jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vladimir Vutov | Thorsten Dickhaus
Source: Statistics in Medicine (article, 2023-05-12)
Field: Chemistry — Analytical Chemistry

Multiple multi‐sample testing under arbitrary covariance dependency Modern high-throughput biomedical devices routinely produce data on a large scale, and the analysis of high-dimensional datasets has become commonplace in biomedical studies. However, given thousands or tens of thousands of measured variables in these datasets, extracting meaningful features poses a challenge. In this article, we propose a procedure to evaluate the strength of the associations between a nominal (categorical) response variable and multiple features simultaneously. Specifically, we propose a framework of large-scale multiple testing under arbitrary correlation dependency among test statistics. First, marginal multinomial regressions are performed for each feature individually. Second, we use an approach of multiple marginal models for each baseline-category pair to establish asymptotic joint normality of the stacked vector of the marginal multinomial regression coefficients. Third, we estimate the (limiting) covariance matrix between the estimated coefficients from all marginal models. Finally, our approach approximates the realized false discovery proportion of a thresholding procedure for the marginal p-values for each baseline-category logit pair. The proposed approach offers a sensible trade-off between the expected numbers of true and false findings. Furthermore, we demonstrate a practical application of the method on hyperspectral imaging data. This dataset is obtained by a matrix-assisted laser desorption/ionization (MALDI) instrument. MALDI demonstrates tremendous potential for clinical diagnosis, particularly for cancer research. In our application, the nominal response categories represent cancer (sub-)types. Categorical variable ; Marginal model ; Computer science ; Multinomial logistic regression ; Covariance ; Multinomial distribution ; Covariance matrix ; Marginal likelihood ; Statistics ; Feature (linguistics) ; Sample size determination ; Estimator ; Pattern recognition (psychology) ; Mathematics ; Artificial intelligence ; Regression analysis ; Machine learning ; Bayesian probability Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Statistical Methods and Inference
Authors: Marietta Fodor | Zsuzsa Jókai | Anna Matkovits | Eszter Benes
Source: Foods (article, 2023-08-15)
Field: Chemistry — Analytical Chemistry

Assessment of Maturity of Plum Samples Using Fourier Transform Near-Infrared Technique Combined with Chemometric Methods The FT-NIR technique was used for rapid and non-destructive determination of plum ripeness. The dry matter (DM), titratable acidity (TA), total soluble solids (TSS) and calculated maturity index (MI: TSS/TA) were used as reference values. The PLS correlations were validated via five-fold cross-validation (RMSECV for different parameters: DM: 0.66%, <i>w</i>/<i>w</i>; TA = 0.07%, <i>w</i>/<i>w</i>; TSS = 0.72%, <i>w</i>/<i>w</i>; MI = 1.39) and test set validation (RMSEP for different parameters: DM: 0.65%, <i>w</i>/<i>w</i> TA = 0.07%, <i>w</i>/<i>w</i>; TSS = 0.61%, <i>w</i>/<i>w</i>; MI = 1.50). Different classification algorithms were performed for TA, TSS and MI. Linear, quadratic and Mahalanobis discriminant analysis (LDA, QDA, MDA) were found to be the best sample detection methods. The accuracy of the classification methods was 100% for all investigated parameters and cultivars. Mathematics ; Ripeness ; Linear discriminant analysis ; Mahalanobis distance ; Analytical Chemistry (journal) ; Chemistry ; Statistics ; Chromatography ; Food science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Food Drying and Modeling
Authors: Sanjaya Herath | Hashan K. Weerasooriya | Don Yasiru Lakshan Ranasinghe | Wele Gedara Chaminda Bandara | Vijitha Herath | Roshan Godaliyadda | M. P. B. Ekanayake | Terrence Madhujith
Source: Journal of Food Science and Technology (article, 2023-02-27)
Field: Chemistry — Analytical Chemistry

Quantitative assessment of adulteration of coconut oil using transmittance multispectral imaging Multispectral image ; Computer science ; Artificial intelligence ; Multispectral pattern recognition ; Remote sensing ; Environmental science ; Process engineering ; Pattern recognition (psychology) ; Engineering Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ting Tang | Canming Chen | Weibin Wu | Ying Zhang | Chongyang Han | Jie Li | Ting Gao | Jiehao Li
Source: Remote Sensing (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral Inversion Model of Relative Heavy Metal Content in Pennisetum sinese Roxb via EEMD-db3 Algorithm Detection rapidity and model accuracy are the keys to hyperspectral nondestructive testing technology, especially for Pennisetum sinese Roxb (PsR) due to its extremely high adsorptive heavy metal content. The study of the resolution of PsR is conducive to the analysis of the accumulated heavy metal content in its different parts. In this paper, the contents of Cd, Cu and Zn accumulated in the old leaves, young leaves, upper stem, middle stem and lower stem, as well as the hyperspectral data of the corresponding parts, were measured simultaneously in both fresh and dry states. To begin, the spectral data of PsR were preprocessed by using Ensemble Empirical Mode Decomposition-Daubechies3 (EEMD-db3), Savitzky–Golay (SG), Symlet3 (sym3), Symlet5 (sym5), and multiplicative scatter correction (MSC). The 40 samples were divided into 32 training sets and 8 validation sets. The preprocessed spectral data were transformed by the first derivative (FD) and reciprocal logarithm (log(1/R)) to highlight the singularities using binary wavelet decomposition. After screening the significant bands from the correlation curve, the competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were applied to extract the spectral characteristic variables, which were used to establish the partial least-squares (PLS) regression and multiple stepwise linear regression (MSLR) inversion models of Cd, Cu, and Zn contents. Based on EEMD-db3 pretreatment, the inversion model of Zn in the dry (fresh) state had R2 values of 0.884 (0.880), NRMSE values of 0.179 (0.253) and RPD values of 3.191 (3.221), indicating excellent stability and predictive performance. The findings of this study can not only aid in the rapid nondestructive detection of heavy metal adsorption in various parts of PsR, but can also be applied to guide the development and use of animal feed. Hyperspectral imaging ; Algorithm ; Mathematics ; Hilbert–Huang transform ; Logarithm ; Computer science ; Artificial intelligence ; Statistics Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Water Quality Monitoring and Analysis
Authors: István Bíró | M Trojanová | Tomáš Čakurda | Zoltán Fabulya | József Sárosi
Source: Acta Polytechnica Hungarica (article, 2023-01-01)
Field: Materials Science — General Materials Science

Simulation of the Position Control of PAM, Using Its’ Nonlinear Dynamic Model The paper investigates the mathematical, dynamic nonlinear model, of a single Pneumatic Artificial Muscle (PAM).A Fluid muscle from FESTO, is connected vertically as an oscillating non-linear system with 1 degree of freedom.When the muscle is filled with a working medium -compressed air, there is a change in the pressure over time and muscle parameters (change in length, diameter, fiber braiding angle, mass position, etc.).The simulation of the position of mass is the goal of the research described in this article.A mathematical model of the system, considers the change in the spring stiffness and the damping factor.At the same time, the model considers the effect of time pressure changes, on system oscillation.The article provides a few examples, which help specify the positioning model (also identifying values of individual coefficients of quantities occurring in the model). Nonlinear system ; Position (finance) ; Nonlinear model ; Computer science ; Control theory (sociology) ; Control (management) ; Artificial intelligence ; Physics ; Economics Material Properties and Applications ; Innovations in Concrete and Construction Materials ; Robotic Mechanisms and Dynamics
Authors: Yuhang 宇航 Wei 魏 | Dahai 达海 He 贺
Source: Chinese Physics Letters (article, 2023-08-24)
Field: Materials Science — Materials Chemistry

Inverse Design of Phononic Crystal with Desired Transmission via a Gradient-Descent Approach We propose a general approach based on the gradient descent method to study the inverse problem, making it possible to reversely engineer the microscopic configurations of materials that exhibit desired macroscopic properties. Particularly, we demonstrate its application by identifying the microscopic configurations within any given frequency range to achieve transparent phonon transport through one-dimensional harmonic lattices. Furthermore, we obtain the phonon transmission in terms of normal modes and find that the key to achieving phonon transparency or phonon blocking state lies in the ratio of the mode amplitudes at ends. Phonon ; Gradient descent ; Inverse ; Transmission (telecommunications) ; Range (aeronautics) ; Harmonic ; Materials science ; Mode (computer interface) ; Descent (aeronautics) ; Physics ; Condensed matter physics ; Computer science ; Acoustics ; Mathematics ; Geometry ; Telecommunications Thermal properties of materials ; Acoustic Wave Phenomena Research ; Thermal Radiation and Cooling Technologies
Authors: Javier Robledo Moreno | Jeffrey F. Cohn | Dries Sels | Mário Motta
Source: arXiv (Cornell University) (preprint, 2023-02-22)
Field: Materials Science — Materials Chemistry

Enhancing the Expressivity of Variational Neural, and Hardware-Efficient Quantum States Through Orbital Rotations Variational approaches, such as variational Monte Carlo (VMC) or the variational quantum eigensolver (VQE), are powerful techniques to tackle the ground-state many-electron problem. Often, the family of variational states is not invariant under the reparametrization of the Hamiltonian by single-particle basis transformations. As a consequence, the representability of the ground-state wave function by the variational ansatz strongly dependents on the choice of the single-particle basis. In this manuscript we study the joint optimization of the single-particle basis, together with the variational state in the VMC (with neural quantum states) and VQE (with hardware-efficient circuits) approaches. We show that the joint optimization of the single-particle basis with the variational state parameters yields significant improvements in the expressive power and optimization landscape in a variety of chemistry and condensed matter systems. We also realize the first active-space calculation using neural quantum states, where the single-particle basis transformations are applied to all of the orbitals in the basis set. Ansatz ; Variational Monte Carlo ; Hamiltonian (control theory) ; Wave function ; Atomic orbital ; Basis function ; Basis (linear algebra) ; Quantum ; Statistical physics ; Computer science ; Physics ; Quantum mechanics ; Quantum Monte Carlo ; Mathematics ; Monte Carlo method ; Electron ; Mathematical optimization Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Semiconductor materials and devices
Authors: Taylor Q. Nguyen | Gabriela Schneider | Alagammai Kaliappan | Robert Buscaglia | Guy Brock | Melissa Barousse Hall | Donald M. Miller | Jason Chesney | Nichola C. Garbett
Source: Current Oncology (article, 2023-06-24)
Field: Chemistry — Physical and Theoretical Chemistry

Plasma Thermogram Parameters Differentiate Status and Overall Survival of Melanoma Patients Melanoma is the fifth most common cancer in the United States and the deadliest of all skin cancers. Even with recent advancements in treatment, there is still a 13% two-year recurrence rate, with approximately 30% of recurrences being distant metastases. Identifying patients at high risk for recurrence or advanced disease is critical for optimal clinical decision-making. Currently, there is substantial variability in the selection of screening tests and imaging, with most modalities characterized by relatively low accuracy. In the current study, we built upon a preliminary examination of differential scanning calorimetry (DSC) in the melanoma setting to examine its utility for diagnostic and prognostic assessment. Using regression analysis, we found that selected DSC profile (thermogram) parameters were useful for differentiation between melanoma patients and healthy controls, with more complex models distinguishing melanoma patients with no evidence of disease from patients with active disease. Thermogram features contributing to the third principal component (PC3) were useful for differentiation between controls and melanoma patients, and Cox proportional hazards regression analysis indicated that PC3 was useful for predicting the overall survival of active melanoma patients. With the further development and optimization of the classification method, DSC could complement current diagnostic strategies to improve screening, diagnosis, and prognosis of melanoma patients. Medicine ; Melanoma ; Proportional hazards model ; Internal medicine ; Oncology ; Disease ; Cancer ; Dermatology ; Cancer research thermodynamics and calorimetric analyses ; Computational Drug Discovery Methods ; Essential Oils and Antimicrobial Activity
Authors: Gülçin Gündüz | Dilşat Bozdoğan Konuşkan
Source: Journal of Oleo Science (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Fatty Acid and Sterol Compositions of Turkish Monovarietal Olive Oils with Regard to Olive Ripening This study was conducted in crop season of 2018 and the olive fruits from three Turkish varieties Saurani, Karamani and Halhalı under the same pedoclimatic conditions (with no irrigation and no fertilization) were assessed. Oil content, fatty acid, and sterol compositions of three monovarietal 'Halhalı', 'Karamani', and 'Saurani' virgin olive oils were examined at green, spotted and ripe olives. The oil content of olives ranges between 23.77-34.77% and the highest oil yield was observed in the ripe Karamani variety. In terms of fatty acids, the lowest oleic acid values were found in the ripe period of Karamani variety (59.78%), and the highest oleic acid values in the green period of Halhalı variety (69.97%). The oleic and palmitic acid contents decreased, while linoleic and stearic acid contents increased with olive ripening. Total sterol amounts of olive oils varied between 946-1782 mg/kg and showed a significant increase with ripening (p < 0.05). The highest β-sitosterol amount was detected in the green period of Saurani variety (91.66%), and the lowest β-sitosterol amount in the spotted period of Halhalı variety (86.16%). The highest ∆<sup>5</sup>-avenasterol amounts were detected in the ripe period of Saurani variety (6.54%), the lowest ∆<sup>5</sup>-avenasterol amounts were detected in the green period of Halhalı variety (2.36%). Total β-sitosterol, stigmasterol and erythrodiol+uvaol contents of olive oils are changed with ripening. Accordingly, these results showed that fatty acid and sterol compositions can be used as indicators of variety and ripening degree among monovarietal virgin olive oils. Oleic acid ; Ripening ; Sterol ; Palmitic acid ; Food science ; Linoleic acid ; Chemistry ; Fatty acid ; Stearic acid ; Stigmasterol ; Campesterol ; Botany ; Horticulture ; Biology ; Cholesterol ; Biochemistry ; Chromatography Edible Oils Quality and Analysis ; Cholesterol and Lipid Metabolism ; Essential Oils and Antimicrobial Activity
Authors: Denys Oliinyk | Andreas Will | Felix R. Schneidmadel | Sean J. Humphrey | Florian Meier
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-04-06)
Field: Chemistry — Spectroscopy

µPhos: a scalable and sensitive platform for functional phosphoproteomics Abstract Mass spectrometry has revolutionized cell signaling research by vastly simplifying the identification and quantification of many thousands of phosphorylation sites in the human proteome. Defining the cellular response to internal or external perturbations in space and time is crucial for further illuminating functionality of the phosphoproteome. Here we describe µPhos, an accessible phosphoproteomics platform that permits phosphopeptide enrichment from 96-well cell culture experiments in &lt; 8 hours total processing time. By minimizing transfer steps and reducing liquid volumes to &lt; 200 µL, we demonstrate increased sensitivity, over 90% selectivity, and excellent quantitative reproducibility. Employing highly sensitive trapped ion mobility mass spectrometry, we quantify more than 20,000 unique phosphopeptides in a human cancer cell line using 20 µg starting material, and confidently localize &gt; 5,000 phosphorylation sites from 5 µg. This depth covers key intracellular signaling pathways, rendering sample-limited applications and extensive perturbation experiments with hundreds of samples viable. Abstract Figure Phosphoproteomics ; Phosphopeptide ; Phosphorylation ; Chemistry ; Proteome ; Mass spectrometry ; Computational biology ; Cell biology ; Nanotechnology ; Chromatography ; Protein phosphorylation ; Biology ; Biochemistry ; Materials science ; Protein kinase A Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: 徐俊 Xu Jun | 李云飞 Li Yunfei | 程跃 Cheng Yue | 檀剑飞 Tan Jianfei | 刘东 Liu Dong | 周振 Zhou Zhen
Source: Laser & Optoelectronics Progress (article, 2023-01-01)
Field: Chemistry — Spectroscopy

基于TDLAS-WMS的甲烷泄漏遥测系统研制 针对甲烷气体泄漏高精度、非接触和远距离探测的需求，提出一种基于TDLAS-WMS的甲烷泄漏遥测系统。该系统采用高级精简指令集机器（ARM）和现场可编程门阵列（FPGA）双核架构，FPGA实现数字锁相解调提取一次谐波（1f）和二次谐波（2f）信号，ARM通过串行外设接口（SPI）实时接收FPGA解调的1f、2f信号并传送至Labview进行谐波信号分析和甲烷浓度在线解调。通过2f/1f信号处理技术消除光强与靶标反射系数的变化对系统测量结果的影响。研究同等实验条件下0~90 m范围内谐波信号与探测距离的关系，结果表明，2f/1f信号处理技术对噪声抑制效果显著。测量不同积分浓度的谐波信号，采用最小二乘法对2f/1f和气体积分浓度数据进行线性拟合，获得系统标定线性度，为0.9966。对系统误差进行实验测量和分析，在0~2000×10-6 m范围内，系统测量最大相对误差为-3.66%，最小为-0.23%。利用1500×10-6 m甲烷浓度的二次谐波信号评估系统的检测下限为70.5×10-6 m。结果表明，设计和研制的甲烷泄漏遥测系统可广泛应用于城市燃气场站和天然气管网等场合的燃气突发泄漏监测预警。 Computer science Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Photonic Crystal and Fiber Optics
Authors: Constantin Ammar | Julia P. Schessner | Sander Willems | André C. Michaelis | Matthias Mann
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-02-19)
Field: Chemistry — Spectroscopy

Accurate label-free quantification by directLFQ to compare unlimited numbers of proteomes ABSTRACT Recent advances in mass spectrometry (MS)-based proteomics enable the acquisition of increasingly large datasets within relatively short times, which exposes bottlenecks in the bioinformatics pipeline. Whereas peptide identification is already scalable, most label-free quantification (LFQ) algorithms scale quadratic or cubic with the sample numbers, which may even preclude the analysis of large-scale data. Here we introduce directLFQ, a ratiobased approach for sample normalization and the calculation of protein intensities. It estimates quantities via aligning samples and ion traces by shifting them on top of each other in logarithmic space. Importantly, directLFQ scales linearly with the number of samples, allowing analyses of large studies to finish in minutes instead of days or months. We quantify 10,000 proteomes in 10 minutes and 100,000 proteomes in less than two hours - thousand-fold faster than some implementations of the popular LFQ algorithm MaxLFQ. In-depth characterization of directLFQ reveals excellent normalization properties and benchmark results, comparing favorably to MaxLFQ for both data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, directLFQ provides normalized peptide intensity estimates for peptide-level comparisons. It is available as an open-source Python package and as a GUI with a one-click installer and can be used in the AlphaPept ecosystem as well as downstream of most common computational proteomics pipelines. Normalization (sociology) ; Computer science ; Python (programming language) ; Scalability ; Proteome ; Pipeline (software) ; Proteomics ; Data mining ; Bioinformatics ; Biology ; Operating system Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Patryk Różyło | Kuba ROSŁANIEC | М. Kuciej
Source: Advances in Science and Technology – Research Journal (article, 2023-11-20)
Field: Materials Science — General Materials Science

Buckling of Compressed Thin-Walled Composite Structures with Closed Sections The subject of the study was thin-walled composite structures, which were made of carbon-epoxy composite. The structures were characterized by a closed square cross-sectional shape. In the research paper, stability tests of axially compressed thin-walled composite structures were carried out in order to determine the critical state of the structures. Experimental tests were carried out on a universal testing machine, using a system for optical measurement of deformation of structures. Numerical simulations were carried out using the finite element method. The research made it possible to evaluate the work of the structure in the buckling state from a qualitative and quantitative point of view. Buckling ; Composite number ; Materials science ; Composite material ; Structural engineering ; Engineering Material Properties and Applications ; Composite Structure Analysis and Optimization ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Eli J. Larson | Melissa R. Pergande | Michelle E. Moss | Kalina J. Rossler | Rephael Wenger | Boris Krichel | Harini Josyer | Jake A. Melby | David S. Roberts | Kyndalanne Pike | Zhuoxin Shi | Hsin‐Ju Chan | Bridget Knight | Holden T. Rogers | Kyle A. Brown | Irene M. Ong | Kyowon Jeong | Michael T. Marty | Sean J. McIlwain | Ying Ge
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-01-03)
Field: Chemistry — Spectroscopy

MASH Native: A Unified Solution for Native Top-Down Proteomics Data Processing Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking. Herein, we have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a one-stop shop for characterizing both native protein complexes and proteoforms. The MASH Native app, video tutorials, written tutorials and additional documentation are freely available for download at https://labs.wisc.edu/gelab/MASH_Explorer/MASHNativeSoftware.php . All data files shown in user tutorials are included with the MASH Native software in the download .zip file. Computer science ; Software ; Documentation ; Download ; Interface (matter) ; User Friendly ; World Wide Web ; Database ; Programming language ; Operating system Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies