Individual Signals Research Articles

Lightweight and Low-Parametric Network for Hardware Inference of Obstructive Sleep Apnea

Background: Obstructive sleep apnea is a sleep disorder that is linked to many health complications and can even be lethal in its severe form. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Artificial intelligence (AI)-embedded wearable device as a portable and less intrusive monitoring system is a highly desired alternative to polysomnography. However, AI models often require substantial storage capacity and computational power for edge inference which makes it a challenging task to implement the models in hardware with memory and power constraints. Methods: This study demonstrates the implementation of depth-wise separable convolution (DSC) as a resource-efficient alternative to spatial convolution (SC) for real-time detection of apneic activity. Single lead electrocardiogram (ECG) and oxygen saturation (SpO2) signals were acquired from the PhysioNet databank. Using each type of convolution, three different models were developed using ECG, SpO2, and model fusion. For both types of convolutions, the fusion models outperformed the models built on individual signals across all the performance metrics. Results: Although the SC-based fusion model performed the best, the DSC-based fusion model was 9.4, 1.85, and 11.3 times more energy efficient than SC-based ECG, SpO2, and fusion models, respectively. Furthermore, the accuracy, precision, and specificity yielded by the DSC-based fusion model were comparable to those of the SC-based individual models (~95%, ~94%, and ~94%, respectively). Conclusions: DSC is commonly used in mobile vision tasks, but its potential in clinical applications for 1-D signals remains unexplored. While SC-based models outperform DSC in accuracy, the DSC-based model offers a more energy-efficient solution with acceptable performance, making it suitable for AI-embedded apnea detection systems.

Simulation of Space Charge Effects in Fourier Transform Quadrupole Ion Traps (FT-QITs).

We present ion dynamics simulations regarding the effect of space charge on the performance of Fourier transform quadrupole ion traps (FT-QITs) with special attention to signal stability, mass resolving power, and sensitivity. Ion trajectory simulations within an idealized QIT geometry are performed by applying a dedicated application (QITSim) using an in-house developed open simulation framework (IDSimF). Image current detection transients are generated by the application and are subsequently transformed into frequency spectra of ion secular motion. Such frequency spectra are the basis for the calculation of mass spectra in FT-QIT instruments. The simulation results are used to assess the extent of space charge induced effects regarding the analytical performance of FT-QIT systems. The simulation results for two Cl+ and seven Xe+ isotope ions exhibit diverse space charge induced phenomena. Most prominently, complete isotope signal fusion, quantitative individual signal suppression, and severe signal distortions are observed even at low absolute numbers of trapped ions. Furthermore, significant shifts of the secular oscillation frequencies occur, even when the signal shape appears to be mostly unaffected and when the frequency separation for trapped ions with different masses is large. This significantly limits the applicability of FT-QIT systems for high resolution mass spectrometry. An increase of the RF amplitude of the trapping field as well as increasing the extent of ion excitation partly mitigate these adverse space charge effects; nevertheless, the usable operational range of the simulated FT-QIT system for analytical applications remains rather narrow.

Leveraging a new data resource to define the response of C. neoformans to environmental signals.

Cryptococcus neoformans is an opportunistic fungal pathogen with a polysaccharide capsule that becomes greatly enlarged in the mammalian host and during in vitro growth under host-like conditions. To understand how individual environmental signals affect capsule size and gene expression, we grew cells in all combinations of five signals implicated in capsule size and systematically measured cell and capsule sizes. We also sampled these cultures over time and performed RNA-Seq in quadruplicate, yielding 881 RNA-Seq samples. Analysis of the resulting data sets showed that capsule induction in tissue culture medium, typically used to represent host-like conditions, requires the presence of either CO2 or exogenous cyclic AMP (cAMP). Surprisingly, adding either of these pushes overall gene expression in the opposite direction from tissue culture media alone, even though both are required for capsule development. Another unexpected finding was that rich medium blocks capsule growth completely. Statistical analysis further revealed many genes whose expression is associated with capsule thickness; deletion of one of these significantly reduced capsule size. Beyond illuminating capsule induction, our massive, uniformly collected dataset will be a significant resource for the research community.

Traffic Light Detection using Ensemble Learning by Boosting with Color-Based Data Augmentation

Recent advancements in deep neural networks have significantly improved the detection and recognition of traffic lights for advanced driver assistance systems (ADAS). Traditional methods often rely on identifying traffic light boxes and then recognizing individual signal lights, which can be problematic due to variations in bulb arrangements across different regions. To address this limitation, we propose a novel traffic light detection method that directly recognizes individual signal lights. Our two-stage approach combines data augmentation and ensemble learning to achieve high detection rates. By learning color characteristics from validation sets, we can effectively identify signal light candidates with a 97.26% accuracy rate. Subsequent classification results in a recognition accuracy of 98.6%, surpassing the performance of existing state-of-the-art traffic light detection algorithms. Code and dataset are available at https://github.com/981124/yolov7_traffic_light_detect.

Leveraging a new data resource to define the response of C. neoformans to environmental signals: How host-like signals drive gene expression and capsule expansion in Cryptococcus neoformans.

Cryptococcus neoformans is an opportunistic fungal pathogen with a polysaccharide capsule that becomes greatly enlarged in the mammalian host and during in vitro growth under host-like conditions. To understand how individual environmental signals affect capsule size and gene expression, we grew cells in all combinations of five signals implicated in capsule size and systematically measured cell and capsule sizes. We also sampled these cultures over time and performed RNA-Seq in quadruplicate, yielding 881 RNA-Seq samples. Analysis of the resulting data sets showed that capsule induction in tissue culture medium, typically used to represent host-like conditions, requires the presence of either CO2 or exogenous cyclic AMP (cAMP). Surprisingly, adding either of these pushes overall gene expression in the opposite direction from tissue culture media alone, even though both are required for capsule development. Another unexpected finding was that rich medium blocks capsule growth completely. Statistical analysis further revealed many genes whose expression is associated with capsule thickness; deletion of one of these significantly reduced capsule size. Beyond illuminating capsule induction, our massive, uniformly collected dataset will be a significant resource for the research community.

A novel approach to detecting epileptic patients: complex network-based EEG classification

Abstract Detection of epileptic seizures is important for early diagnosis and treatment. It is known that the behavioral patterns of the brain in electroencephalogram (EEG) signals have huge and complex fluctuations. Diagnosing epilepsy by analyzing signals are costly process. Various methods are used to classify epileptic seizures. However, the inadequacy of these approaches in classifying signals makes it difficult to diagnose epilepsy. Complex network science produces effective solutions for analyzing interrelated structures. Using methods based on complex network analysis, it is possible to EEG signals analyze the relationship between signals and perform a classification process. In this study proposes a novel approach for classifying epileptic seizures by utilizing complex network science. In addition, unlike the studies in the literature, classification processes were carried out with lower dimensional signals by using 1-s EEG signals instead of 23.6-s full-size EEG signals. Using the topological properties of the EEG signal converted into a complex network, the classification process has been performed with the Jaccard Index method. The success of the classification process with the Jaccard Index was evaluated using Accuracy, F1 Score, Recall, and K-Fold metrics. In the results obtained, the signals of individuals with epileptic seizures were separated with an accuracy rate of 98.15%.

Temporal dynamics of scent mark composition in field‐experimental lizard populations

Abstract Animal signals are dynamic traits that can undergo considerable spatial and temporal changes and that are influenced by factors such as age, health condition and interactions with both the abiotic and biotic environment. However, much of our understanding of signal changes throughout an individual's lifetime stems from cross‐sectional, often laboratory‐based, studies focused on visual and auditory signals. Longitudinal field investigations of temporal variation in chemical signals, especially in vertebrates, remain rare despite chemical communication being the most ubiquitous form of information exchange in the natural world. To remedy this, we conducted a unique, replicated field experiment to study the temporal signal dynamics in free‐living lizard populations on natural islands. Specifically, we collected scent marks from individually marked lizards across five populations during the spring of two consecutive years and analysed the lipophilic chemical composition of these scent marks. Our findings demonstrate that the overall scent mark composition of individual lizards changed over time, shifting consistently in both direction and magnitude from year to year among individuals and across replicate populations. Similar patterns were observed for the chemical richness and diversity of scent marks. Temporal variation in the relative proportions of three potentially socially relevant signalling compounds in lizard scent marks revealed a more complex pattern: α‐tocopherol remained stable over time, oleic acid decreased and the change in octadecanoic acid proportion was body size‐dependent. Together, our results provide novel insights into how individual vertebrate chemical signals may fluctuate across space and time. We discuss the potential causes of the observed temporal variability and its consequences for chemical signal evolution. Read the free Plain Language Summary for this article on the Journal blog.

Predicting the Future Signalization of Traffic-Actuated Signals Using Extreme Gradient Boosting

Stops, braking, and acceleration maneuvers at traffic signals cause high fuel consumption and emission levels. Green light optimized speed advisory (GLOSA) can help to reduce a proportion of such unnecessary vehicle maneuvers. For this, GLOSA systems require reliable switching time estimations as input. However, estimating the switching times of traffic-actuated signals can be challenging. The signalization of traffic-actuated lights is adjusted to match the current traffic. Based on green-time requests, the status of signals can change with almost no lead time. This paper presents an approach for predicting the signalization of traffic-actuated signals. We exploit the limited nature of the space for switched signal state combinations at intersections, and express combinations of motorized traffic-related signal states as one feature to depict the motorized traffic signalization state (MTSSt) at an intersection. Predicting MTSSt-switches allows later determination of the switching times of individual signals. To conduct the predictions, the machine learning method “extreme gradient boosting” was used. A three-step methodology—data preparation, tuning the prediction procedure, and testing the approach—was applied and evaluated on the historical data of four traffic-actuated signalized intersections. The results showed that within a period of the next 30 s, signal changes were predicted with an overall precision and sensitivity of about 95% and an average mean absolute error of less than 1.1 s. In this period, the sequence of switched signals was predicted accurately to the second, without any deviation, in, on average, over 82% of cases.

APT: Alarm Prediction Transformer

Distributed control systems (DCS) are essential to operate complex industrial processes. A major part of a DCS is the alarm system, which helps plant operators to keep the processes stable and safe. Alarms are defined as threshold values on individual signals taking into account minimum reaction time of the human operator. In reality, however, alarms are often noisy and overwhelming, and thus can be easily overlooked by the operators. Early alarm prediction can give the operator more time to react and introduce corrective actions to avoid downtime and negative impact on human safety and the environment. In this context, we introduce Alarm Prediction Transformer (APT), a multimodal Transformer-based machine learning model for early alarm prediction based on the combination of recent events and signal data. Specifically, we propose two novel fusion strategies and three methods of label encoding with various levels of granularity. Given a window of several minutes of event logs and signal data, our model predicts whether an alarm is going to be triggered after a few minutes and, if yes, it also predicts its location. Our experiments on two novel real industrial plant data sets and a simulated data set show that the model is capable of predicting alarms with the given horizon and that our proposed fusion technique combining inputs from different modalities, i. e. events and signals, yields more accurate results than any of the modalities alone or conventional fusion techniques.

Adaptive signal fusion for swashplate pump fault detection using bidirectional long short-term memory and wavelet scattering transform

In construction equipment, swashplate pumps are vital for uninterrupted operations. However, these pumps often experience multiple simultaneous faults. Current detection methods typically rely on a single signal, insufficient for accurately identifying multiple faults in this hydraulic system. To improve accuracy, we utilize multiple signals that provide comprehensive information, using an adaptive signal-level fusion technique. This method involves analyzing pressure, shaft torque, and swashplate torque signals to identify potential faults. The study employs a Bidirectional Long Short-Term Memory (BiLSTM) model to analyze each type of signal, assessing the model's ability to classify different faults. Once detection accuracy for each fault type is assessed, we propose using a weight matrix to adaptively fuse signals based on their assigned weights. Additionally, the Wavelet Scattering Transform (WST) is utilized for feature extraction from signals with low variance, and Bayesian optimization is applied to find the optimal settings for the BiLSTM model. The proposed approach is tested under various scenarios, including the impact of white Gaussian noise, to evaluate its effectiveness and stability. The results show that the adaptive signal-level fusion approach outperforms traditional methods and individual signal analyses in accurately and robustly detecting swashplate pump faults, highlighting its potential to significantly improve the reliability of construction equipment.

A new concept analog system to readout multi-channel photodetectors

In this work an innovative readout for multi-channel photodetectors is proposed, and the results of the prototype test are presented. This readout concept is based on the equalization and on the analog sum of a programmable number of channels. A configurable sum of individual channel signals allows for a reduction in the number of digitized channels, lowering the costs of future calorimeter granularity enhancement projects. A potential application of this new readout is in ATLAS-like hadron calorimeters.

Protecting unauthenticated messages in LTE/5G mobile networks: A two-level Hierarchical Identity-Based Signature (HIBS) solution

As an essential public infrastructure, the security and reliability of mobile networks have a profound impact on people’s production and life. Although the security of LTE/5G networks has been improved a lot with the evolution of standards, there are still some unprotected messages being transmitted between the cellular network and device due to the symmetric key-based security architecture and the trade-off between security and other criteria like network availability. By exploiting these messages, various security attacks have been proposed and demonstrated against commercial mobile networks and devices in existing literature, such as user location tracking, bidding-down, and DoS attacks. To address this security issue, in this paper, we aim to protect these unauthenticated messages in mobile networks using digital signatures. Based on the idea of Hierarchical Identity-Based Signature (HIBS) in existing work, we analyse and design a two-level HIBS solution in detail in terms of different aspects such as keys generation and provisioning procedures, replay mitigation, and cell selection. Unlike previous work, our proposed solution also supports the protection of individual vulnerable RRC and NAS layer signalling in addition to authenticating the base station. We evaluated the efficiency and feasibility of several existing HIBS schemes and implemented the most efficient one in the 5G standalone network setup using open-source software. The implementation results further proved the feasibility of the solution in practice.

Characterization of the angiomodulatory effects of Interleukin 11 cis- and trans-signaling in the retina

BackgroundThe IL-6 cytokine family, with its crucial and pleiotropic intracellular signaling pathway STAT3, is a promising target for treating vasoproliferative retinal diseases. Previous research has shown that IL-6 cis-signaling (via membrane-bound receptors) and trans-signaling (via soluble receptors) can have distinct effects on target cells, leading to their application in various disease treatments. While IL-6 has been extensively studied, less is known about the angiogenic effects of IL-11, another member of the IL-6 family, in the retina. Therefore, the aim of this study was to characterize the effects of IL-11 on retinal angiogenesis.Main textIn vitreous samples from proliferative diabetic retinopathy (PDR) patients, elevated levels of IL-11Rα, but not IL-11, were detected. In vitro studies using vascular endothelial cells revealed distinct effects of cis- and trans-signaling: cis-signaling (IL-11 alone) had antiangiogenic effects, while trans-signaling (IL-11 + sIL-11Rα) had proangiogenic and pro-migratory effects. These differences can be attributed to their individual signaling responses and associated transcriptomic changes. Notably, no differences in cis- and trans-signaling were detected in primary mouse Müller cell cultures. STAT3 and STAT1 siRNA knockdown experiments revealed opposing effects on IL-11 signaling, with STAT3 functioning as an antiproliferative and proapoptotic player while STAT1 acts in opposition to STAT3. In vivo, both IL-11 and IL-11 + sIL-11Rα led to a reduction in retinal neovascularization. Immunohistochemical staining revealed Müller cell activation in response to treatment, suggesting that IL-11 affects multiple retinal cell types in vivo beyond vascular endothelial cells.ConclusionsCis- and trans-signaling by IL-11 have contrasting angiomodulatory effects on endothelial cells in vitro. In vivo, cis- and trans-signaling also influence Müller cells, ultimately determining the overall angiomodulatory impact on the retina, highlighting the intricate interplay between vascular and glial cells in the retina.

Electrocardiogram Signal Compression Using Deep Convolutional Autoencoder with Constant Error and Flexible Compression Rate

ObjectivesElectrocardiogram (ECG) signals are beneficial for diagnosing cardiac diseases. The cardiac patients' life quality likely increases with continuous or long-period recording and monitoring of ECG signals, leading to better and early diagnosis of disease and heart attacks. However, continuous ECG recording necessitates high data rates and storage, which means high costs. Therefore, ECG compression is a handy concept that facilitates continuous monitoring of ECG signals. Deep neural networks open up new horizons for compression and also for ECG compression by providing high compression rates and quality. Although they bring constant compression ratios with better average quality, the compression quality of individual samples is not guaranteed, which may lead to misdiagnoses. This study aims to investigate the effect of compression quality on the diagnoses and to develop a deep neural network-based compression strategy that guarantees a quality-bound in return for varying compression ratios.Materials and methodsThe effect of the compression quality on the arrhythmia diagnoses is tested by comparing the performance of the deep learning-based ECG classifier on the original ECG recordings and the distorted recordings using a lossy compression algorithm with different compression error levels. Then, a compression error upper limit is calculated in terms of normalized percent root mean square difference (PRDN) error, which also coincides with the findings of the previous studies in the literature. Lastly, to enable deep learning in ECG compression, a single encoder-multi-decoder convolutional autoencoder architecture, and multiple quantization levels are proposed to guarantee a desired upper limit on the error rate.ResultsThe efficiency of the proposed method is demonstrated on a popular benchmark data set for ECG compression methods using a transfer learning approach. The PRDN error is fixed to various values, and the average compression rates are reported. An average of 13.019:1 compression is achieved for a 10% PRDN error rate, assessed as a fair quality threshold for reconstruction error. It has also been shown that the compression model has a runtime that can be run in real-time on wearable devices such as commercial smartwatches.ConclusionThis study proposes a deep learning-based ECG compression algorithm that guarantees a desired upper limit on the compression error. This model may facilitate an eHealth solution for continuous monitoring of ECG signals of individuals, especially cardiac patients.

Entangling gates on degenerate spin qubits dressed by a global field

Semiconductor spin qubits represent a promising platform for future large-scale quantum computers owing to their excellent qubit performance, as well as the ability to leverage the mature semiconductor manufacturing industry for scaling up. Individual qubit control, however, commonly relies on spectral selectivity, where individual microwave signals of distinct frequencies are used to address each qubit. As quantum processors scale up, this approach will suffer from frequency crowding, control signal interference and unfeasible bandwidth requirements. Here, we propose a strategy based on arrays of degenerate spins coherently dressed by a global control field and individually addressed by local electrodes. We demonstrate simultaneous on-resonance driving of two degenerate qubits using a global field while retaining addressability for qubits with equal Larmor frequencies. Furthermore, we implement SWAP oscillations during on-resonance driving, constituting the demonstration of driven two-qubit gates. Significantly, our findings highlight how dressing can overcome the fragility of entangling gates between superposition states and increase their noise robustness. These results constitute a paradigm shift in qubit control in order to overcome frequency crowding in large-scale quantum computing.

UniChromCD for Demultiplexing Time-Resolved Charge Detection-Mass Spectrometry Data.

Charge detection mass spectrometry (CD-MS) enables characterization of large, heterogeneous analytes through the analysis of individual ion signals. Because hundreds to thousands of scans must be acquired to produce adequate ion statistics, CD-MS generally requires long analysis times. The slow acquisition speed of CD-MS complicates efforts to couple it with time-dispersive techniques, such as chromatography and ion mobility, because it is not always possible to acquire enough scans from a single sample injection to generate sufficient ion statistics. Multiplexing methods based on Hadamard and Fourier transforms offer an attractive solution to this problem by improving the duty cycle of the separation while preserving retention/drift time information. However, integrating multiplexing with CD-MS data processing is complex. Here, we present UniChromCD, a new module in the open-source UniDec package that incorporates CD-MS time-domain data processing with demultiplexing tools. Following a detailed description of the algorithm, we demonstrate its capabilities using two multiplexed CD-MS workflows: Hadamard-transform size-exclusion chromatography and Fourier-transform ion mobility. Overall, UniChromCD provides a user-friendly interface for analysis and visualization of time-resolved CD-MS data.

CNN-ELMNet: fault diagnosis of induction motor bearing based on cross-modal vector fusion

Abstract As the primary driving equipment in industrial, accurate fault diagnosis and condition monitoring of induction motor is crucial for ensuring operational safety. This paper focuses on the bearing faults of induction motors, which have a substantial impact on both the mechanical and electromagnetic systems of the motors. However, in diagnostic tasks, we are faced with the challenges of multi-source, multi-modal data, significant influence from environmental noise, and minimal differentiation between fault data. This paper proposed a novel cross-modal vector fusion fault diagnosis and classification model (CNN-ELMNet), which includes a cross-modal vector fusion network (VF) based on D-S evidence theory, feature extraction layer (FE) and classification layer (CL). Specifically, the VF prioritizes the integration of diagnostic results from individual vibration signals or stator current signals within convolutional neural networks with the features of the input implicit vectors as decision-making evidence, followed by weighted vector fusion through D-S evidence theory at the decision level. The FE focuses on retaining the convolutional, pooling, and fully connected layers of the convolutional network and freezing the final fully connected layer, thus preserving training parameters and fully utilizing the network’s powerful FE capabilities. The CL includes an Extreme Learning Machine optimized for random hyperparameters using the snow ablation optimizer (SAO) algorithm, which offers rapid convergence and high classification recognition rates. The CNN-ELMNet model combines a convolutional network with an extreme learning machine optimized by the SAO algorithm, which not only preserves the model’s FE capability but also enhances the convergence speed and classification recognition rate of the model. Experimental results on real datasets demonstrate that the proposed model exhibits strong stability, generalization, and high accuracy in fault diagnosis, achieving accuracy rate of 99.29% and 98.75%. This provides a more feasible solution for the bearing fault diagnosis of induction motors and holds promising prospects for practical applications.

Sensor-Head Distance and Signal Strength in Whole-Head Magnetoencephalography: Report of 996 Patients With Epilepsy.

Although the sensor-to-head distance is theoretically known to affect the signal strength in magnetoencephalography (MEG), these values have not been reported for a whole-head MEG system in a large population. We measured the distance and signal strength in 996 patients with epilepsy. The MEG sensor array consisted of 102 measurement sites, each of which had two gradiometers and one magnetometer. The sensor-head distance was defined as the minimum distance between each site and a set of digitized scalp points. For the signal strength, we calculated the root-mean-square of the signal values in each sensor over a recording of 4 minutes. For analyses at the individual and sensor levels, these values were averaged over the sensors and patients, respectively. We evaluated the correlation between distance and signal strength at both individual and sensor levels. At the sensor level, we investigated regional differences in these measures. The individual-level analysis showed only a weak negative correlation between the sensor-head distance and the signal strength. The sensor-level analysis demonstrated a considerably negative correlation for both gradiometers and magnetometers. The sensor-head distances showed no significant differences between the regions, whereas the signal strength was higher in the temporal and occipital sensors than in the frontal and parietal sensors. Sensor-head distance was not a definitive factor for determining the magnitude of MEG signals in individuals. Yet, the distance is important for the signal strength at a sensor level. Regional differences in signal strength may need to be considered in the analysis and interpretation of MEG.

Robust In-Vehicle Signal Quality Assessment Using Multimodal Signal Fusion.

Continuous monitoring of physiological signals such as electrocardiogram (ECG) in driving environments has the potential to reduce the need for frequent health check-ups by providing real-time information on cardiovascular health. However, capturing ECG from sensors mounted on steering wheels creates difficulties due to motion artifacts, noise, and dropouts. To address this, we propose a novel method for reliable and accurate detection of heartbeats using sensor fusion with a bidirectional long short-term memory (BiLSTM) model. Our dataset contains reference ECG, steering wheel ECG, photoplethysmogram (PPG), and imaging PPG (iPPG) signals, which are more feasible to capture in driving scenarios. We combine these signals for R-wave detection. We conduct experiments with individual signals and signal fusion techniques to evaluate the performance of detected heartbeat positions. The BiLSTMs model achieves a performance of 62.69% in the driving scenario city. The model can be integrated into the system to detect heartbeat positions for further analysis.

ATP, the 31P Spectral Modulus, and Metabolism.

Adenosine triphosphate (ATP) has a high intracellular millimolar concentration (ca. 2.4 mM) throughout the phylogenetic spectrum of eukaryotes, archaea, and prokaryotes. In addition, the function of ATP as a hydrotrope in the prevention of protein aggregation and maintenance of protein solubilization is essential to cellular, tissue, and organ homeostasis. The 31P spectral modulus (PSM) is a measure of the health status of cell, tissue, and organ systems, as well as of ATP, and it is based on in vivo 31P nuclear magnetic resonance (31P NMR) spectra. The PSM is calculated by dividing the area of the 31P NMR integral curve representing the high-energy phosphates by that of the low-energy phosphates. Unlike the difficulties encountered in measuring organophosphates such as ATP or any other phosphorylated metabolites in a conventional 31P NMR spectrum or in processed tissue samples, in vivo PSM measurements are possible with NMR surface-coil technology. The PSM does not rely on the resolution of individual metabolite signals but uses the total area derived from each of the NMR integral curves of the above-described spectral regions. Calculation is based on a simple ratio of the high- and low-energy phosphate bands, which are conveniently arranged in the high- and low-field portions of the 31P NMR spectrum. In practice, there is essentially no signal overlap between these two regions, with the dividing point being ca. -3 δ. ATP is the principal contributor to the maintenance of an elevated PSM that is typically observed in healthy systems. The purpose of this study is to demonstrate that (1) in general, the higher the metabolic activity, the higher the 31P spectral modulus, and (2) the modulus calculation does not require highly resolved 31P spectral signals and thus can even be used with reduced signal-to-noise spectra such as those detected as a result of in vivo analyses or those that may be obtained during a clinical MRI examination. With increasing metabolic stress or maturation of metabolic disease in cells, tissues, or organ systems, the PSM index declines; alternatively, with decreasing stress or resolution of disease states, the PSM increases. The PSM can serve to monitor normal homeostasis as a diagnostic tool and may be used to monitor disease processes with and without interventional treatment.