Development and Selection of Integrative Measurement Descriptors: Application for Investigating Physical Properties of Biopolymers in Hairs

Abstract Integrative measurement analysis of complex subjects, such as polymers is a major challenge to obtain comprehensive understanding of the properties. In this study, we describe analytical strategies to extract and selectively associate compositional information measured by multiple analytical techniques, aiming to reveal their relationships with physical properties of biopolymers derived from hair. Hair samples were analyzed by multiple techniques, including solid-state nuclear magnetic resonance (NMR), time-domain NMR, Fourier transform infrared spectroscopy, and thermogravimetric and differential thermal analysis. The measured data were processed by different processing techniques, such as spectral differentiation and deconvolution, and then converted into a variety of “measurement descriptors” with different compositional information. The descriptors were associated with the mechanical properties of hair by constructing prediction models using machine learning algorithms. Herein, the stepwise model refinement based on importance evaluation identified the most contributive descriptors, which provided an integrative interpretation about the compositional factors, such as a-helix keratins in cortex; and bounded water and thermal resistant components in cuticle. These results demonstrated the efficacy of the present strategy to generate and select descriptors from manifold measured data for investigating the nature of sophisticated subjects, such as hair.

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Integrative measurement analysis via machine learning descriptor selection for investigating physical properties of biopolymers in hairs

Scientific Reports ◽

10.1038/s41598-021-03793-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ayari Takamura ◽

Kaede Tsukamoto ◽

Kenji Sakata ◽

Jun Kikuchi

Keyword(s):

Machine Learning ◽

Physical Properties ◽

Prediction Models ◽

Analytical Techniques ◽

Measured Data ◽

Machine Learning Algorithms ◽

Measurement Analysis ◽

Descriptor Selection ◽

Hair Samples ◽

Compositional Information

AbstractIntegrative measurement analysis of complex subjects, such as polymers is a major challenge to obtain comprehensive understanding of the properties. In this study, we describe analytical strategies to extract and selectively associate compositional information measured by multiple analytical techniques, aiming to reveal their relationships with physical properties of biopolymers derived from hair. Hair samples were analyzed by multiple techniques, including solid-state nuclear magnetic resonance (NMR), time-domain NMR, Fourier transform infrared spectroscopy, and thermogravimetric and differential thermal analysis. The measured data were processed by different processing techniques, such as spectral differentiation and deconvolution, and then converted into a variety of “measurement descriptors” with different compositional information. The descriptors were associated with the mechanical properties of hair by constructing prediction models using machine learning algorithms. Herein, the stepwise model refinement via selection of adopted descriptors based on importance evaluation identified the most contributive descriptors, which provided an integrative interpretation about the compositional factors, such as α-helix keratins in cortex; and bounded water and thermal resistant components in cuticle. These results demonstrated the efficacy of the present strategy to generate and select descriptors from manifold measured data for investigating the nature of sophisticated subjects, such as hair.

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Instant 99mTc Compounds

Nuklearmedizin ◽

10.1055/s-0038-1624749 ◽

1971 ◽

Vol 10 (03) ◽

pp. 245-251 ◽

Cited By ~ 1

Author(s):

P. Richards ◽

W. C. Eckelman

Keyword(s):

Physical Properties ◽

High Purity ◽

Analytical Techniques ◽

Full Potential ◽

Labeled Compounds ◽

Ph Adjustment ◽

One Step ◽

Stannous Ion ◽

99Mtc Radiopharmaceuticals ◽

Potential Use

SummaryThe full potential use of technetium has not been achieved despite its ideal physical properties, dosimetry and availability because of the complex preparations required for 99mTc radiopharmaceuticals. One of the goals of our work is to develop techniques for the preparation of high-purity 99mTc compounds which can be easily prepared, ideally by adding pertechnetate to a prepared solution.The use of stannous ion as reducing agent for technetium makes it possible to obtain such one-step, high-purity products. All non-radioactive components can be premixed in a single vial before addition of the radioactive pertechnetate. No final pH adjustment, further chemical manipulation or purification is required.Procedures for two instantly labeled compounds have been developed to date: 99mTc DTPA and 99mTc HSA. The 99mTc DTPA is prepared by adding pertechnetate to a previously prepared solution of stannous ion and CaNa3 DTPA which has been stored at pH 4. The 99mTc HSA is prepared by adding pertechnetate to a solution of stannous ion and HSA. The parametric variations and analytical techniques involved in formulating these procedures are described. It appears that development of kits for other biologically interesting compounds may be possible using similar procedures.

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Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study (Preprint)

10.2196/preprints.11728 ◽

2018 ◽

Author(s):

Liyan Pan ◽

Guangjian Liu ◽

Xiaojian Mao ◽

Huixian Li ◽

Jiexin Zhang ◽

...

Keyword(s):

Machine Learning ◽

Retrospective Study ◽

Random Forest ◽

Precocious Puberty ◽

Prediction Models ◽

Central Precocious Puberty ◽

Machine Learning Algorithms ◽

Stimulation Test ◽

Gnrh Analogue ◽

Prediction Probability

BACKGROUND Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis—gonadotropin-releasing hormone (GnRH)–stimulation test or GnRH analogue (GnRHa)–stimulation test—is expensive and makes patients uncomfortable due to the need for repeated blood sampling. OBJECTIVE We aimed to combine multiple CPP–related features and construct machine learning models to predict response to the GnRHa-stimulation test. METHODS In this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models. RESULTS Both the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability. CONCLUSIONS The prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.

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Development of Machine Learning Models for Prediction of Smoking Cessation Outcome

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18052584 ◽

2021 ◽

Vol 18 (5) ◽

pp. 2584

Author(s):

Cheng-Chien Lai ◽

Wei-Hsin Huang ◽

Betty Chia-Chen Chang ◽

Lee-Ching Hwang

Keyword(s):

Machine Learning ◽

Smoking Cessation ◽

Success Rate ◽

Prediction Models ◽

Smoking Status ◽

Medical Center ◽

Machine Learning Algorithms ◽

Classification And Regression Tree ◽

Support Vector ◽

Smoking Cessation Outcome

Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machine learning algorithms to predict the outcome of smoking cessation. Data was acquired from patients underwent smoking cessation program at one medical center in Northern Taiwan. A total of 4875 enrollments fulfilled our inclusion criteria. Models with artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LoR), k-nearest neighbor (KNN), classification and regression tree (CART), and naïve Bayes (NB) were trained to predict the final smoking status of the patients in a six-month period. Sensitivity, specificity, accuracy, and area under receiver operating characteristic (ROC) curve (AUC or ROC value) were used to determine the performance of the models. We adopted the ANN model which reached a slightly better performance, with a sensitivity of 0.704, a specificity of 0.567, an accuracy of 0.640, and an ROC value of 0.660 (95% confidence interval (CI): 0.617–0.702) for prediction in smoking cessation outcome. A predictive model for smoking cessation was constructed. The model could aid in providing the predicted success rate for all smokers. It also had the potential to achieve personalized and precision medicine for treatment of smoking cessation.

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Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves

Scientific Reports ◽

10.1038/s41598-021-83847-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hiroto Yamashita ◽

Rei Sonobe ◽

Yuhei Hirono ◽

Akio Morita ◽

Takashi Ikka

Keyword(s):

Short Wave ◽

Machine Learning Algorithms ◽

Estimation Methods ◽

Chemical Information ◽

Tea Quality ◽

The Mean ◽

Processing Techniques ◽

Spectral Patterns ◽

Physical And Chemical ◽

Non Destructive

AbstractSpectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.

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Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients

Acta Epileptologica ◽

10.1186/s42494-020-00035-9 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Shijun Yang ◽

Bin Wang ◽

Xiong Han

Keyword(s):

Antiepileptic Drugs ◽

Statistical Models ◽

Prediction Models ◽

External Validation ◽

Machine Learning Algorithms ◽

Treatment Withdrawal ◽

Patient Treatment ◽

Recent Developments ◽

Progression Of Disease ◽

Patients With Epilepsy

AbstractAlthough antiepileptic drugs (AEDs) are the most effective treatment for epilepsy, 30–40% of patients with epilepsy would develop drug-refractory epilepsy. An accurate, preliminary prediction of the efficacy of AEDs has great clinical significance for patient treatment and prognosis. Some studies have developed statistical models and machine-learning algorithms (MLAs) to predict the efficacy of AEDs treatment and the progression of disease after treatment withdrawal, in order to provide assistance for making clinical decisions in the aim of precise, personalized treatment. The field of prediction models with statistical models and MLAs is attracting growing interest and is developing rapidly. What’s more, more and more studies focus on the external validation of the existing model. In this review, we will give a brief overview of recent developments in this discipline.

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57 Precision neoantigen discovery using novel algorithms and expanded HLA-ligandome datasets

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0057 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A62-A62

Author(s):

Dattatreya Mellacheruvu ◽

Rachel Pyke ◽

Charles Abbott ◽

Nick Phillips ◽

Sejal Desai ◽

...

Keyword(s):

Machine Learning ◽

Cell Lines ◽

Antigen Processing ◽

Large Scale ◽

Prediction Models ◽

K562 Cells ◽

Machine Learning Algorithms ◽

Training Data ◽

High Quality ◽

Tissue Samples

BackgroundAccurately identified neoantigens can be effective therapeutic agents in both adjuvant and neoadjuvant settings. A key challenge for neoantigen discovery has been the availability of accurate prediction models for MHC peptide presentation. We have shown previously that our proprietary model based on (i) large-scale, in-house mono-allelic data, (ii) custom features that model antigen processing, and (iii) advanced machine learning algorithms has strong performance. We have extended upon our work by systematically integrating large quantities of high-quality, publicly available data, implementing new modelling algorithms, and rigorously testing our models. These extensions lead to substantial improvements in performance and generalizability. Our algorithm, named Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™), is integrated into the ImmunoID NeXT Platform®, our immuno-genomics and transcriptomics platform specifically designed to enable the development of immunotherapies.MethodsIn-house immunopeptidomic data was generated using stably transfected HLA-null K562 cells lines that express a single HLA allele of interest, followed by immunoprecipitation using W6/32 antibody and LC-MS/MS. Public immunopeptidomics data was downloaded from repositories such as MassIVE and processed uniformly using in-house pipelines to generate peptide lists filtered at 1% false discovery rate. Other metrics (features) were either extracted from source data or generated internally by re-processing samples utilizing the ImmunoID NeXT Platform.ResultsWe have generated large-scale and high-quality immunopeptidomics data by using approximately 60 mono-allelic cell lines that unambiguously assign peptides to their presenting alleles to create our primary models. Briefly, our primary ‘binding’ algorithm models MHC-peptide binding using peptide and binding pockets while our primary ‘presentation’ model uses additional features to model antigen processing and presentation. Both primary models have significantly higher precision across all recall values in multiple test data sets, including mono-allelic cell lines and multi-allelic tissue samples. To further improve the performance of our model, we expanded the diversity of our training set using high-quality, publicly available mono-allelic immunopeptidomics data. Furthermore, multi-allelic data was integrated by resolving peptide-to-allele mappings using our primary models. We then trained a new model using the expanded training data and a new composite machine learning architecture. The resulting secondary model further improves performance and generalizability across several tissue samples.ConclusionsImproving technologies for neoantigen discovery is critical for many therapeutic applications, including personalized neoantigen vaccines, and neoantigen-based biomarkers for immunotherapies. Our new and improved algorithm (SHERPA) has significantly higher performance compared to a state-of-the-art public algorithm and furthers this objective.

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Multidimensional Analytical Characterization of Water-Soluble Organic Aerosols: Challenges and New Perspectives

Applied Sciences ◽

10.3390/app11062539 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2539

Author(s):

Regina M. B. O. Duarte ◽

João T. V. Matos ◽

Armando C. Duarte

Keyword(s):

High Resolution ◽

High Resolution Mass Spectrometry ◽

Organic Aerosols ◽

Analytical Techniques ◽

Water Soluble ◽

Chemical Complexity ◽

Molecular Features ◽

High Resolution Mass ◽

Analytical Strategies ◽

Resolution Mass

Water-soluble organic aerosols (OA) are an important component of air particles and one of the key drivers that impact both climate and human health. Understanding the processes involving water-soluble OA depends on how well the chemical composition of this aerosol component is decoded. Yet, obtaining detailed information faces several challenges, including water-soluble OA collection, extraction, and chemical complexity. This review highlights the multidimensional non-targeted analytical strategies that have been developed and employed for providing new insights into the structural and molecular features of water-soluble organic components present in air particles. First, the most prominent high-resolution mass spectrometric methods for near real-time measurements of water-soluble OA and their limitations are discussed. Afterward, a special emphasis is given to the degree of compositional information provided by offline multidimensional analytical techniques, namely excitation–emission (EEM) fluorescence spectroscopy, high-resolution mass spectrometry and two-dimensional nuclear magnetic resonance (NMR) spectroscopy and their hyphenation with chromatographic systems. The major challenges ahead on the application of these multidimensional analytical strategies for OA research are also addressed so that they can be used advantageously in future studies.

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A Robust Method to Predict Fluid Properties Based on Big Data and Machine Learning Algorithms

10.2523/iptc-21356-ms ◽

2021 ◽

Author(s):

Yingxian Liu ◽

Cunliang Chen ◽

Hanqing Zhao ◽

Yu Wang ◽

Xiaodong Han

Keyword(s):

Machine Learning ◽

Physical Properties ◽

Learning Algorithm ◽

Direct Method ◽

Learning Algorithms ◽

Small Error ◽

Machine Learning Algorithms ◽

Well Test ◽

Empirical Formulas ◽

Fluid Properties

Abstract Fluid properties are key factors for predicting single well productivity, well test interpretation and oilfield recovery prediction, which directly affect the success of ODP program design. The most accurate and direct method of acquisition is underground sampling. However, not every well has samples due to technical reasons such as excessive well deviation or high cost during the exploration stage. Therefore, analogies or empirical formulas have to be adopted to carry out research in many cases. But a large number of oilfield developments have shown that the errors caused by these methods are very large. Therefore, how to quickly and accurately obtain fluid physical properties is of great significance. In recent years, with the development and improvement of artificial intelligence or machine learning algorithms, their applications in the oilfield have become more and more extensive. This paper proposed a method for predicting crude oil physical properties based on machine learning algorithms. This method uses PVT data from nearly 100 wells in Bohai Oilfield. 75% of the data is used for training and learning to obtain the prediction model, and the remaining 25% is used for testing. Practice shows that the prediction results of the machine learning algorithm are very close to the actual data, with a very small error. Finally, this method was used to apply the preliminary plan design of the BZ29 oilfield which is a new oilfield. Especially for the unsampled sand bodies, the fluid physical properties prediction was carried out. It also compares the influence of the analogy method on the scheme, which provides potential and risk analysis for scheme design. This method will be applied in more oil fields in the Bohai Sea in the future and has important promotion value.

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High Tc Oxide Superconductors

MRS Bulletin ◽

10.1557/s0883769400059479 ◽

1990 ◽

Vol 15 (6) ◽

pp. 31-33

Author(s):

M. Brian Maple

Keyword(s):

Physical Properties ◽

Energy Gap ◽

Vortex State ◽

Oxide Superconductors ◽

High Tc ◽

Superconducting Energy Gap ◽

Consistent Picture ◽

Novel Processing ◽

Processing Techniques

This issue of the MRS BULLETIN is devoted to high Tc superconductivity. It is the sequel to a previous series of articles on the same subject which appeared in the MRS BULLETIN in January 1989. While the articles in the January 1989 issue emphasized the families of high Tc superconducting oxides known at that rime, as well as novel processing techniques and thin films, the papers in this issue focus on the physical properties of high Tc oxide superconductors.The quality of polycrystalline and single-crystal bulk and thin-film materials has improved to the point where researchers can now make reliable measurements of many physical properties representative of the intrinsic behavior of these materials. As a result, a broad spectrum of important issues such as the nature of the electronic structure, the type of superconducting electron pairing, the magnitude and temperature dependence of the superconducting energy gap, the behavior of fluxoids in the vortex state, etc., can be addressed meaningfully. Presently emerging is a consistent picture of the physical properties of the high Tc oxides, which will form the foundation to eventually developing an appropriate theory for the normal and superconducting states of these remarkable materials.

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