Precise Quantitation Research Articles

Ultra-fast label-free quantification and comprehensive proteome coverage with narrow-window data-independent acquisition.

Mass spectrometry (MS)-based proteomics aims to characterize comprehensive proteomes in a fast and reproducible manner. Here we present the narrow-window data-independent acquisition (nDIA) strategy consisting of high-resolution MS1 scans with parallel tandem MS (MS/MS) scans of ~200 Hz using 2-Th isolation windows, dissolving the differences between data-dependent and -independent methods. This is achieved by pairing a quadrupole Orbitrap mass spectrometer with the asymmetric track lossless (Astral) analyzer which provides >200-Hz MS/MS scanning speed, high resolving power and sensitivity, and low-ppm mass accuracy. The nDIA strategy enables profiling of >100 full yeast proteomes per day, or 48 human proteomes per day at the depth of ~10,000 human protein groups in half-an-hour or ~7,000 proteins in 5 min, representing 3× higher coverage compared with current state-of-the-art MS. Multi-shot acquisition of offline fractionated samples provides comprehensive coverage of human proteomes in ~3 h. High quantitative precision and accuracy are demonstrated in a three-species proteome mixture, quantifying 14,000+ protein groups in a single half-an-hour run.

Assessing Matrix Solid-Phase Dispersion Extraction Strategies for Determining Bisphenols and Phthalates in Gilthead Sea Bream Samples

Microplastics (MPs) and nanoplastics (NPs) are widely spread in the environment, generating significant concern due to their potential impact on environmental health. Marine species usually ingest plastic fragments, mistaking them for food. Many toxic compounds, such as plastic additives that are not chemically bound to the plastic matrix, can be released from MPs and NPs and reach humans via the food chain. This paper highlights the development and validation of a straightforward solid–liquid extraction clean-up procedure in combination with a matrix solid-phase dispersion method using high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS) detection, enabling facile, precise, and reliable identification and quantitation of a total of six bisphenols and phthalates in gilthead sea breams. Under the optimized conditions, the developed method showed good linearity (R2 > 0.993) for all target compounds. The recoveries obtained were between 70 and 92%. The relative standard deviations (RSDs) for reproducibility (inter-day) and repeatability (intra-day) were less than 9% and 10%, respectively. The limit of detection (LOD) and limit of quantification (LOQ) for the target compounds ranged from 0.11 to 0.68 µg/kg and from 0.37 to 2.28 µg/kg, respectively. A new, efficient extraction methodology for the determination of BPA, BPS, BPF, DBP, DEP, and DHEP in gilthead seabream has been optimized and validated.

Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme.

In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430-670 m/z using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).

The Evolution and Potential of Precision Agriculture in China Anchored In "3S" Technology.

Agriculture serves as a linchpin for the national economy. With the accelerated evolution of information technology and emergent high-tech innovations, the digitization of agriculture has emerged as a pivotal trajectory for contemporary agricultural advancement. Conventional practices encompassing planting, fertilization, and harvesting are transitioning towards more quantitative, localized, and technologically-driven precision agriculture. Concurrently, with the global surge in "precision agriculture", Chinese academicians have progressively delved into its study. Over the past three decades, the "3S" technology-centric "precision agriculture" in China has witnessed commendable advancements, exemplified by innovations such as the Global Agricultural Remote Sensing Quick Report System and the Beidou Satellite Navigation System-based autonomic agricultural machinery operation. However, the modernization of Chinese agriculture exhibits certain limitations, including limited adoption, suboptimal integration, and a scarcity of high-caliber experts. Given the swift progression of Chinese scientific and economic domains, the "3S" technology is poised to surmount these challenges, enhancing the modernization of Chinese agriculture. Hence, "precision agriculture" anchored in "3S" technology retains significant growth prospects within China.

Multiplex, Quantitative, High-Resolution Imaging of Protein:Protein Complexes via Hybridization Chain Reaction.

Signal amplification based on the mechanism of hybridization chain reaction (HCR) facilitates spatial exploration of gene regulatory networks by enabling multiplex, quantitative, high-resolution imaging of RNA and protein targets. Here, we extend these capabilities to the imaging of protein:protein complexes, using proximity-dependent cooperative probes to conditionally generate a single amplified signal if and only if two target proteins are colocalized within the sample. HCR probes and amplifiers combine to provide automatic background suppression throughout the protocol, ensuring that even if reagents bind nonspecifically in the sample, they will not generate amplified background. We demonstrate protein:protein imaging with a high signal-to-background ratio in human cells, mouse proT cells, and highly autofluorescent formalin-fixed paraffin-embedded (FFPE) human breast tissue sections. Further, we demonstrate multiplex imaging of three different protein:protein complexes simultaneously and validate that HCR enables accurate and precise relative quantitation of protein:protein complexes with subcellular resolution in an anatomical context. Moreover, we establish a unified framework for simultaneous multiplex, quantitative, high-resolution imaging of RNA, protein, and protein:protein targets, with one-step, isothermal, enzyme-free HCR signal amplification performed for all target classes simultaneously.

Identification and quantitation of multiple variants in RNA virus genomes.

The goal of the study was to identify and characterize RNA virus variants containing mutations spread over genomic distances >5 kb. As proof of concept, high-quality viral RNA of the Dengue 2 component of Takeda's tetravalent dengue vaccine candidate (TDV-2) was used to develop a reverse transcription-polymerase chain reaction protocol to amplify a ∼5.3 kb cDNA segment that contains the three genetic determinants of TDV-2 attenuation. Unique molecular identifiers were incorporated into each viral cDNA molecule for PacBio library preparation to improve the quantitative precision of the observed variants at the attenuation loci. Following assay optimization, PacBio long-read sequencing was validated with multiple clone-derived TDV-2 revertant variants and four complex revertant mixtures containing various compositions of TDV-2 and revertant viruses. PacBio sequencing analysis correctly identified and quantified variant composition in all tested samples, demonstrating that TDV-2 revertants could be identified and characterized and supporting the use of this method in the differentiation and quantification of complex variants of other RNA viruses. Long-read sequencing can identify complex RNA virus variants containing multiple mutations on a single-genome molecule, which is useful for in-depth genetic stability and revertant detection of live-attenuated viral vaccines, as well as research in virus evolution to reveal mechanisms of immune evasion and host cell adaption.

Research on the Inventory Management in the Modern Business

Inventory management plays a pivotal role in modern business operations, impacting operational efficiencies, financial security, and competitive advantages. This essay explores the multidimensional field of inventory management, delving into seminal research articles. It shows how accounting methods and strategic decision-making interact, shaping operational pathways. Operations research guides inventory management strategies' effectiveness assessment. Creating a culture of continuous improvement based on empirical observations is equally crucial. Innovations, like structured Markov Decision Processes (MDPs) with convex cost functions, bridge theory and real-world applications. The essay also examines forecasting errors' implications when relying too heavily on predictions, highlighting optimal control techniques as resilience tools in volatile environments. Contemporary inventory strategies balance precision and adaptability, focusing on supply/demand synchronization. Empirical case studies exemplify efficient responses to market changes. Variability management in inventory frameworks offers insights into resource allocation across diverse product lines. Lastly, the essay deconstructs the strategic implications of asymmetric sales changes on inventory strategies. In a globalized, technologically dynamic age with shifting market forces, prudent inventory management is imperative. This essay showcases its significance with empirical wisdom and academic rigor, illuminating quantitative precision, strategic insight, and operational excellence in modern business environments.

Chlamydomonas as a model system to study cilia and flagella using genetics, biochemistry, and microscopy.

The unicellular green alga, Chlamydomonas reinhardtii, has played a central role in discovering much of what is currently known about the composition, assembly, and function of cilia and flagella. Chlamydomonas combines excellent genetics, such as the ability to grow cells as haploids or diploids and to perform tetrad analysis, with an unparalleled ability to detach and isolate flagella in a single step without cell lysis. The combination of genetics and biochemistry that is possible in Chlamydomonas has allowed many of the key components of the cilium to be identified by looking for proteins that are missing in a defined mutant. Few if any other model organisms allow such a seamless combination of genetic and biochemical approaches. Other major advantages of Chlamydomonas compared to other systems include the ability to induce flagella to regenerate in a highly synchronous manner, allowing the kinetics of flagellar growth to be measured, and the ability of Chlamydomonas flagella to adhere to glass coverslips allowing Intraflagellar Transport to be easily imaged inside the flagella of living cells, with quantitative precision and single-molecule resolution. These advantages continue to work in favor of Chlamydomonas as a model system going forward, and are now augmented by extensive genomic resources, a knockout strain collection, and efficient CRISPR gene editing. While Chlamydomonas has obvious limitations for studying ciliary functions related to animal development or organ physiology, when it comes to studying the fundamental biology of cilia and flagella, Chlamydomonas is simply unmatched in terms of speed, efficiency, cost, and the variety of approaches that can be brought to bear on a question.

Analytical validation of a novel comprehensive genomic profiling informed circulating tumor DNA monitoring assay for solid tumors.

Emerging technologies focused on the detection and quantification of circulating tumor DNA (ctDNA) in blood show extensive potential for managing patient treatment decisions, informing risk of recurrence, and predicting response to therapy. Currently available tissue-informed approaches are often limited by the need for additional sequencing of normal tissue or peripheral mononuclear cells to identify non-tumor-derived alterations while tissue-naïve approaches are often limited in sensitivity. Here we present the analytical validation for a novel ctDNA monitoring assay, FoundationOne®Tracker. The assay utilizes somatic alterations from comprehensive genomic profiling (CGP) of tumor tissue. A novel algorithm identifies monitorable alterations with a high probability of being somatic and computationally filters non-tumor-derived alterations such as germline or clonal hematopoiesis variants without the need for sequencing of additional samples. Monitorable alterations identified from tissue CGP are then quantified in blood using a multiplex polymerase chain reaction assay based on the validated SignateraTM assay. The analytical specificity of the plasma workflow is shown to be 99.6% at the sample level. Analytical sensitivity is shown to be >97.3% at ≥5 mean tumor molecules per mL of plasma (MTM/mL) when tested with the most conservative configuration using only two monitorable alterations. The assay also demonstrates high analytical accuracy when compared to liquid biopsy-based CGP as well as high qualitative (measured 100% PPA) and quantitative precision (<11.2% coefficient of variation).

A novel ultrasensitive chemiluminescence enzyme immunoassay by employment of a signal enhancement of horseradish peroxidase-luminol-hydrogen peroxide reaction for the quantitation of atezolizumab, a monoclonal antibody used for cancer immunotherapy.

This study describes, for the first time, the development and validation of a novel ultrasensitive chemiluminescence enzyme immunoassay (CLEIA) for the quantification of atezolizumab (ATZ), a monoclonal antibody approved by the FDA for treatment of different types of cancer. The assay involved the non-competitive binding of ATZ to its specific antigen (PD-L1 protein). The immune complex of PD-L1/ATZ formed on the internal surface of the plate wells was quantified by a novel chemiluminescence (CL)-producing horseradish peroxidase (HRP) reaction. The reaction employed a highly efficient CL enhancer for the HRP-luminol-hydrogen peroxide reaction which was 4-(imidazol-1-yl)phenol. The conditions of the CLEIA and its detection system were refined, and the optimum procedures were established. The CLEIA was validated in accordance with the guidelines of immunoassay validation for bioanalysis, and all the validation criteria were acceptable. The assay's limit of detection and limit of quantitation were 12.5 and 37.5 pg mL-1, respectively, with a working dynamic range of 25-800 pg mL-1. The assay enables the accurate and precise quantitation of ATZ in human plasma samples without any interferences from endogenous substances and/or the plasma matrix. The results of the proposed CLEIA were favourably comparable with those of a pre-validated enzyme-linked immunosorbent assay using a colorimetric detection system. The CLEIA is characterized by simple and high throughput features. The CLEIA is superior to the existing analytical methodologies for ATZ. The proposed CLEIA has a great value in the quantitation of ATZ in clinical settings for assessment of its pharmacokinetics, therapeutic drug monitoring, and refining the safety profile.

Quantitative Precision Second-Order Temporal Transformation Based Pose Control for Spacecraft Proximity Operations

Quantitative Precision Second-Order Temporal Transformation Based Pose Control for Spacecraft Proximity Operations

Triplex glycan quantification by metabolic labeling with isotopically labeled glucose in yeast

Mass spectrometry-based approaches encompass a powerful collection of tools for the analysis biological molecules, including glycans and glycoconjugates. Unlike most traditional bioanalytical methods focusing on these molecules, mass spectrometry is especially suited for multiplexing, by utilizing stable-isotope labeling. Indeed, stable isotope-based multiplexing can be regarded as the gold-standard approach in reducing noise and uncertainty in quantitative mass spectrometry and quantitative analyses generally. The increasing sophistication and depth of biological questions being asked continue to challenge the practitioners of mass spectrometry method development. To understand the biological relevance of glycans, many stable isotope labeling-based mass spectrometry methods have been developed. Based on the duplex MILPIG (metabolic isotope labeling of polysaccharides with isotopic glucose), we establish here a novel triplex isotope labeling method using baker's yeast as the model system. Two differentially isotope-labeled glucoses (medium: 1–13C1 and heavy: 1,2–13C2), in addition to natural abundance glucose (light), were successfully used to label each monosaccharide ring in N-linked glycans in three different cell culture conditions, that, after sample mixing, resulted in a predictable triplet spectrum amenable for relative quantitation. We demonstrate excellent accuracy and precision of relative quantitation for a 1:1:1 mixture of glycans labeled in such a fashion. In addition, we applied triplex MILPIG to interrogate differential N-glycan profiles in tunicamycin-treated and control yeast cells and show that different N-glycans respond differently to tunicamycin.

BASIL: A toolbox for perfusion quantification using arterial spin labelling

Abstract Arterial Spin Labelling (ASL) MRI is now an established non-invasive method to quantify cerebral blood flow and is increasingly being used in a variety of neuroimaging applications. With standard ASL acquisition protocols widely available, there is a growing interest in advanced options that offer added quantitative precision and information about haemodynamics beyond perfusion. In this article, we introduce the BASIL toolbox, a research tool for the analysis of ASL data included within the FMRIB Software Library (FSL), and explain its operation in a variety of typical use cases. BASIL is not offered as a clinical tool, and nor is this work intended to guide the clinical application of ASL. Built around a Bayesian model-based inference algorithm, the toolbox is designed to quantify perfusion and other haemodynamic measures, such as arterial transit times, from a variety of possible ASL input data, particularly exploiting the information available in more advanced multi-delay acquisitions. At its simplest, the BASIL toolbox offers a graphical user interface that provides the analysis options needed by most users; through command line tools, it offers more bespoke options for users needing customised analyses. As part of FSL, the toolbox exploits a range of complementary neuroimaging analysis tools so that ASL data can be easily integrated into neuroimaging studies and used alongside other modalities.

A simple and green capillary electrophoresis-mass spectrometry method for therapeutic drug monitoring of colistin in clinical plasma samples

Colistin and other polymyxin antibiotics have become increasingly used in clinical settings as a result of treating multidrug-resistant infections in critically ill patients. The highly variable pharmacokinetics of colistin in these patients is accompanied by a high risk of toxicity or underdosing. An effective tool that allows rational optimization of the drug dosage regimen is therapeutic drug monitoring. Therefore, there is a need to dispose with appropriate, sensitive, and accurate analytical methods. Here, a simple, specific, and accurate on-line capillary electrophoresis – tandem mass spectrometry method was developed and applied for the first time to determine colistin in human plasma. Protein precipitation using acidified acetonitrile was the solitary procedure used to achieve sample pretreatment. A bare fused silica capillary was employed for the separation process, and the background electrolyte used was 50 mM formic acid (pH 2.54). The FDA's bioanalytical method validation guidelines were followed in the validation of the proposed method. For colistin A and colistin B, favorable performance and validation parameters were obtained (such as linearity, limit of detection, lower limit of quantitation, intra-day and inter-day precision, accuracy, and stability).The validated method was then effectively used to analyze real clinical samples taken from patients who were in critical condition. Our newly developed method is comparable with previously published liquid chromatography approaches and has the potential to be applied in the therapeutic monitoring of colistin in routine clinical laboratories. Moreover, according to the greenness assessment, the developed capillary electrophoresis – mass spectrometry method represents a very interesting green and sustainable tool in the field of bioanalysis.

Development and validation of an LC-MS/MS method for the simultaneous quantitation of angiotensin (1–7), (1–8), (1–9) and (1–10) in human plasma

Cardiovascular diseases have cast a significant negative impact on the lives of millions worldwide. Over the years, extensive efforts have been dedicated to enhancing diagnostic and prognostic tools for these diseases. A growing body of evidence indicates that the angiotensin convertase enzyme (ACE) and the angiotensin convertase enzyme 2 (ACE2), and angiotensin peptide levels could hold a pivotal role in assisting clinicians with the management of cardiovascular conditions, notably hypertension and heart failure. However, despite the considerable body of knowledge in this domain, a void remains in the field of analytical methodologies for these molecules. In this study, we present a fully validated LC-MS/MS method for the precise quantitation of plasma angiotensin (1–7), (1–8), (1–9), and (1–10), following the guidelines set by the Clinical and Laboratory Standards Institute (CLSI). Our method not only enables the accurate quantification of angiotensin peptides but also provides a means to assess ACE and ACE2 activity. Remarkably, our method achieved a Lower Limit of Measurement Interval (LLMI) as low as 5 pg/mL. This has enabled the detection of angiotensin (1–7), (1–8), (1–9) and (1–10) and the accurate quantitation of angiotensin (1–7), (1–8) and (1–10) in all analyzed groups, including healthy controls, patients with high blood pressure, and patients with chronic kidney disease. To our knowledge, our method represents the most sensitive approach allowing for simultaneous quantitation of these four angiotensin peptides. A distinct advantage of our method, when compared to immunoassays, is its high sensitivity combined with comprehensive chromatographic separation of all currently known angiotensin peptides. This combination translates to an exceptional level of selectivity, underscoring the value and potential of our methodology in advancing cardiovascular disease research.

Radiologists' Rating for Comparative Qualitative Assessment of Intravoxel Incoherent Motion Using Novel Analysis Methods.

The objective was to assess qualitative interpretability and quantitative precision and reproducibility of intravoxel incoherent motion ( IVIM) parametric images evaluated using novel IVIM analysis methods for diagnostic accuracy. Intravoxel incoherent motion datasets of 55 patients (male/female = 41:14; age = 17.8 ± 5.5 years) with histopathology-proven osteosarcoma were analyzed. Intravoxel incoherent motion parameters-diffusion coefficient ( D ), perfusion fraction ( f ), and perfusion coefficient ( D* )-were estimated using 5 IVIM analysis methods-(i) biexponential (BE) model, (ii) BE-segmented fitting 2-parameter (BESeg-2), (iii) BE-segmented fitting 1-parameter (BESeg-1), (iv) BE model with total variation penalty function (BE + TV), and (v) BE model with Huber penalty function (BE + HPF). Qualitative scoring in a 5-point Likert scale (uninterpretable: 1; poor: 2; fair: 3; good: 4; excellent: 5) was performed by 2 radiologists for 4 criteria: (a) tumor shape and margin, (b) morphologic correlation, (c) noise suppression, and (d) overall interpretability. Interobserver agreement was evaluated using Spearman rank-order correlation ( rs ). Precision and reproducibility were evaluated using within-subject coefficient of variation (wCV) and between-subject coefficient of variation (bCV). BE + TV and BE + HPF produced significantly ( P < 10 -3 ) higher qualitative scores for D (fair-good [3.3-3.8]) than BE (poor [2.3]) and for D* (poor-fair [2.2-2.7]) and f (fair-good [3.2-3.8]) than BE, BESeg-2, and BESeg-1 ( D* : uninterpretable-poor [1.3-1.9] and f : poor-fair [1.5-3]). Interobserver agreement for qualitative scoring was rs = 0.48-0.59, P < 0.009. BE + TV and BE + HPF showed significantly ( P < 0.05) improved reproducibility in estimating D (wCV: 24%-31%, bCV: 21%-31% improvement) than the BE method and D* (wCV: 4%-19%, bCV: 5%-19% improvement) and f (wCV: 25%-49%, bCV: 25%-47% improvement) than BE, BESeg-2, and BESeg-1 methods. BE + TV and BE + HPF demonstrated qualitatively and quantitatively improved IVIM parameter estimation and may be considered for clinical use further.

The Wave Model of Sleep Dynamics and an Invariant Relationship between NonREM and REM Sleep.

Explaining the complex structure and dynamics of sleep, which consist of alternating and physiologically distinct nonREM and REM sleep episodes, has posed a significant challenge. In this study, we demonstrate that a single-wave model concept captures the distinctly different overnight dynamics of the four primary sleep measures-the duration and intensity of nonREM and REM sleep episodes-with high quantitative precision for both regular and extended sleep. The model also accurately predicts how these polysomnographic measures respond to sleep deprivation or abundance. Furthermore, the model passes the ultimate test, as its prediction leads to a novel experimental finding-an invariant relationship between the duration of nonREM episodes and the intensity of REM episodes, the product of which remains constant over consecutive sleep cycles. These results suggest a functional unity between nonREM and REM sleep, establishing a comprehensive and quantitative framework for understanding normal sleep and sleep disorders.

Selective and reliable fluorometric quantitation of anti-cancer drug in real plasma samples using nitrogen-doped carbon dots after MMIPs solid phase microextraction: Monitoring methotrexate plasma level

A novel selective and reliable ratiometric fluorescence probe has been successfully synthesized for precise, sensitive, and simple quantitation of methotrexate (MTX). Hydrothermal method was employed to fabricate nitrogen-doped carbon dots using Annona squamosa seeds (AS-CDs) as a starting material, and their characteristics were confirmed using transmission electron microscopy (TEM), UV-Vis spectroscopy, fluorescence spectroscopy, X-ray diffractometry (XRD), and Fourier Transform Infrared Spectroscopy (FTIR). The ratiometric fluorometric assay, which is based on measuring the ratio of emissions (F355/F430), has a wide detection range of 5–2000 ng /mL and a limit of detection (LOD, S/N = 3) of 1.5 ng /mL. The developed sensing method was successfully applied to the quantification of MTX in rabbit plasma samples and parenteral formulations, achieving satisfactory recoveries %. Magnetic molecularly imprinted solid-phase microextraction was used for selective extraction of MTX from plasma samples. The pharmacokinetic parameters were successfully determined in real rabbit plasma samples after intravenous administration of MTX. The as-designed probe does not only improve the sensitivity, but also enhances the precision and accuracy of the proposed method. Overall, this study presents a promising approach for the detection of MTX in genuine samples with acceptable degree of selectivity and sensitivity.

Cross-modal self-attention mechanism for controlling robot volleyball motion.

The emergence of cross-modal perception and deep learning technologies has had a profound impact on modern robotics. This study focuses on the application of these technologies in the field of robot control, specifically in the context of volleyball tasks. The primary objective is to achieve precise control of robots in volleyball tasks by effectively integrating information from different sensors using a cross-modal self-attention mechanism. Our approach involves the utilization of a cross-modal self-attention mechanism to integrate information from various sensors, providing robots with a more comprehensive scene perception in volleyball scenarios. To enhance the diversity and practicality of robot training, we employ Generative Adversarial Networks (GANs) to synthesize realistic volleyball scenarios. Furthermore, we leverage transfer learning to incorporate knowledge from other sports datasets, enriching the process of skill acquisition for robots. To validate the feasibility of our approach, we conducted experiments where we simulated robot volleyball scenarios using multiple volleyball-related datasets. We measured various quantitative metrics, including accuracy, recall, precision, and F1 score. The experimental results indicate a significant enhancement in the performance of our approach in robot volleyball tasks. The outcomes of this study offer valuable insights into the application of multi-modal perception and deep learning in the field of sports robotics. By effectively integrating information from different sensors and incorporating synthetic data through GANs and transfer learning, our approach demonstrates improved robot performance in volleyball tasks. These findings not only advance the field of robotics but also open up new possibilities for human-robot collaboration in sports and athletic performance improvement. This research paves the way for further exploration of advanced technologies in sports robotics, benefiting both the scientific community and athletes seeking performance enhancement through robotic assistance.

Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata.

Deep learning has emerged as the preferred modeling approach for automatic ECG analysis. In this study, we investigate three elements aimed at improving the quantitative accuracy of such systems. These components consistently enhance performance beyond the existing state-of-the-art, which is predominantly based on convolutional models. Firstly, we explore more expressive architectures by exploiting structured state space models (SSMs). These models have shown promise in capturing long-term dependencies in time series data. By incorporating SSMs into our approach, we not only achieve better performance, but also gain insights into long-standing questions in the field. Specifically, for standard diagnostic tasks, we find no advantage in using higher sampling rates such as 500Hz compared to 100Hz. Similarly, extending the input size of the model beyond 3 seconds does not lead to significant improvements. Secondly, we demonstrate that self-supervised learning using contrastive predictive coding can further improve the performance of SSMs. By leveraging self-supervision, we enable the model to learn more robust and representative features, leading to improved analysis accuracy. Lastly, we depart from synthetic benchmarking scenarios and incorporate basic demographic metadata alongside the ECG signal as input. This inclusion of patient metadata departs from the conventional practice of relying solely on the signal itself. Remarkably, this addition consistently yields positive effects on predictive performance. We firmly believe that all three components should be considered when developing next-generation ECG analysis algorithms.