Data Acquisition Research Articles

Review and Practical Guide for Getting Started With Single-Cell Proteomics.

Single-cell proteomics (SCP) has advanced significantly in recent years, with new tools specifically designed for the preparation and analysis of single cells now commercially available to researchers. The field is sufficiently mature to be broadly accessible to any lab capable of isolating single cells and performing bulk-scale proteomic analyses. In this review, we highlight recent work in the SCP field that has significantly lowered the barrier to entry, thus providing a practical guide for those who are newly entering the SCP field. We outline the fundamental principles and report multiple paths to accomplish the key steps of a successful SCP experiment including sample preparation, separation, and mass spectrometry data acquisition and analysis. We recommend that researchers start with a label-free SCP workflow, as achieving high-quality and quantitatively accurate results is more straightforward than label-based multiplexed strategies. By leveraging these accessible means, researchers can confidently perform SCP experiments and make meaningful discoveries at the single-cell level.

Rotor angle stability of a microgrid generator through polynomial approximation based on RFID data collection and deep learning.

The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients. Redundant data, like as vibrations or noise signals, can cause delays in defect diagnosis and decrease diagnostic accuracy. This problem is addressed by integrating RFID technology. RFID technology could potentially be used with a hybrid CNN-LSTM model to develop a sophisticated fault diagnostic system. This entails identifying fault characteristics through the use of signal processing techniques and feature extraction methods, such as the Fourier transform and time-domain statistical features. In addition, we use Total Harmonic Distortion (THD) to reduce superfluous data. The suggested techniques significantly increase fault detection efficiency and precision, outperforming existing techniques with a 0.94 classification accuracy. An extensive case study on an IEEE 3-machine 9-bus system is used to illustrate its efficacy, showing observable improvements in fault detection speed and accuracy that make microgrid operations safer and more dependable.

Automatic Identification and Suppression of Random Noise and Methods for Profile Splicing in the Sub-Bottom Profile of Deep Water

The complex topography of deep sea presents numerous challenges for the accurate exploration of sub-bottom profiles. These include real-time tracking of seafloor reflectors, acquisition and storage of deep-sea long-term reflection data, and splicing of successive profiles. Based on the actual survey data of deep sea, we have developed automatic positioning and noise suppression algorithms, namely the double-difference threshold of proximity points. Furthermore, we have created automatic algorithms, namely content expansion and group data moving, based on extremum in seafloor’s depth. These have been designed to automatically suppress the random noise and effectively splice the sub-bottom profile data in deep water. The aforementioned processing techniques facilitate the enhancement of the quality of deep-water sub-bottom profile data, thereby enabling the provision of a comprehensive and successively long profile for interpretation in the context of deep-water sub-bottom profile data.

Analysis and Prediction of Chymotrypsin Substrate Preferences through Large Data Acquisition with Target-Free mRNA Display.

Oral delivery of peptide therapeutics is limited by degradation by gut proteases like chymotrypsin. Existing databases of peptidases are limited in size and do not enable systematic analyses of protease substrate preferences, especially for non-natural amino acids. Thus, stability optimization of hit compounds is time and resource intensive. To accelerate the stability optimization of peptide ligands, we generated large datasets of chymotrypsin-resistant peptides via mRNA display to create a predictive model for chymotrypsin-resistant sequences. Through analysis of enriched motifs, we recapitulate known chymotrypsin cleavage sites, reveal positionally dependent effects of monomers on peptide cleavage, and report previously unidentified protective and destabilizing residues. We then developed a machine-learning-based modelpredicting peptide resistance to chymotrypsin cleavage and validated both model performance and the NGS experimental data by measuring chymotrypsin half-lives for a subset of peptides. Finally, we simulated stability predictions on non-natural amino acids through a leucine hold-out model and observed robust performance. Overall, we demonstrate the utility of mRNA display as a tool for big data generation and show that pairing mRNA display with machine learning yields valuable predictions for chymotrypsin cleavage. Expansion of this workflow to additional proteases could provide complementary predictive models that focus future peptide drug discovery efforts.

The Evaluation and Learning of Web Crawler Technology in the Field of Digital Media Art

With the rapid advancement of digital technology, digital media art has progressively emerged as a novel darling in the contemporary art market, primarily due to its distinctive interactivity, generativity, and digital distribution methods.The appraisal of market value is crucial in the transaction of digital art, and web crawling technology presents an innovative approach for data acquisition and processing. This study is dedicated to exploring the convergence of digital media art and web crawling technology, conducting an in-depth investigation on the application of web crawlers in the determination of market value for digital art.In the context of developing a theoretical framework, a novel value assessment model is proposed by integrating the features of the digital art market with established value assessment theories. On this basis, a value assessment tool targeted at the digital media art market was developed.This tool integrates multiple factors such as historical transaction data of artworks, artist backgrounds, work style, and media exposure, and employs machine learning algorithms to conduct market value assessment of artworks.Case analyses have also confirmed the accuracy of the assessment results and the practicality of the assessment model.Results show that this assessment tool not only provides powerful market guidance for art investors and collectors but also offers a pricing decision basis for digital art trading platforms.This research not only provides new perspectives and practical tools for the assessment of digital art but also makes a valuable exploration into the application of web crawling technology in the acquisition of art data information.It is of positive significance for the value discovery of digital media art and the long-term healthy development of the art market.

Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II

Abstract New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge Machine Learning (EdgeML) addresses this challenge by integrating
Machine Learning (ML) algorithms with edge computing, enabling real-time data processing directly at the point of data generation. EdgeML is particularly beneficial for environments where immediate decisions are required, or where bandwidth and
storage are limited. In this paper, we demonstrate a high-speed configurable ML model in a fully customizable EdgeML system using a Field Programmable Gate Array (FPGA). Our demonstration focuses on an angular array of electron spectrometers,
referred to as the ’CookieBox,’ developed for the Linac Coherent Light Source II (LCLS-II) project. The EdgeML system captures 51.2 Gbps from a 6.4 GS/s analog to digital converter and is designed to integrate data pre-processing and ML inside an
FPGA. Our implementation achieves an inference latency of 0.2 μs for the ML model, and a total latency of 0.4 μs for the complete EdgeML system, which includes preprocessing, data transmission, digitization, and ML inference. The modular design of
the system allows it to be adapted for other instrumentation applications requiring low-latency data processing.

Unveiling the Potential of Ultraviolet Fluorescence Imaging as a Versatile Inspection Tool: Insights from Extensive Photovoltaic Module Inspections in Multi‐MWp Photovoltaic Power Stations

UV fluorescence imaging (UVF) has the potential to grow into a powerful, informative, and economically attractive inspection method for photovoltaic (PV) power stations. UVF demonstrates the ability to indicate differences in polymer types and degradation states in backsheets and encapsulation materials of PV modules. The data acquisition rate of UVF measurements is 10 to 15 times higher than that achieved by state‐of‐the‐art near‐infrared spectroscopy, enabling the mapping of the bill of materials (BoMs) and degradation state for every module in large PV arrays. Combinations of UVF with vibrational spectroscopies allow the polymer BoMs to be recognized and correlated with aging phenomena such as metal corrosion or potential induced degradation. UVF imaging can also be used for conventional visualization of nonmaterial‐related anomalies, such as hot cells or cell cracks. The low purchase cost of the equipment makes UVF an affordable and high‐throughput diagnostic method yielding comprehensive information that would otherwise only be accessible by combining several slower and more laborious methods. Automated UVF image analysis and refinements of correlations between different BoMs and UVF pattern geometry can further unlock the potential of UVF as a straightforward tool accessible to all stakeholders and promote proactive strategies for the optimization of the reliability and performance of PV power stations.

Streaming Large-Scale Microscopy Data to a Supercomputing Facility.

Data management is a critical component of modern experimental workflows. As data generation rates increase, transferring data from acquisition servers to processing servers via conventional file-based methods is becoming increasingly impractical. The 4D Camera at the National Center for Electron Microscopy generates data at a nominal rate of 480 Gbit s-1 (87,000 frames s-1), producing a 700 GB dataset in 15 s. To address the challenges associated with storing and processing such quantities of data, we developed a streaming workflow that utilizes a high-speed network to connect the 4D Camera's data acquisition system to supercomputing nodes at the National Energy Research Scientific Computing Center, bypassing intermediate file storage entirely. In this work, we demonstrate the effectiveness of our streaming pipeline in a production setting through an hour-long experiment that generated over 10 TB of raw data, yielding high-quality datasets suitable for advanced analyses. Additionally, we compare the efficacy of this streaming workflow against the conventional file-transfer workflow by conducting a postmortem analysis on historical data from experiments performed by real users. Our findings show that the streaming workflow significantly improves data turnaround time, enables real-time decision-making, and minimizes the potential for human error by eliminating manual user interactions.

BN SO58 - Patient Reported Outcome Measures in Hiatal Surgery: A Feasibility Study

Abstract Background Hiatal surgery is predominantly a quality-of-life procedure rather than a lifesaving intervention. Whilst procedural complications are well understood, they do not necessarily reflect the patient experience. Traditional indicators of patient care are insufficient. Patient Reported Outcome Measures (PROM) are robust indicators of care quality (1) and are being used across surgical specialities to benchmark success and identify areas for improvement (2). We present a three-year review of our PROM data to demonstrate feasibility of data collection. Method Patient reported outcome measure data were collected from the National Hiatal Surgery Registry (3) for our benign centre over a three-year period (November 2021-January 2024). Consent was obtained from all patients for research purposes. Surgical procedures included: anti-reflux surgery, hiatus hernia repair and cardiomyotomy. All patients were asked to complete pre-operative quality of life surveys. Patients are automatically contacted through the registry at 6months, 1 year and then annually for five years and prompted to complete quality of life surveys. Ekhardt score is used in achalasia. GORD-QoL score was used for anti-reflux procedures, with the Hernia-QoL score for hiatal hernia. Results Data were collected for 20 patients. Mean BMI was 28.2. All completed preoperative surveys. At six months there was attrition of one patient, who has since completed 1-year and 2-year data. At the time of writing, six patients have completed 6month data, 5 patients have completed 1-year data and 3 patients have completed 2-year data. Mean Ekhardt score was 9.25 preoperatively; at six months mean score was 0. Five patients underwent LINX, eleven underwent fundoplication; one Dor, ten Toupet. Mean pre-operative quality of life score was 31.1; at six months mean score was 15.5. At one year this was 4. Conclusion The National Hiatal Surgery Registry facilitates easy acquisition of surgeon specific PROM data over a short- and long-term period. Patient attrition is low, demonstrating acceptability. Our data demonstrate significantly improved patient reported quality of life postoperatively for all hiatal surgery.

Effective Rapid Fluorescence Lifetime Imaging of the Brain: A Novel Approach Using Upconversion Photoluminescence Lifetime Based on Gate-Width Acquisition.

The rapid lifetime imaging of upconversion photoluminescence is becoming increasingly popular in biosensing, anticounterfeiting, optical thermometry, and multiplex imaging. However, existing Rapid Lifetime Determination (RLD) techniques are limited in their ability to integrate contiguous, overlapping, and discrete windows into a single measurement, hindering accurate fluorescence lifetime retrieval. This study introduces a new data acquisition method using three adjustable gates in a single measurement to enhance resolution. We apply this method in rapid upconversion fluorescence lifetime imaging to visualize capillary networks and map pH levels based on intensity and lifetime differences in mouse brain vasculature. By enhancing brightness using NaYbF4@NaYF4,Er,Tm@NaYF4 nanoparticles, we achieve effective brain imaging. Monte Carlo simulations demonstrate a relative standard deviation of less than 0.4% for fluorescence durations spanning from 1 to 20 ns. This method provides a fast, high-contrast solution for multiplex brain imaging, addressing the limitations of slow data collection and poor accuracy in existing RLD techniques.

Climatic Specific Energy Rating Analysis of Outdoor PV Field Data

ABSTRACTThe IEC 61853 standard series define the Climatic Specific Energy Rating (CSER), which can be used within the photovoltaic (PV) community as a reliable and accurate tool to differentiate between PV modules performance for different reference climates. The CSER is assumed to represent the expected PV device operation under real‐world conditions. The rating model combines a set of measurements on modules with a numerical approach. So far, research has mainly focused on the implementation of algorithms to determine the input parameters and to calculate the CSER values based on indoor measurement performance data. There is lack of information and technical specification how to obtain these parameters from outdoor PV field data in natural sunlight. In the present study, we first estimated the CSER parameter (according to IEC 61853‐3) for a PV module tested indoor using input values measured or defined according to IEC 61853‐1&2 with associated measurement uncertainties. Then, we analysed long‐term outdoor field data of the same device for a period of 36 consecutive months in order to understand how the length and seasonality of the data acquisition period impacts the reliability of irradiance and temperature power matrix values, particularly in relation to CSER calculations. Uncertainties estimations for the outdoor procedure have been also defined for rating accuracy. The PV outdoor performance has been validated against its indoor counterpart. The results concluded that a period of 9 or 12 months provides a good compromise between accuracy, time and cost‐effectiveness. The time period may be reduced to 6 months if the meteorological conditions are sufficiently diverse in this time frame. The study might raise worthwhile discussion regarding the revision of the IEC 61853‐1 for determining the (G‐T) matrix of PV modules from outdoor measurements under natural sunlight. It could enhance important insights to the PV community since the CSER parameter may become a compulsory requirement for each PV module produced, imported or sold in Europe if the potential European Union (EU) Ecodesign Directive and the Energy Labelling Regulation will be implemented.

Digitization of Dentate and Edentulous Maxillectomy and Mandibulectomy Defects with Three Different Intraoral Scanners: A Comparative In Vitro Study

Objectives: The objective of this study was to compare the trueness and precision of three intraoral scanners (IOSs) for the digitization of dentate and edentulous maxillectomy and mandibulectomy defects in artificial models. Methods: Four representative defect models—a dentate and an edentulous maxillectomy model and a dentate and an edentulous mandibulectomy model—were used for digital scanning. After a reference scan of each model, they were scanned with three IOSs: CEREC AC Omnicam, True Definition, and cara TRIOS 3. For comparison, five conventional impressions with a polysiloxane material were taken and digitized with a laboratory scanner. The obtained data were evaluated with three-dimensional (3D) inspection software and superimposed with the reference scan data by using a best-fit algorithm. The mean absolute 3D deviations of the IOS compared to the reference data (trueness) and when comparing the datasets within the IOS (precision) were analyzed. Linear mixed models and multiple pairwise comparisons were used for statistical analyses. Results: The overall comparison of the four evaluated procedures for data acquisition showed a significant difference in trueness (p < 0.0001) and precision (p < 0.0001). The average mean trueness of the IOSs ranged from 32.17 to 204.43 µm, compared to 32.07 to 64.85 µm for conventional impressions. Here, the conventional impression and cara TRIOS 3 performed the most precisely with no significant difference. CEREC AC Omnicam achieved the worst precision. Conclusions: Using a suitable intraoral scanner, defective jaws even without teeth could be captured in satisfying accuracy. This shows the possibility to use an intraoral scanner for maxillofacial defect patients and gives a vision of using digital technology in maxillofacial prosthetics.

The Data Acquisition System for Phase-III of the BeEST Experiment

The Data Acquisition System for Phase-III of the BeEST Experiment

Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations

IntroductionOver the past few decades, numerous researchers have explored the application of machine learning for assessing children’s neurological development. Developmental changes in the brain could be utilized to gauge the alignment of its maturation status with the child’s chronological age. AI is trained to analyze changes in different modalities and estimate the brain age of subjects. Disparities between the predicted and chronological age can be viewed as a biomarker for a pathological condition. This literature review aims to illuminate research studies that have employed AI to predict children’s brain age.MethodsThe inclusion criteria for this study were predicting brain age via AI in healthy children up to 12 years. The search term was centered around the keywords “pediatric,” “artificial intelligence,” and “brain age” and was utilized in PubMed and IEEEXplore. The selected literature was then examined for information on data acquisition methods, the age range of the study population, pre-processing, methods and AI techniques utilized, the quality of the respective techniques, model explanation, and clinical applications.ResultsFifty one publications from 2012 to 2024 were included in the analysis. The primary modality of data acquisition was MRI, followed by EEG. Structural and functional MRI-based studies commonly used publicly available datasets, while EEG-based studies typically relied on self-recruitment. Many studies utilized pre-processing pipelines provided by toolkit suites, particularly in MRI-based research. The most frequently used model type was kernel-based learning algorithms, followed by convolutional neural networks. Overall, prediction accuracy may improve when multiple acquisition modalities are used, but comparing studies is challenging. In EEG, the prediction error decreases as the number of electrodes increases. Approximately one-third of the studies used explainable artificial intelligence methods to explain the model and chosen parameters. However, there is a significant clinical translation gap as no study has tested their model in a clinical routine setting.DiscussionFurther research should test on external datasets and include low-quality routine images for MRI. T2-weighted MRI was underrepresented. Furthermore, different kernel types should be compared on the same dataset. Implementing modern model architectures, such as convolutional neural networks, should be the next step in EEG-based research studies.

Implementation of Wastewater Monitoring System using Low-Cost Water Sensor and IoT-based SCADA

Wastewater monitoring is crucial for ensuring the wastewater quality is in the allowable range. This paper presents the implementation of a wastewater monitoring system using low-cost water sensors and Internet of Things (IoT)-based Supervisory Control and Data Acquisition (SCADA). The low-cost water sensor comprises the pH, temperature, turbidity, and Dissolved Oxygen (DO) sensors. The sensors are connected to the ESP32 microcontroller that is equipped with the WiFi module for communicating with the master unit, namely the Haiwell CBOX. Since the Haiwell CBOX is an IoT-based device, it is connected to the IoT cloud server. Thus, it can be accessed remotely through the Internet. The experimental results show that the user-friendly graphical interface is easy to develop and provides the user with useful and helpful information for monitoring and analyzing wastewater parameters. Further, the communication time interval of four seconds between the ESP32 and CBOX achieves a stable connection.

Abstract 4113997: Semaglutide Improves Myocardial Perfusion and Performance in a Large Animal Model of Coronary Artery Disease

Background: Coronary artery disease (CAD) is the leading cause of death worldwide. It imposes an enormous symptomatic burden on patients, leaving many with residual disease despite optimal procedural therapy, and up to 1/3 rd with debilitating angina amenable neither to procedures, nor to current pharmacologic options. Semaglutide, a glucagon-like peptide 1 agonist originally approved for management of diabetes, has garnered substantial attention for its capacity to attenuate cardiovascular risk. Although subgroup analyses in patients indicate promise, studies explicitly designed to isolate the impact of semaglutide on the sequelae of CAD are lacking. Methods: Yorkshire swine (n=17) underwent placement of an ameroid constrictor around the left circumflex coronary artery to induce CAD. Oral semaglutide was initiated postoperatively at 1.5 mg and scaled up in 2 weeks to 3 mg in treatment animals (SEM, n=8) for a total of 5 weeks, while control animals (CON, n=9) received no drug. All then underwent myocardial harvest with acquisition of perfusion and functional data using microsphere injection and pressure-volume loop catheterization. Immunoblotting, immunohistochemistry, and immunofluorescence were performed on the most ischemic myocardial segments for mechanistic elucidation. Results: SEM animals exhibited improved left ventricular ejection fraction, both at rest and during rapid myocardial pacing to 150 bpm (both p<0.03), accompanied by increased perfusion to the most ischemic myocardial region at rest and during rapid pacing (both p<0.03); reduced perivascular and interstitial fibrosis (both p <0.03); and apoptosis (p=0.008). These changes were associated with increased activation of the endothelial-protective AMPK pathway (p=0.005), coupled with downstream increases in endothelial nitric oxide synthase (p=0.014). Conclusions: This study is the first to reveal the capacity of oral semaglutide to augment cardiac function in the chronically ischemic heart in a highly translational large animal model, likely through AMPK-mediated improvement in endothelial function and perfusion to the ischemic myocardium.

Development of an internet-of-things-based controlled-source ultrasonic audio frequency electromagnetic receiver

Abstract. Electromagnetic exploration, characterized by its low cost, wide applicability, and high operational efficiency, finds extensive applications in fields such as oil and gas exploration, mineral prospecting, and engineering geology. Traditional controlled-source electromagnetic detection methods are typically confined to operating frequencies below 250 kHz, resulting in insufficient detection accuracy for applications such as shallow- and intermediate-depth exploration, thereby constraining their performance in high-resolution imaging. To address these challenges, we propose a controlled-source ultrasonic audio frequency electromagnetic receive system based on the internet of things (IoT). We investigate cascaded digital filtering and sampling techniques to extend the receiver's sampling rate range, thereby elevating the operating frequency of controlled-source electromagnetic acquisition from the conventional maximum of 250 kHz to 1 MHz. The receiver achieves a sampling rate of up to 2.5 MHz, comprising three magnetic field measurement channels and two electric field measurement channels. The instrument is compact, lightweight, and capable of real-time data storage locally and real-time data transmission to an upper computer. Additionally, IoT technology is introduced, leading to the design of a cloud-based real-time remote control and data acquisition scheme. Experimental results demonstrate the stability of the instrument, meeting the requirements of field exploration.

Deciphering Isotopic Fine Structures of Silylated Compounds in Gas Chromatography-Vacuum Photoionization Orbitrap Mass Spectrometry of Bio-Oils.

We introduce vacuum resonance-enhanced multiphoton ionization (REMPI) with high-resolution Orbitrap Fourier transform mass spectrometry (FTMS) for analyzing silylated polar compounds. UV laser radiation at 248 nm sensitively and selectively targets aromatic constituents, while high-resolution mass spectrometry (HRMS) enables high-performance mass spectrometric detection. This workflow enhances the detection confidence of polar constituents by identifying unique isotopologue patterns, including at the isotopic fine structure (IFS) level, in analytical standards and complex bio-oils. A direct and derivatized gas chromatography (GC) procedure on a polar standard component mixture allowed us to explore the general ionization and detection characteristics of REMPI FTMS. HRMS enabled the examination of the complex isotopologue profiles, revealing distinct patterns for the CHOxSiy-class compounds. Particularly in complex mixtures, this isobaric/isonucleonic complexity exceeded the classical mass resolution capabilities of the employed Orbitrap D30 series and prompted the usage of prolonged transients via an external data acquisition system. This procedure substantially improved mass spectrometric results by recording the unreduced time-domain transient data for up to 2 s. Notably, the ability to distinguish diagnostic isotopic pairs, such as 12C/29Si vs 13C/28Si with a mass split of ∼3.79 mDa and 13C12C/28Si29Si vs 13C2/28Si2, with an approximate mass difference of ∼3.32 mDa, demonstrates a significant and expected performance improvement. Finally, we benchmark the GC HRMS methodology to identify silylated oxygenated and nitrogen-containing constituents in ultracomplex bio-oil samples. The presented approach of utilizing the silicon isotope pattern and unique isotopologue mass splits for increasing attribution confidence can be applied beyond bio-oils toward the general GC analyses of polar oxygenates.

UHPLC-QTOFMS Urine Drug Screening With Dilute-and-Shoot Sample Preparation and Vacuum-Insulated Probe-Heated Electrospray Ionization.

We developed a method for comprehensive urine drug screening by applying dilute-and-shoot extraction and vacuum-insulated probe-heated electrospray ionization with ultra-high performance liquid chromatography high-resolution quadrupole time-of-flight mass spectrometry (DS-UHPLC-VIP-HESI-QTOFMS). The method involved five-fold post-hydrolysis dilution of urine samples and chromatography on a C18 UHPLC column prior to QTOFMS analysis. The recently introduced VIP-HESI ion source was chosen due to its enhanced ionization efficiency and compatibility with UHPLC-QTOFMS. Extensive data was acquired in positive ion mode with a low collision energy (7 eV) and an elevated collision energy (30 eV), using the broadband collision-induced dissociation data acquisition scan mode that continuously generated high-resolution and accurate mass for parent and fragment qualifier ions, and parent ion isotopic patterns. Compound identification was performed against an in-house database with 1263 compound entries, using an automated post-run reverse target database search with preset identification criteria. Method validation with 56 different drugs showed acceptable results for the limit of identification (median 5 ng/mL), matrix effects (70-130%), repeatability of retention times (< 1%), mass accuracy (< 1 mDa), as well as for specificity and stability. As compared with an established UHPLC-QTOFMS method relying on solid-phase extraction and conventional electrospray ionization, DS-UHPLC-VIP-HESI-QTOFMS produced comparable results from authentic clinical urine samples for most drugs, but showed clearly improved detectability for pregabalin, gabapentin, and ritalinic acid. We anticipate that the new method will be a step forward for laboratories performing routine urine drug screening due to its fast turnaround time, reduced manual workload, cost efficiency, and broad substance coverage.

Autonomous Dissociation-type Selection for Glycoproteomics Using a Real-Time Library Search.

Tandem mass spectrometry (MS/MS) is the gold standard for intact glycopeptide identification, enabling peptide sequence elucidation and site-specific localization of glycan compositions. Beam-type collisional activation is generally sufficient for N-glycopeptides, while electron-driven dissociation is crucial for site localization in O-glycopeptides. Modern glycoproteomic methods often employ multiple dissociation techniques within a single LC-MS/MS analysis, but this approach frequently sacrifices sensitivity when analyzing multiple glycopeptide classes simultaneously. Here we explore the utility of intelligent data acquisition for glycoproteomics through real-time library searching (RTLS) to match oxonium ion patterns for on-the-fly selection of the appropriate dissociation method. By matching dissociation method with glycopeptide class, this autonomous dissociation-type selection (ADS) generates equivalent numbers of N-glycopeptide identifications relative to traditional beam-type collisional activation methods while also yielding comparable numbers of site-localized O-glycopeptide identifications relative to conventional electron transfer dissociation-based methods. The ADS approach represents a step forward in glycoproteomics throughput by enabling site-specific characterization of both N-and O-glycopeptides within the same LC-MS/MS acquisition.