MATLAB Toolkit Research Articles

A Descriptive Comparison of Oral Cavity Movements Between Brass Instrumentalists Performing Large Interval Slurs.

We have previously described patterns of oral cavity movement in advanced French horn players during the performance of large interval slurs. The current study expands upon that work by reporting comparisons between horn, trumpet, and trombone players performing similar large interval exercises. Real-time MRI (RT-MRI) films at 20 msec resolution were simultaneously obtained in the sagittal and coronal planes in 10 trumpet players, 9 horn players, and 10 trombone players as they performed 2 repetitions each of slur sequences spanning 1 octave and 1 octave + 3rd (interval of a tenth) at a mezzo forte dynamic level. Nine profile lines were overlaid on the sagittal images allowing the measurement of dorsal tongue edge movement using a customized MATLAB toolkit. Image J (FIJI) was utilized to measure the cross-sectional area formed between the dorsal edge of the tongue and the hard palate as depicted in coronal images. In horn and trumpet players, but not in trombone players, sagittal and coronal views demonstrate patterned tongue movements that narrow and widen the air channel during ascending and descending slurs, respectively. The magnitude of these movements is greater during larger intervals, though not perfectly consistent within each slur sequence. The sagittal views revealed these patterns to be most prominent in the anterior oral cavity rather than the middle and posterior oral cavity across all instruments. Conformational changes of the oral cavity (vocal-tract tuning) during large interval slurs in the upper register may assist performance in trumpet and horn players. Trombone players may also employ these strategies when the extreme upper register is involved, but future study must verify this.

Improvement of Basal Ganglia Detectability in Brain Single Photon Emission Computerized Tomography by Wavelet Transformation in Image Processing Domain: A XCAT Phantom Study

Purpose: Noise in brain Single Photon Emission Computed Tomography (SPECT) images limits an early diagnosis of Parkinson's Disease (PD). To overcome the limitation, as an image processing approach, wavelet transformation was used to denoising the images also with a segmentation method to differentiate the basal ganglia in brain SPECT.
 Materials and Methods: The brain scans of the human 4D Extended Cardiac Torso (XCAT) phantom through the Simulating Medical Imaging Nuclear Detectors (SIMIND) simulated SPECT system were imported to the MATLAB toolkit for image processing. The reconstructed brain images by iterative reconstruction were de-noised through 9 methods of wavelet transformation at different levels, and then six segmentation methods were applied to differentiate the caudate and putamen. The Dice coefficient, Specificity, and Sensitivity evaluation criteria were calculated based on the adaptive thresholding of the selected images from segmentation. A ground truth image was manually marked by a clinical nuclear medicine specialist.
 Results: The dice coefficient was obtained in a range from 0.3979 to 0.6299, as well as the specificity criterion from 0.7682 to 0.8168 and the sensitivity from 0.9049 to 0.9871.
 The results from adaptive threshold segmentation and the evaluation criteria showed that the best levels of the nucleuses detectability were provided by level 7 of Biorthogonal, levels 4 and 7 of Coiflet, level 6 of Daubechies, level 5 of Haar, level 6 of Morlet and level 6 of Symlet methods.
 Conclusion: Parkinson’s disease may be diagnosed in the early stage by an image processing approach to improve the quality of brain SPECT images.

Sorotoki: A Matlab Toolkit for Design, Modeling, and Control of Soft Robots

Sorotoki: A Matlab Toolkit for Design, Modeling, and Control of Soft Robots

Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets.

Radiomics involves the extraction of information from medical images that are not visible to the human eye. There is evidence that these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations, namely the MATLAB toolkit and Pyradiomics, when applied to independent datasets of CT scans of patients: (i) the open access RIDER dataset containing a set of repeat CT scans taken 15 min apart for 31 patients (RIDER Scan 1 and Scan 2, respectively) treated for lung cancer; and (ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV), manually outlined by an experienced observer available on both datasets, was used. The 43 common radiomics features available in MATLAB and Pyradiomics were calculated using two intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters, and the Spearman's rank coefficient, rs, calculated. Reproducibility was defined when a highly correlated feature in the RIDER dataset also correlated highly in the HN1 dataset, and vice versa. A total of 29 out of the 43 reported stable features were found to be highly reproducible between MATLAB and Pyradiomics implementations, having a consistently high correlation in rank ordering for RIDER Scan 1 and RIDER Scan 2 (rs > 0.8). 18/43 reported features were common in the RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets.

Development of a MATLAB-based Toolkit to Quantify Neurodegenerative Histopathology Markers Applied to the Evaluation of a CRISPR-based Therapeutic for Alzheimer’s Disease (P3-6.003)

<h3>Objective:</h3> The objective of this study was to develop a comprehensive, easy-to-use set of MATLAB-based tools to quantify Alzheimer’s disease (AD) associated histopathological markers in immunolabeled brain tissue. We then utilized this MATLAB-based toolkit to assess the efficacy of a novel CRISPR-based, amyloid precursor protein (APP)-editing approach in a mouse model of AD. <h3>Background:</h3> AD is characterized immunohistologically by the presence of extracellular amyloid plaque deposits and associated neuroinflammation throughout the cerebral cortex and hippocampus. Amyloid plaques are aggregates of amyloid beta (Aβ) peptides that are released by successive cleavages of full length APP. Our lab has developed a novel CRISPR-based, APP-editing approach that effectively deletes the YENPTY endocytic domain at the C-terminus of APP, thus attenuating pathologic beta-cleavage and up-regulating neuroprotective alpha-cleavage. In order to assess the efficacy of our approach in vivo, we used CRISPRs to generate germ-line and somatic edits in an APP knock in (APP NL/G/F) mouse model of AD. <h3>Design/Methods:</h3> <h3>Immunohistochemistry (IHC):</h3> Tissues from appropriately aged control and CRISPR-edited APP NL/G/F mice were immunostained for Aβ plaques (6E10 antibodies), astrocytes (anti-GFAP antibodies) and microglia (anti-Iba1 antibodies). <h3>MATLAB analyses:</h3> After images were optimally contrasted, a color-channel filter was applied to isolate cells and plaques from the background. Our MATLAB program was then able to segment individual cells/plaques and measure their area, distance to nearby cells/plaques, and percentage of overlap. Activated microglia were defined as those with an area of 25 μm² or greater. <h3>Results:</h3> MATLAB quantification of IHC-stained sections revealed a robust decrease in Aβ deposition and reactive astrogliosis in CRISPR-treated APP NL/G/F tissues. These results matched supporting qualitative assessments and biochemical analyses. <h3>Conclusions:</h3> Our MATLAB toolkit can reliably quantify common neuropathological IHC markers and may be useful for evaluating the efficacy of neurotherapeutics. <b>Disclosure:</b> Mr. Satyadev has nothing to disclose. Dr. Aulston has nothing to disclose. Miss Branes has nothing to disclose. Miss Checka has nothing to disclose. Dr. Roy has stock in CRISPRAlz. The institution of Dr. Roy has received research support from Beam Therapeutics. Dr. Roy has received intellectual property interests from a discovery or technology relating to health care.

Oral Cavity Movements of the Tongue During Large Interval Slurs in High-Level Horn Players: A Descriptive Study.

Recent publications describing lingual movement strategies within the oral cavity of brass players have established the existence of efficient and predictable movement patterns in healthy performers for a variety of performance tasks. In this study we extend the research to include the playing of large interval slurs in horn players. Real-time MRI films at 40-msec resolution were simultaneously obtained in the sagittal and coronal planes in 9 professional horn players as they performed 2 repetitions each of 3 slur sequences spanning 1 octave, 1 octave + 3rd, and 1 octave + 5th at a mezzo forte dynamic level. Nine profile lines were overlaid on the images allowing the measurement of dorsal tongue edge movement using a customized MATLAB toolkit. Movement along lines representing the anterior, middle, and posterior oral cavity in the sagittal plane, as well as the vertical height of an air channel observed in the coronal plane, are reported. Both sagittal and coronal views demonstrate patterned tongue movements that narrow and widen the air channel during ascending and descending slurs, respectively. The magnitude of these movements is greater during larger intervals, though not perfectly consistent within each slur sequence. Additionally, the tongue position during notes tends to drift in the direction of the subsequent note in each sequence. We suggest that the observed movements may help to modulate air speed through the lips, possible attenuating embouchure muscle tension changes by assisting changes in lip vibration frequency.

Artificial neural network (ANN) based prediction of process parameters in additive manufacturing

In recent years, selective laser melting (SLM), a part of additive manufacturing (AM) is one of the most encouraging ones that permit fabricating metallic parts from metal powder with complex geometry. Diversities in thesecycle boundaries become an imperative system to improve the nature of the outcome.Cycleboundaries, for example, laser power, scan speed, hatch spacing, layer height used as input parameters and have a significant impact on the mechanical property taken as an output parameter of the manufactured part. The Artificial Neural Network (ANN) model includes a multi-layer perceptron (MLP) learning algorithm named as Levenberg-Marquardt and tangent sigmoid function consider as preparing and testing functions respectively utilizing MATLAB toolkit. Ideal cycle boundaries are attained dependent on the mean square error function (MSE) and correlation coefficient(R2).

PO-0263 A Matlab toolkit for Ru-106 eye plaque QA, dose calculation, reference table and report generation

PO-0263 A Matlab toolkit for Ru-106 eye plaque QA, dose calculation, reference table and report generation

CML: Chronic Myeloid Leukemia Detection Using Particle Swarm Optimization and Fuzzy C-Means Clustering

Leukemia is a blood cancer caused by a rise in the number of white blood cells in your body. Those white blood cells crowd out the red blood cells and platelets that your body needs to be healthy. The extra white blood cells don’t work right. However, the macroscopic examination of human blood necessitates various microscopic techniques, including color imaging, segmentation, and clustering, to distinguish patients with the disease. The white blood cell count can be higher than normal and prevent the immune system from functioning properly. The most reliable method to detect leukemia is with the assistance of a microscope. Automation of this form of Leukemia diagnosis is required because fewer and more expensive precise tests are required than the above. Blood slides don't reveal the findings because of hematologists' combined experience and tiredness, and those results can vary significantly from one PSO to another. As it is reasonably inexpensive and effective, blood-stained histopathology imaging to test for leukemia is a low-cost and innovative choice. The fuzzy-c segmentation coefficients of this paper are compared to those for image segmentation k. Using the GLCM to separate color characteristics from photographs performs better precision than using Fuzzy-classifications. Fuzzy's consistency level is 96%, while sharpness is 83% CNN is mainly a television outlet. The whole project has been done using the MATLAB toolkit.

Optimal Design Prediction of a Singly Reinforced Concrete Beam Steel Using Artificial Neural Network

This paper presents the prediction of a singly reinforced concrete beam tension reinforcement design requirements using Artificial Neural Networks (ANN). The method was adopted for cost optimization of the tension reinforcement in the structural element and compared with the requirements of Eurocode 2 design. The code provisions for the design of a singly reinforced beam can vary from place to place. The use of a system immune from the code variation is an excellent means of predicting the reinforcement’s need of a rectangular concrete beam. In this work, an artificial neural network (ANN) is employed to forecast the reinforcement of such a beam. Artificial neural network has the potential to simulate the data that are hard to produce in arithmetical analysis. The scheme was established using the MATLAB tool kit. The design variables were the depth of the beam, the width of the beam, and the moments. A forward pass supervised backward propagation training. The regression analysis of the results is one to one match. The predicted and target values are completely in accord.

Prediction of a singly reinforced concrete beam steel using artificial neural network

This paper presents the prediction of a singly reinforced concrete beam using Artificial Neural Networks (ANN). The method was adopted for cost optimization of the structural element and compared with the requirements of Eurocode 2 design. The code provisions for the design of a singly reinforced beam can vary from place to place. The use of a system immune from the code variation is an excellent means of predicting the reinforcement’s need of a rectangular concrete beam. In this work, an artificial neural network (ANN) is employed to forecast the reinforcement of such a beam. Artificial neural network has the potential to simulate the data that are hard to produce in arithmetical analysis. The scheme was established using the MATLAB tool kit. The design variables were the depth of the beam, the width of the beam, and the moments. A forward pass supervised backward propagation training. The regression analysis of the results is one to one match. The predicted and target values are completely in accord.

PVAnalytX: A MATLAB toolkit for techno-economic analysis and performance evaluation of rooftop PV systems

The adaptation of rooftop solar PV systems has increased significantly in recent years. Batteries have become an essential part of the rooftop solar PV systems, which ensures maximum utilisation of solar energy. With a view to present a clear insight to benefits of a rooftop solar PV system, considering both the technical and economic benefits, a MATLAB based PVAnalytX software toolkit has been presented in this paper. The end users can evaluate both the technical and economic benefits of a rooftop solar PV system considering solar generation and electricity demand variability, as well the battery performance degradation over the course of its usage. In addition, the mechanism to generate forecasted solar power generation and electricity demand data is also included as a feature in the proposed software toolkit. Furthermore, the toolkit considers actual electricity imports and export tariffs that allow the end consumers to know their financial expenditures for electricity consumption and income generated through the export of access rooftop PV energy to the grid. A thorough validation of the proposed toolkit has been presented through two case studies using actual dataset of rooftop PV systems located at CBD region of Sydney, Australia.

Risk assessment optimization for decision support using intelligent model based on fuzzy inference renewable rules

Due to the unreliability of wired communications and the risks of controlling the process of transmitting data besides the complications that affecting data protection and the high costs of systems infrastructure, led to use wireless communications instead of wires media, but these networks are vulnerable towards illegal attacks. The side effects of these attacks are modifying data or penetrate the security system and discover its weaknesses, which leads to great material losses. These risks and difficulties led to the reluctance of wires communications and propose intelligent techniques and robust encryption algorithms for preventing data transmitted over wireless networks to keep it safe from cyber security attacks. So, there is a persistent need for providing intelligent techniques and robust algorithms to preserve conveyed information using wireless network. This paper introduces scenario for proposing intelligent technique to increase data reliability and provides a new way to improve high level of protection besides reduces infrastructure cost. The proposed system relies on two models, where the first model based on producing a knowledge base of risk rules while the aim of the second module is a risk assessment outcomes and encryption process according to attacks type. In this system, reducing risks levels based on renewable rules whereas a novel security system established on non-periodic keys with unsystematic operations using fuzzy system. We concluded that the proposed system has the ability to protect the transmitted data, increases its reliability and reduce the potential risks. MATLAB Toolkit 2014 then Weka open source package was used in encryption and data mining for the proposed system.&lt;iframe class="ginger-extension-definitionpopup" style="left: -91.5px; top: -78px; z-index: 100001; display: none;" src="chrome-extension://kdfieneakcjfaiglcfcgkidlkmlijjnh/content/popups/definitionPopup/index.html?title=Abstract&amp;amp;description=a%20concept%20or%20idea%20not%20associated%20with%20any%20specific%20instance"&gt;&lt;/iframe&gt;

Tongue involvement in embouchure dystonia: new piloting results using real-time MRI of trumpet players

BackgroundThe embouchure of trumpet players is of utmost importance for tone production and quality of playing. It requires skilled coordination of lips, facial muscles, tongue, oral cavity, larynx and breathing and has to be maintained by steady practice. In rare cases, embouchure dystonia (EmD), a highly task specific movement disorder, may cause deterioration of sound quality and reduced control of tongue and lip movements. In order to better understand the pathophysiology of this movement disorder, we use real-time MRI to analyse differences in tongue movements between healthy trumpet players and professional players with embouchure dystonia.MethodsReal-time MRI videos (with sound recording) were acquired at 55 frames per second, while 10 healthy subjects and 4 patients with EmD performed a defined set of exercises on an MRI-compatible trumpet inside a 3 Tesla MRI system. To allow for a comparison of tongue movements between players, temporal changes of MRI signal intensities were analysed along 7 standardized positions of the tongue using a customised MATLAB toolkit. Detailed results of movements within the oral cavity during performance of an ascending slurred 11-note harmonic series are presented.ResultsPlaying trumpet in the higher register requires a very precise and stable narrowing of the free oral cavity. For this purpose the anterior section of the tongue is used as a valve in order to speed up airflow in a controlled manner. Conversely, the posterior part of the tongue is much less involved in the regulation of air speed. The results further demonstrate that healthy trumpet players control movements of the tongue rather precisely and stable during a sustained tone, whereas trumpet players with EmD exhibit much higher variability in tongue movements.ConclusionControl of the anterior tongue in trumpet playing emerges as a critical feature for regulating air speed and, ultimately, achieving a high-quality performance. In EmD the observation of less coordinated tongue movements suggests the presence of compensatory patterns in an attempt to regulate (or correct) pitch. Increased variability of the anterior tongue could be an objective sign of dystonia that has to be examined in further studies and extended to other brass instruments and may be also a potential target for therapy options.

Simultaneous dual-plane, real-time magnetic resonance imaging of oral cavity movements in advanced trombone players.

This paper describes the use of real-time magnetic resonance imaging to simultaneously obtain magnetic resonance imaging (MRI) videos in both a sagittal and coronal plane during the performance of a musical exercise in five advanced trombone players. Dual-slice recordings were implemented in a frame-interleaved manner with 20 ms acquisitions per frame to achieve two interleaved videos at a rate of 25 frames per second. A customized MATLAB toolkit was used for the extraction of line profiles from MRI videos to quantify tongue movements associated with exercise performance from both perspectives. Across all subjects, the analyses revealed precise coupling of vertical movements of the dorsal tongue surface (DTS), viewed from a sagittal perspective, with reduction in the vertical and horizontal dimensions of the air channel formed between the DTS and the hard palate, viewed from a coronal perspective. The cross-correlation between these movements was very strong (mean R=0.967). These results demonstrate the unique utility of this dual-slice technology in describing the coordination of complex tongue movements occurring in two planes (i.e., three directions) simultaneously, lending a deeper understanding of lingual motor control during trombone performance.

Tongue Position Variability During Sustained Notes in Healthy vs Dystonic Horn Players Using Real-Time MRI.

Embouchure dystonia (EmD) is a variant of focal task-specific dystonia in musicians characterized by the loss of control in facial and oral muscles while controlling airflow into the mouthpiece of a wind or brass instrument. We compared tongue position variability (TPV) during sustained notes between healthy, elite horn players and horn players affected by EmD. Real-time MRI films at 33.3 ms resolution were obtained from 8 healthy elite and 5 EmD horn players as they performed on a non-ferromagnetic horn at each of three different dynamic levels: pianissimo, mezzo forte, and fortissimo. Nine profile lines (3 from anterior, 3 from middle, and 3 from posterior oral cavity regions) were overlaid on each image using a customized MATLAB toolkit, and the variability of the dorsal tongue edge position was examined at each dynamic from temporal intensity profiles produced by MATLAB. Despite trends for more pronounced TPV (larger standard deviations) in the elite musicians (p=0.062), 2-way repeated measures ANOVA revealed no significant differences between groups. However, dynamic level significantly influenced TPV for all subjects, combined (p=0.048) and different regions of the oral cavity showed differing TPV (p<0.001). When only the most active region (anterior oral cavity) was included in the model, differences between groups reached statistical significance (elite > EmD, p<0.048), particularly at the fortissimo dynamic. We postulate that these differences may be due, in part, to a greater degree of generalized orofacial muscle tension in the EmD subjects that includes the tongue.

РАЗРАБОТКА ИНСТРУМЕНТАЛЬНОГО СРЕДСТВА УПРАВЛЕНИЯ ФИНАНСОВОЙ УСТОЙЧИВОСТЬЮ ПРЕДПРИЯТИЙ НА ОСНОВЕ МЕТОДОВ ТЕОРИИ НЕЧЕТКОЙ ЛОГИКИ

In the current economic and political situation the existing methods and models of analyzing and consolidating financial statements in the information architecture of an enterprise do not meet modern business environment criteria. The article presents a model built by using the fuzzy logic apparatus, managing the financial stability of the enterprises of the consolidated group using MATLAB toolkit and FuzzyLogic software package. The model is based on an integral indicator of the financial performance of the enterprise. The key performance indicators have been formed allowing to comprehensively assess the financial condition of the enterprise and the level of its financial stability. The presented indicators go through the stage of fuzzification, where eache factor is given a membership function, a decision and defuzzification stage, i.e. reduction of the resulting a fuzzy value to clarity. Application of Mamdani fuzzy inference algorithm allows to obtain a clear value of the integral indicator of the financial state of the enterprise. The obtained value allows to take on-line operational management decisions. The developed model has been applied to assess the financial state of “Metinvest Holding” enterprise, to identify its weak and strong sides, as well as to propose ways to improve its activities and possible options for taking managerial decisions. It has been inferred that fuzzy logic methods make it possible to analyze the financial stability of consolidated groups of companies under conditions of uncertainty, for example, in those cases when there is not any full-fledged statistics, or that non-financial data should be included in the number of investigated indicators. There have been used general scientific methods: generalization, abstraction, analogy, induction and deduction, a systematic approach and toolkit of fuzzy modeling to assess financial state of the enterprise and to manage its stability, as well as methods of scientific cognition.

CONSTRUCTING INFORMATION AND ANALYTICAL SYSTEM OF DECISION-MAKING SUPPORT CONCERNING REDEVELOPMENT PROJECT

Визначено предметну галузь управління проектами редевелопменту як засобу якісного перетворення міської території. Проаналізовано напрями сучасних досліджень у редевелопменті, що дало змогу виділити їх переваги та недоліки. Визначено необхідність проектування та реалізації інформаційно-аналітичної системи (ІАС) підтримки рішень "Редевелопмент" щодо проекту редевелопменту, зважаючи на багатокритеріальність, багатоваріантність задач редевелопменту, потребу оброблення та зберігання великих масивів кількісної та якісної інформації. Розроблено інфологічну модель ІАС "Редевелопмент", яка містить множину функцій оброблення даних, обчислювальних функцій та функцій забезпечення інтерфейсу з користувачем, що передбачають використання як високорівневої мови програмування C#, так і переваг відомого інструментального засобу MATLAB, необхідні функції якого імплементовано в програму на C#. Представлено схему бази даних, що дозволило здійснювати зберігання та обмін інформацією між користувачами системи, а саме стейкхолдерами проекту: інвестором, замовником, підрядником, управляючою компанією. Базовими функціями аналітичного блоку розробленої системи є такі: автоматизація процедури експертного оцінювання характеристик проекту редевелопменту, прогнозування попиту та пропозиції щодо певного функціонального призначення проекту редевелопменту.

Kalman filter implementation for small satellites using constraint GPS data

Due to the increased need for autonomy, an Extended Kalman Filter (EKF) has been designed to autonomously estimate the orbit using GPS data. A propagation step models the satellite dynamics as a two body with J2 (second zonal effect) perturbations being suitable for orbits in altitudes higher than 600 km. An onboard GPS receiver provides continuous measurement inputs. The continuity of measurements decreases the errors of the orbit determination algorithm. Power restrictions are imposed on small satellites in general and nanosatellites in particular. In cubesats, the GPS is forced to be shut down most of the mission’s life time. GPS is turned on when experiments like atmospheric ones are carried out and meter level accuracy for positioning is required. This accuracy can’t be obtained by other autonomous sensors like magnetometer and sun sensor as they provide kilometer level accuracy. Through simulation using Matlab and satellite tool kit (STK) the position accuracy is analyzed after imposing constrained conditions suitable for small satellites and a very tight one suitable for nanosatellite missions.

FATES: a flexible analysis toolkit for the exploration of single-particle mass spectrometer data

Abstract. Single-particle mass spectrometer (SPMS) analysis of aerosols has become increasingly popular since its invention in the 1990s. Today many iterations of commercial and lab-built SPMSs are in use worldwide. However, supporting analysis toolkits for these powerful instruments are outdated, have limited functionality, or are versions that are not available to the scientific community at large. In an effort to advance this field and allow better communication and collaboration between scientists, we have developed FATES (Flexible Analysis Toolkit for the Exploration of SPMS data), a MATLAB toolkit easily extensible to an array of SPMS designs and data formats. FATES was developed to minimize the computational demands of working with large data sets while still allowing easy maintenance, modification, and utilization by novice programmers. FATES permits scientists to explore, without constraint, complex SPMS data with simple scripts in a language popular for scientific numerical analysis. In addition FATES contains an array of data visualization graphic user interfaces (GUIs) which can aid both novice and expert users in calibration of raw data; exploration of the dependence of mass spectral characteristics on size, time, and peak intensity; and investigations of clustered data sets.