to optimize the quality perception of the actor network. Finally, we conduct large-scale experiments on UHD VQA databases and the results reveal that, compared to other state-of-the-art VQA methods, our method achieves competitive quality prediction performance with a shorter runtime and fewer model parameters.

Genetic Algorithm-Based Online-Partitioning BranchyNet for Accelerating Edge Inference.

In order to effectively apply BranchyNet, a DNN with multiple early-exit branches, in edge intelligent applications, one way is to divide and distribute the inference task of a BranchyNet into a group of robots, drones, vehicles, and other intelligent edge devices. Unlike most existing works trying to select a particular branch to partition and deploy, this paper proposes a genetic algorithm (GA)-based online partitioning approach that splits the whole BranchyNet with all its branches. For this purpose, it establishes a new calculation approach based on the weighted average for estimating total execution time of a given BranchyNet and a two-layer chromosome GA by distinguishing partitioning and deployment during the evolution in GA. The experiment results show that the proposed algorithm can not only result in shorter execution time and lower device-average energy cost but also needs less time to obtain an optimal deployment plan. Such short running time enables the proposed algorithm to generate an optimal deployment plan online, which dynamically meets the actual requirements in deploying an intelligent application in the edge.

Application of QbD based approach in development and validation of RP-HPLC method for simultaneous estimation of methotrexate and baicalin in dual-drug-loaded liposomes.

The present study delineates the development of a novel, rugged and sensitive stability-indicating risk-based HPLC method for the concurrent estimation of methotrexate (MTX) and baicalin (BCL) in dual-drug-loaded-nanopharmaceuticals based on an analytical quality-by-design approach. Preliminary screening trials along with systemic risk analysis were performed, endeavouring to explicate the critical method attributes, namely pH, percentage of orthophosphoric acid and percentage of acetonitrile, that influence the critical quality attributes. Box-Behnken design was utilized for the optimization of the tailing factor as the response for MTX and BCL in a short run time. The chromatographic conditions were optimized by performing 17 experimental runs using design expert software. The chromatographic conditions were selected after the analysis of the optimized zone within the confines of the design space: water:acetonitrile adjusted to a pH of 3.0 with 0.05% orthophosphoric acid (60:40, %v/v) was the mobile phase, the flow rate was 1.0ml/min and an analytical C18 column was used at an isobestic wavelength of 282 nm. Furthermore, the optimized method was validated in accordance with the International Council for Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use guidelines and was found to be within the prescribed limits. Therefore, the developed reversed-phase-high-performance liquid chromatography method has a high degree of practical utility for synchronous detection of MTX and BCL in pharmaceutical nano-dosage forms such as protein-based nanoparticles, nanocrystals, polymeric nanoparticles and metallic nanoparticles in in vivo and in vitro studies.

A fast and validated LC-MS/MS method to quantify lenvatinib in dried blood spot

A new LC-MS/MS method for the quantification of lenvatinib (LENVA) in venous Dried Blood Spot (DBS) samples has been presented. This method is characterized by a short run time (4 min), requires a volumetric sampling of 10 µL and extraction of the entire spot to avoid hematocrit (Hct) and spot volume effects. The quantification method was successfully validated in the range of 5.00–2000 ng/mL on two different DBS filter papers (Whatman 31 ET CHR and Whatman 903) according to European Medicines Agency (EMA) and Food and Drug Administration (FDA) guidelines, European Bioanalysis Forum (EBF), and International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT) recommendations. During the validation process, the following parameters were evaluated: recovery (≥ 77% for both filter papers), absence of matrix effect, process efficiency (close to 72% for Whatman 31 ET CHR and close to 77% for Whatman 903), Hct effect (CV ≤ 6.3% and accuracy within 96–112%), linearity (r ≥ 0.998 for Whatman 31 ET CHR and r ≥ 0.999 for Whatman 903), intra- and inter-day precision (CV ≤ 8.8%) and accuracy (92.8–108%), selectivity and sensitivity, reproducibility with incurred samples reanalysis (ISR), and stability. This method was applied to quantify venous DBS samples from patients with hepatocellular carcinoma treated with LENVA enrolled in a cross-validation study (CRO-2018–83). A good correlation between LENVA plasma concentration determined by standard procedure and the new developed DBS LENVA method (R2 ≥ 0.996) has been observed.

Approximating optimal building retrofit solutions for large-scale retrofit analysis

Coordinated large scale building retrofit is one of the most effective approaches not only to reduce the environmental footprint of existing buildings but also to assure that climate goals can be met. Existing bottom-up retrofit approaches become infeasible or lack in terms of accuracy and/or computational cost especially when the scale of application increases in size. Hence, the aim of this paper is to propose and develop a bottom-up approach for large-scale building retrofit with the use of a scalable building-level surrogate retrofit model. To ensure scalability and generalization to large areas, we use artificial neural networks. The developed approach consists of a set of artificial neural networks trained on building archetypes, aiming to predict near-optimal building retrofit solutions for all residential buildings in Switzerland. This is achieved with a small set of inputs, and within a short run time. In order to validate the developed approach, we compare it with a conventional mixed building simulation and optimization approach. The surrogate model computes near-optimal building retrofit solutions, with an average coefficient of determination of 0.91 and an average f1 score of 0.89. To illustrate the advantages of this model, we perform a large-scale retrofit analysis of a case study in Geneva municipality. Such a surrogate model is capable of providing insights into the overall retrofit potential of large areas and thus shall support reaching energy emission reduction targets of existing residential buildings.

Adjusted green HPLC determination of nirmatrelvir and ritonavir in the new FDA approved co-packaged pharmaceutical dosage using supported computational calculations

The greening of analytical methods has gained interest in the quantitative analysis field to reduce environmental impact and improve safety health conditions for analysts. Nirmatrelvir plus ritonavir is a new FDA approved co-packaged medication developed for the treatment of COVID-19. The aim of this research was to develop green fitted HPLC method using pre experimental computational testing of different stationary phases as well as selecting mobile phase regarding to green analytical chemistry principles. Computational study was designed to test the physical interaction between nirmatrelvir and ritonavir and different columns (C8, C18, Cyano column). The study showed that the C18 column was better for simultaneous HPLC analysis of the cited drugs. Regarding to green point of view, mobile phase consisted of ethanol: water (80:20, v/v) provided an efficient chromatographic separation of nirmatrelvir and ritonavir within a short analytical run time, reasonable resolution and excellent sensitivity. Isocratic elution was performed on a selected C18 column and a green adjusted mobile phase at flow rate of 1 mL/min and UV detection at 215 nm. The chromatographic system allowed complete baseline separation with retention times of 4.9 min for nirmatrelvir and 6.8 min for ritonavir. The method succeeded to determine nirmatrelvir and ritonavir over the concentration range of 1.0–20.0 μg/mL in the pure form and in pharmaceutical dosage form. Greenness profiles of the applied HPLC method was assessed using analytical eco-scale, the green analytical procedure index and the AGREE evaluation method. The results revealed adherence of the described method to the green analytical chemistry principles. The authors hope to provide a promising challenge for achieving green goals through integrating computational tools and applying them with green assessment metrics.

Advance prediction of coastal groundwater levels with temporal convolutional and long short-term memory networks

Abstract. Prediction of groundwater level is of immense importance and challenges coastal aquifer management with rapidly increasing climatic change. With the development of artificial intelligence, data-driven models have been widely adopted in hydrological process management. However, due to the limitation of network framework and construction, they are mostly adopted to produce only 1 time step in advance. Here, the temporal convolutional network (TCN) and models based on long short-term memory (LSTM) were developed to predict groundwater levels with different leading periods in a coastal aquifer. The initial data of 10 months, monitored hourly in two monitoring wells, were used for model training and testing, and the data of the following 3 months were used as prediction with 24, 72, 180, and 360 time steps (1, 3, 7, and 15 d) in advance. The historical precipitation and tidal-level data were incorporated as input data. For the one-step prediction of the two wells, the calculated R2 of the TCN-based models' values were higher and the root mean square error (RMSE) values were lower than that of the LSTM-based model in the prediction stage with shorter running times. For the advanced prediction, the model accuracy decreased with the increase in the advancing period from 1 to 3, 7, and 15 d. By comparing the simulation accuracy and efficiency, the TCN-based model slightly outperformed the LSTM-based model but was less efficient in training time. Both models showed great ability to learn complex patterns in advance using historical data with different leading periods and had been proven to be valid localized groundwater-level prediction tools in the subsurface environment.

Algorithm Research Based on the PDE Sensitivity Filter

The PDE sensitivity filter can eliminate the checkerboard patterns and numerical instability existing in the topology optimization results of continuum structures, and the essence of the PDE sensitivity filter is the Helmholtz partial differential equation with Neumann boundary conditions. To solve the large-scale PDE sensitivity filter problem, the conjugate gradient algorithm, the multigrid algorithm and the multigrid preconditioned conjugate gradient algorithm were used to solve the algebraic equations obtained by finite element analysis, and the effects of accuracy, filter radius and grid numbers on the efficiency of topology optimization were studied. The results show that, compared with the conjugate gradient algorithm and the multi-grid algorithm, the multi-grid preconditioned conjugate gradient algorithm has the least number of iterations and the shortest running time, which greatly improves the efficiency of topology optimization.

Dynamic Rapid Scheduling Algorithm for Vehicle Time Sensitive Communication based on CILP and AGA

Vehicle time-sensitive communication (VTSC) is an emerging paradigm where the Time-Sensitive Networking together with V2X provides connected vehicle with deterministic and low communication latency. Scheduling is a critical step to realize the determinism. Recently, as the scale and variety of VTSCs increase, the requirements of simultaneously achieve multicast supportment, short runtime, dynamic schduling ability and low resource overhead challenge the traditional TSN scheduling algorithms. This paper proposes a scheduling scheme with multiple stages to achieve both efficient and rapid scheduling. This paper first formulates the joint routing and scheduling problems by proposing a cluster-Integer Linear Programming (CILP). It aims to accelerate the static scheduling and realize real-time response for dynamic incremental scheduling. An improved topology pruning method and a clustering-aggregation ILP procedure are presented. Then, since the dynamic scheduling will gradually reduce the resource utilization, this paper proposes an adaptive genetic algorithm (AGA) to rapidly perform the resource optimization. The crossover operator, mutation operator, adaptive probability and fitness function are designed, supporting multicast TSN flows. Finally, this paper conducts extensive simulations to validate the proposed algorithms. The results show that, comparing with traditional methods, the proposed CILP method can reduce the average runtime by up to 86.7% for static scheduling and realize real-time dynamic scheduling. The proposed AGA method can reduce the resource optimization runtime by up to 70% while improving the schedulability. The overall multi-stage scheduling mechanism could satisfy the VTSC scheduling requirements.

A MIQP Approach Based on Demand Response in Distribution Networks to Improve the Multi-Objective Function in the Presence of Renewable Energy Resources and Batteries to the Subway Systems

The increasing expansion of devices such as electric vehicle charging stations, renewable energy resources, and electric subways, will cause an imbalance between load and production in the distribution network, which will lead to an increase in power losses, a decrease or increase in voltage and ultimately impose more costs on the independent system operator (ISO). In this paper, we have presented a mixed integer quadratic programming (MIQP) optimization model to improve the distribution network performance in the presence of these devices. The proposed model is a mixed integer model including demand side management modeling, energy storage system, battery to subway (B2S) system, optimal control of on-load tap changer (OLTC), step voltage regulator (SVR), fossil generation resources, renewable energy and capacitors, and shunt reactors. The considered multi-objective function is a scenario-based stochastic model, which accurately models the uncertainties in renewable energy resources. Paying attention to the nature of the proposed model will guarantee globally optimal solutions. The proposed model runs on the standard network of 33 buses, and the simulation results guarantee the optimal and accurate performance of the proposed model. The simulation results demonstrate that the outage of three distributed generation units from the network causes an average increase of 55% in energy losses, and the outage of three renewable units leads to an increase in emissions of about 40%. Another point received from the results illustrated that with the implementation of the proposed model under any load level of the network, as well as the outage of fossil and renewable distributed generation (DG) resources, no load shedding will occur in the network during the 24 hours. Finally, due to the short execution run time, the proposed model can be used for real and online grids.

QNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids.

Protein sequence-based predictors of nucleic acid (NA)-binding include methods that predict NA-binding proteins and NA-binding residues. The residue-level tools produce more details but suffer high computational cost since they must predict every amino acid in the input sequence and rely on multiple sequence alignments. We propose an alternative approach that predicts content (fraction) of the NA-binding residues, offering more information than the protein-level prediction and much shorter runtime than the residue-level tools. Our first-of-its-kind content predictor, qNABpredict, relies on a small, rationally designed and fast-to-compute feature set that represents relevant characteristics extracted from the input sequence and a well-parametrized support vector regression model. We provide two versions of qNABpredict, a taxonomy-agnostic model that can be used for proteins of unknown taxonomic origin and more accurate taxonomy-aware models that are tailored to specific taxonomic kingdoms: archaea, bacteria, eukaryota, and viruses. Empirical tests on a low-similarity test dataset show that qNABpredict is 100 times faster and generates statistically more accurate content predictions when compared to the content extracted from results produced by the residue-level predictors. We also show that qNABpredict's content predictions can be used to improve results generated by the residue-level predictors. We release qNABpredict as a convenient webserver and source code at http://biomine.cs.vcu.edu/servers/qNABpredict/. This new tool should be particularly useful to predict details of protein-NA interactions for large protein families and proteomes.

Method validation and clinical application for the quantification of lopinavir, efavirenz, and ritonavir in breast milk using liquid chromatography tandem mass spectrometry.

A liquid chromatography tandem mass spectrometry (LC-MS/MS) method has been validated for quantification of three antiretroviral drugs (efavirenz [EFV], lopinavir [LPV], and ritonavir [RTV]) from human breast milk. The samples were extracted employing protein precipitation method using methanol as precipitating agent. The supernatant was evaporated and reconstituted before injecting into the chromatograph and separated on a biphenyl column. Calibration curves for the three tested antiretroviral drugs were linear (r ≥ 0.999) over the range examined. The inter- and intra-day coefficients of variation (CV) were ≤15% for efavirenz, lopinavir, and ritonavir. Mean recovery ranged from 96% to 105% and no major matrix effects were observed. This validated LC-MS/MS method was efficiently applied to determine EFV, LPV, and RTV concentrations in breast milk from Human Immunodeficiency Virus (HIV)-positive breastfeeding mothers. This assay requires a simple sample processing method with a short run time, making it well suited for high-throughput routine clinical or research purposes.

A Method for Quantitative Determination of Amino Acids and Organic Acids in Trace Random Urine and Preliminary Application in Research on Vitamin B12 Nutritional Status and Related Metabolism

Objective To develop a method for the quantification of amino acids and organic acids in trace urine by high performance liquid chromatography-tandem mass spectrometry.Methods Random urine samples(10 μl each)were precipitated by acetonitrile and underwent derivatization with 3 mol/L HCl in n-butanol.The analytes were separated by ACE Excel 2 AQ column(50×2.1 mm,2 μm).Electrospray ionization in positive ion mode was carried out and the analytes were detected in multiple reaction monitoring mode.According to existing guidelines,the method was systematically evaluated in terms of sensitivity,specificity,accuracy,precision,recovery,matrix effect,and stability.Then,the established method was employed to detect 19 target compounds in urine samples from 70 healthy children,27 children with suspected vitamin B12 deficiency,and 3 children with cblC type methylmalonic acidemia.Results The lower limit of quantification of the method for the 19 compounds ranged from 0.01 μmol/L to 1.00 μmol/L,and the calibration curves were linear,R2>0.990.The method showed good accuracy with relative error less than ±15% and the intra-day and intra-day precision less than 15%.The run time was 8 min.No obvious matrix effect was detected except for arginine,and the recovery ranged from 80.20% to 114.97%.The samples were stable after 8 h at room temperature and 3 freeze-thaw cycles.The measured values of the compounds in the urine of healthy children were within the children's reference intervals published by Labcorp.The levels of methylmalonic acid(P=0.030)and homocysteine(P<0.001)in the urine samples of children with suspected vitamin B12 deficiency were higher than those in healthy children.The levels of methylmalonic acid,methylcitric acid,and homocysteine in the urine samples of children with cblC type methylmalonic acidemia were 5.14-76.52 times higher than the median levels of healthy children. Conclusions The method established in this study has small sample demand and short run time,which can accurately quantify the levels of amino acids and metabolites in the urine of children.Moreover,it can provide data support for related studies about the metabolic characteristics of urine amino acids and their metabolites in children with vitamin B12 deficiency.

Fourier Ptychographic Microscopy Reconstruction Method Based on Residual Transfer Networks

Fourier ptychographic microscopy reconstruction mostly adopts the traditional alternating iterative phase recovery method and optimization method, which has high computational complexity, high redundancy of image acquisition data, low reconstruction quality and high time consumption. In this paper, the model of residual transfer networks based on Resnet152 is proposed for Fourier ptychographic microscopy reconstruction, the learning process of deep convolution neural network is introduced, and the image reconstruction method based on deep learning realizes the end-to-end reconstruction of low-resolution images to high-resolution images. Through comparative experiments and analysis, the residual network can overcome the gradient explosion, make the feature information more complete and efficient, and the incremental up-sampling reconstruction network has higher image quality, lower computational complexity and shorter running time.

Comparison of the efficacy of Natural Language Processing Algorithms at classifying Cyberbullying Tweets

Machine Learning is frequently used to predict and classify data. Natural Language Processing (NLP) uses machine learning to classify strings of words. There are many different machine learning models that can be used for NLP, with three main categories being regression, decision tree, and neural net models. Each has their own advantages and drawbacks. After being trained and tested on a set of tweets concerning cyberbullying, Logistic Regression, XGboost, and Long Short-Term Memory (LSTM) were compared in terms of several metrics, including accuracy, recall, precision, and f1-score. Afterwards, the metrics were considered in combination with model runtime and complexity to determine which model was most appropriate for the given dataset and other similar datasets. Logistic Regression was found to lack sufficient complexity to properly classify the data. LSTM had worse metrics than XGboost and had significantly higher complexity and runtime. XGboost performed best, with the highest metrics and relatively short runtime.

A high-throughput LC-MS/MS method for simultaneous determination of isoniazid, ethambutol and pyrazinamide in human plasma.

Tuberculosis (TB) remains a challenging global infectious disease, mainly affecting the lungs. First-line anti-TB drugs play a crucial role in slowing down the rapid spread of TB. In addition, the patient might benefit from therapeutic drug monitoring since it has become an accepted clinical tool for optimizing TB treatment. A simple and sensitive liquid chromatography/tandem mass spectrometry method was developed to monitor the plasma level of isoniazid, ethambutol and pyrazinamide in plasma samples. A one-step extraction procedure using an Ostro™ plate was applied, and extracts were analyzed by gradient elution followed by detection on a mass spectrometer by multiple reaction monitoring mode. The analytes were separated within 4.2min and over the concentration range of 0.2-10μg/ml for isoniazid and ethambutol and 1-65 μg/ml for pyrazinamide. The method was successfully validated according to the European Medicine Agency guideline for the selectivity, linearity and lower limit of detection, precision and accuracy, matrix effect, extraction recovery, carryover, dilution integrity and stability, and applied for quantification of analytes in clinical samples from TB patients. The presented method allows sensitive and reproducible determination of selected anti-TB drugs with advantages such as low sample volume requirement, short run time of analysis, one-step sample preparation procedure with capabilities for phospholipids removal, and a low quantification limit as well as a high degree of selectivity.

A Mixed-Heuristic Quantum-Inspired Simplified Swarm Optimization Algorithm for scheduling of real-time tasks in the multiprocessor system

Real-time task scheduling in multiprocessor (MP) systems has become more critical. As quantum computing advances, scholars have proposed different Quantum-Inspired (QI) Optimization Algorithms to optimize real-time task scheduling problems. In this research, we first take advantage of the Simplified Swarm Optimization Algorithm (SSO) and then propose a Quantum-Inspired Simplified Swarm Optimization (QISSO) to not only overcome the early convergence problem but also enhance the exploration of solution space without premature. Furthermore, we propose the Mixed-Heuristic QISSO for real-time task scheduling in the MP system to improve the scheduling. The proposed MHQISSO achieves the best average percentage of success within the shortest running time in five different size task sets according to two scenarios, Earliest Deadline First (EDF) and Shortest Computational Time First (SCTF).

The what , The from , and The to : The Migration Games in Deduplicated Systems

Deduplication reduces the size of the data stored in large-scale storage systems by replacing duplicate data blocks with references to their unique copies. This creates dependencies between files that contain similar content and complicates the management of data in the system. In this article, we address the problem of data migration, in which files are remapped between different volumes as a result of system expansion or maintenance. The challenge of determining which files and blocks to migrate has been studied extensively for systems without deduplication. In the context of deduplicated storage, however, only simplified migration scenarios have been considered. In this article, we formulate the general migration problem for deduplicated systems as an optimization problem whose objective is to minimize the system’s size while ensuring that the storage load is evenly distributed between the system’s volumes and that the network traffic required for the migration does not exceed its allocation. We then present three algorithms for generating effective migration plans, each based on a different approach and representing a different trade-off between computation time and migration efficiency. Our greedy algorithm provides modest space savings but is appealing thanks to its exceptionally short runtime. Its results can be improved by using larger system representations. Our theoretically optimal algorithm formulates the migration problem as an integer linear programming (ILP) instance. Its migration plans consistently result in smaller and more balanced systems than those of the greedy approach, although its runtime is long and, as a result, the theoretical optimum is not always found. Our clustering algorithm enjoys the best of both worlds: its migration plans are comparable to those generated by the ILP-based algorithm, but its runtime is shorter, sometimes by an order of magnitude. It can be further accelerated at a modest cost in the quality of its results.

Liquid chromatography–tandem mass spectrometry method for the bioanalysis of N,N-dimethyltryptamine (DMT) and its metabolites DMT-N-oxide and indole-3-acetic acid in human plasma

The indole alkaloid N,N-dimethyltryptamine (DMT) induces psychedelic effects in humans. In addition to ceremonial and recreational use, DMT is subject to clinical investigations. Sensitive bioanalytical methods are required to assess the pharmacokinetics of DMT and its metabolites in human plasma. Here, a high performance liquid chromatography–tandem mass spectrometry (LC–MS/MS) method for the quantification of DMT and its major metabolites indole-3-acetic acid (IAA) and DMT-N-oxide (DMT-NO) was developed and validated. As IAA is an endogenous component of human plasma, 13C6-IAA was used to determine IAA concentrations. After simple protein precipitation with methanol, analytes were separated on a pentafluorophenyl column. A gradient consisting of 0.1% (v/v) formic acid in a methanol–water mixture was applied for analyte separation. The analytes were detected by positive electrospray ionization followed by multiple reaction monitoring. The calibration range of the assay was 0.25–250 ng/mL for DMT, 0.1–100 ng/mL for DMT-NO, and 25–25,000 ng/mL for 13C6-IAA. The intra- and inter-assay accuracy was 93–113% for all analytes at all quality control levels, with coefficient of variation ≤ 11%. All analytes were stable under storage conditions relevant for the analysis of large batches of study samples. The validated method was capable of assessing pharmacokinetic (PK) parameters of DMT and its metabolites in study participants intravenously perfused with 1 mg/min DMT for 90 min. Overall, the developed method is easy-to-use, has a short run time, and qualifies for PK and metabolism studies of DMT in clinical settings.