jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kyung Hyun Lee | Shervin Assassi | Chandra Mohan | Claudia Pedroza
Source: BMC Genomics (article, 2024-11-14)
Field: Chemistry — Spectroscopy

Addressing statistical challenges in the analysis of proteomics data with extremely small sample size: a simulation study When the aim of the study is to identify a statistical model that best distinguishes between cohort groups using proteomics data and to uncover the biological pathways and disorders common among the selected proteins, the majority of widely used analysis pipelines perform similarly. However, if the main objective is to pinpoint a set of selected proteins that wield significant influence in discriminating cohort groups and utilize them for subsequent investigations, meticulous consideration is necessary when opting for statistical models, due to the possibility of heterogeneity in the sets of selected proteins. Sample size determination ; Proteomics ; Biology ; Data science ; Computational biology ; Statistical analysis ; Sample (material) ; Statistics ; Bioinformatics ; Computer science ; Genetics ; Mathematics ; Chromatography Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Gene expression and cancer classification
Authors: Mingjia Qu | Zhengwei Gu | Xin Li | Jianbo Wang | Ge Yu | Lingling Yi
Source: Materials (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

Finite Element Simulation and Microstructural Evolution Investigation in Hot Stamping Process of Ti6Al4V Alloy Sheets Titanium alloy hot stamping technology has a wide range of application prospects in the field of titanium alloy part processing due to its high production efficiency and low manufacturing cost. However, the challenges of forming titanium alloy parts with large depths and deformations have restricted its development. In this study, the hot stamping process of a Ti6Al4V alloy box-shaped part was investigated using ABAQUS 2020 software. The thermodynamic properties of a Ti6Al4V alloy sheet were explored at different temperatures (400 °C, 500 °C, 600 °C, 700 °C, 800 °C) and different strain rates (0.1 s<sup>-1</sup>, 0.05 s<sup>-1</sup>, 0.01 s<sup>-1</sup>). In addition, the influence law of hot stamping process parameters on the minimum thickness of the formed part was revealed through the analysis of response surface methodology (RSM), ultimately obtaining the optimal combination of process parameters for Ti6Al4V alloy hot stamping. The experimental results of the hot stamping process exhibited a favorable correlation with the simulated outcomes, confirming the accuracy of the numerical simulation. The study on the microstructure evolution of the formed parts showed that grain refinement strengthening occurred in the part with large deformation, and the formed box-shaped parts exhibited a uniform and fine microstructure overall, demonstrating high forming quality. The achievements of the work provide important guidance for the fabrication of titanium alloy parts with large depths and deformations used in heavy industrial production. Materials science ; Titanium alloy ; Microstructure ; Stamping ; Alloy ; Metallurgy ; Deformation (meteorology) ; Hot stamping ; Titanium ; Fabrication ; Finite element method ; Grain size ; Composite material ; Structural engineering ; Engineering Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Additive Manufacturing Materials and Processes
Authors: Allison B. Esselman | Megan S. Ward | Cody R. Marshall | Ellie L. Pingry | Martin Dufresne | Melissa A. Farrow | Matthew Schrag | Jeffrey M. Spraggins
Source: Journal of the American Society for Mass Spectrometry (article, 2024-11-07)
Field: Chemistry — Spectroscopy

A Streamlined Workflow for Microscopy-Driven MALDI Imaging Mass Spectrometry Data Collection Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a rapidly advancing technology for biomedical research. As spatial resolution increases, however, so do acquisition time, file size, and experimental cost, which increases the need to perform precise sampling of targeted tissue regions to optimize the biological information gleaned from an experiment and minimize wasted resources. The ability to define instrument measurement regions based on key tissue features and automatically sample these specific regions of interest (ROIs) addresses this challenge. Herein, we demonstrate a workflow using standard software that allows for direct sampling of microscopy-defined regions by MALDI IMS. Three case studies are included, highlighting different methods for defining features from common sample types─manual annotation of vasculature in human brain tissue, automated segmentation of renal functional tissue units across whole slide images using custom segmentation algorithms, and automated segmentation of dispersed HeLa cells using open-source software. Each case minimizes data acquisition from unnecessary sample regions and dramatically increases throughput while uncovering molecular heterogeneity within targeted ROIs. This workflow provides an approachable method for spatially targeted MALDI IMS driven by microscopy as part of multimodal molecular imaging studies. Mass spectrometry imaging ; Workflow ; MALDI imaging ; Software ; Segmentation ; Computer science ; Matrix-assisted laser desorption/ionization ; Mass spectrometry ; Sample (material) ; Sampling (signal processing) ; Chemistry ; Microscopy ; Sample preparation ; Automation ; Artificial intelligence ; Pattern recognition (psychology) ; Data mining ; Computer vision ; Chromatography ; Desorption ; Database ; Pathology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Djoudi Ourdani | Aloïs Castellano | Ashwin Kavilen Vythelingum | Jon Ander Arregi | Vojtěch Uhlíř | B. Perrin | M. Belmeguenai | Y. Roussigné | C. Gourdon | Matthieu J. Verstraete | L. Thevenard
Source: Physical review. B./Physical review. B (article, 2024-07-18)
Field: Materials Science — Materials Chemistry

Experimental determination of the temperature- and phase-dependent elastic constants of FeRh The elastic constants of an epitaxial film of FeRh have been determined experimentally in both ferromagnetic (FM) and antiferromagnetic (AF) phases, using a combination of Brillouin light scattering and picosecond acoustics experiments. The ${C}_{11}$ constant is noticeably larger in the FM phase than in the AF phase, while ${C}_{12}$ and ${C}_{44}$ are both lower, leading to larger Rayleigh wave velocities in the FM phase than in the AF phase. The elastic constants were calculated numerically using first-principles anharmonic modeling and machine-learned interatomic potentials. We find that using a temperature-dependent effective potential is indispensable to correctly reproduce the experimental values to within 80--100%. The accurate knowledge of the temperature- and phase-dependences of the elastic constants of crystalline FeRh are valuable ingredients for the predictive modeling of the acoustic and magnetoacoustic properties of this magnetostrictive material. Materials science ; Phase (matter) ; Thermodynamics ; Condensed matter physics ; Physics ; Quantum mechanics Nuclear Materials and Properties ; High-pressure geophysics and materials ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Tatsunori Matsumoto | Lin Du | Francesca Rodino | Yann Thoma | Chinthaka Premachandra | Sandro Carrara
Source: IEEE Sensors Letters (article, 2024-08-29)
Field: Chemistry — Analytical Chemistry

Optimized Quantification of Multiple Drug Concentrations by WeightedMSE With Machine Learning on Electrochemical Sensor Quantification of multiple drugs is of great importance and urgently needed in therapeutic drug monitoring (TDM) and personalized therapy. Especially, based on cyclic voltammograms (CVs) obtained by electrochemical sensors, the use of artificial neural networks (ANNs) has been widely attempted in the accurate quantification of drug concentrations, enabling the development of point-of-care and potentially system-level wearable devices. However, most of the work only considers the accuracy of how the predicted value is close to the actual value, which does not consider whether the predicted drug concentration is underestimated. In practical drug quantification, potential toxicity due to overexposure with underestimated quantification can lead to endangering the patient's body. Therefore, avoiding underestimating the concentration of drugs based on existing quantification models is required and necessary to optimize the conventional loss function at the output stage of ANN. In this letter, a novel loss function based on mean squared error (MSE), WeightedMSE, is proposed for avoiding underestimated quantification. It can be changed flexibly by adjusting parameters in order to adapt the acceptable overestimation range corresponding to the different types of drugs. A simulated dataset and a real dataset of etoposide and methotrexate are used as drug models, demonstrating that the proposed method can avoid underestimation in predicted values by over 98% in quantifying the concentration of multiple drugs and showing significant effectiveness for the development of point-of-care and wearable monitoring systems. Drug ; Computer science ; Electrochemistry ; Electrochemical gas sensor ; Machine learning ; Artificial intelligence ; Chemistry ; Medicine ; Pharmacology ; Electrode Analytical Methods in Pharmaceuticals ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Dorin Maier | Daniela Lucia Manea | Roxana Tamas-Gavrea | Tiriac Alexandra | Paul Costin
Source: Preprints.org (preprint, 2024-09-12)
Field: Materials Science — Polymers and Plastics

Wood-Cement Composites: A Sustainable Approach for Mitigating Environmental Impact in Construction There is a growing awareness of the detrimental environmental impact of the construction industry, primarily due to the utilization of building materials that necessitate energy-intensive processes for their production. The objective of this paper is to explore whether mixing wood and cement could serve as a viable solution to mitigate this adverse environmental effect. The data was obtained by extensively investigating the Clarivate Web of Science database, adhering to the PRISMA technique guidelines, and subsequently processed using Bibliometrix and VOSviewer software. An in-depth critical analysis of the literature revealed a heightened interest in this subject matter in recent years, with the focal points evolving from initial experimental endeavors aimed at understanding the behavior of the innovative wood cement composites, primarily in terms of their mechanical properties, to assessing their environmental impact and the advantages associated with their use. The researchers suggest that optimal results can be achieved by treating the wood with tetraethyl orthosilicate, incorporating cellulose nanocrystal particles or wollastonite into the mixture, and utilizing wood from the Pinus species. Furthermore, it was discovered that a noticeable improvement in strength is only attainable if a low percentage of wood, up to 30%, when wood ash is employed. Ongoing research concerning wood and cement composites continues to progress, with regular advancements and breakthroughs, thus offering promising prospects for the development of superior and eco-friendly building materials. Cement ; Environmental impact assessment ; Materials science ; Environmental science ; Composite material ; Ecology Natural Fiber Reinforced Composites ; Wood Treatment and Properties
Authors: Muh Farid | Muhammad Fuad Anshori | Riccardo Rossi | Feranita Haring | Katriani Mantja | Andi Dirpan | Siti Halimah Larekeng | Marlina Mustafa | Adnan Adnan | Siti Antara Maedhani Tahara | Nirwansyah Amier | M. A. Amal | Andi Isti Sakinah
Source: Agronomy (article, 2024-02-06)
Field: Chemistry — Analytical Chemistry

Combining Image-Based Phenotyping and Multivariate Analysis to Estimate Fruit Fresh Weight in Segregation Lines of Lowland Tomatoes The fruit weight is an important guideline for breeders and farmers to increase marketable productions, although conventionally it requires destructive measurements. The combination of image-based phenotyping (IBP) approaches with multivariate analysis has the potential to further improve the line selection based on economical trait, like fruit weight. Therefore, this study aimed to evaluate the potential of image-derived phenotypic traits as proxies for individual fruits weight estimation using multivariate analysis. To this end, an IBP experimentation was carried out on five populations of low-land tomato. Specifically, the Mawar (M; 10 plants), Karina (K; 10 plants), and F2 generation cross (100 lines) samples were used to extract training data for the proposed estimation model, while data derived from M/K//K backcross population (35 lines) and F5 population (50 lines) plants were used for destructive and non-destructive validation, respectively. Several phenotypic traits were extracted from each imaged tomato fruit, including the slice and whole fruit area (FA), round (FR), width (FW), height (FH), and red (RI), green (GI) and blue index (BI), and used as inputs of a genetic- and multivariate-based method for non-destructively predicting its fresh weight (FFW). Based on this research, the whole FA has the greatest potential in predicting tomato FFW regardless to the analyzed cultivar. The relevant model exhibited high power in predicting FFW, as explained by R2-adjusted, R2-deviation and RMSE statistics obtained for calibration (81.30%, 0.20%, 3.14 g, respectively), destructive (69.80%, 0.90%, 4.46 g, respectively) and non-destructive validation (80.20%, 0.50%, 2.12 g, respectively). These results suggest the potential applicability of the proposed IBP approach in guiding field robots or machines for precision harvesting based on non-destructive estimations of fruit weight from image-derived area, thereby enhancing agricultural practices in lowland tomato cultivation. Multivariate statistics ; Multivariate analysis ; Horticulture ; Biology ; Agronomy ; Statistics ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Potato Plant Research
Authors: 许子怡 Xu Ziyi | Shuo Liu
Source: Laser & Optoelectronics Progress (article, 2024-01-01)
Field: Chemistry — Spectroscopy

基于质谱和光谱分析的同位素气体检测技术研究进展 气体同位素丰度检测在工业生产、生物医疗、地质勘测等方面均具有重要的研究价值，能够应用于工业安全在线监控、依据人体呼出气体实时临床无创诊断等方面。首先介绍同位素气体检测技术的应用价值和应用领域，证实同位素气体检测具有广阔的应用前景。接着阐述了同位素丰度的内涵，然后从质谱分析技术和光谱分析技术两方面出发，介绍了质谱分析、光声光谱、可调谐半导体激光吸收光谱、腔衰荡光谱和腔增强吸收光谱检测技术这五种同位素丰度探测手段，分别阐释了不同检测技术的相关原理和近年来同位素气体检测的研究进展。通过对比五种同位素丰度检测技术的性能，分析得到各同位素气体检测技术的优缺点和适用场景，并提出未来同位素气体检测技术的研究展望。 Computer science Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Haina Shen | Xiaofen Ji
Source: Autex Research Journal (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Optimization of garment sewing operation standard minute value prediction using an IPSO-BP neural network Abstract Standard minute value serves as a pivotal metric guiding the arrangement and balancing of production cycles in clothing production lines, and plays a crucial role in cost pricing and production order arrangement for clothing products. Given the complexity of the garment sewing process, ten influencing factors including fabric weight, fabric thickness, fabric density, stitching length, stitching shapes, cut pieces numbers, notch numbers, sewing technologies, sewing machine, and auxiliary accessories were identified. Upon this foundation, a standard sewing time prediction model, Improved particle swarm optimization - Back-propagation neural network (IPSO-BP), was proposed, focusing on non-quantitative factors. The IPSO-BP model was trained using actual sewing data from a women’s clothing production company. Compared to the unoptimized BP neural network, the IPSO-BP model demonstrated significant advantages in terms of convergence speed and prediction accuracy. Therefore, the IPSO-BP model proposed in this study holds promise for predicting standard sewing hours effectively. Image stitching ; Sewing machine ; Artificial neural network ; Clothing ; Clothing industry ; Particle swarm optimization ; Production (economics) ; Convergence (economics) ; Process (computing) ; Metric (unit) ; Benchmark (surveying) ; Engineering ; Engineering drawing ; Simulation ; Computer science ; Industrial engineering ; Artificial intelligence ; Algorithm ; Mechanical engineering ; Operations management Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; 3D Shape Modeling and Analysis
Authors: Jiarong Wang | Wenxiu Wang | Wenya Xu | Huanjiong An | Qianyun Ma | Jianfeng Sun | Jie Wang
Source: Frontiers in Nutrition (article, 2024-01-24)
Field: Chemistry — Analytical Chemistry

Fusing hyperspectral imaging and electronic nose data to predict moisture content in Penaeus vannamei during solar drying The control of moisture content (MC) is essential in the drying of shrimp, directly impacting its quality and shelf life. This study aimed to develop an accurate method for determining shrimp MC by integrating hyperspectral imaging (HSI) with electronic nose (E-nose) technology. We employed three different data fusion approaches: pixel-, feature-, and decision-fusion, to combine HSI and E nose data for the prediction of shrimp MC. We developed partial least squares regression (PLSR) models for each method and compared their performance in terms of prediction accuracy. The decision fusion approach outperformed the other methods, producing the highest determination coefficients for both calibration (0.9595) and validation sets (0.9448). Corresponding root-mean square errors were the lowest for the calibration set (0.0370) and validation set (0.0443), indicating high prediction precision. Additionally, this approach achieved a relative percent deviation of 3.94, the highest among the methods tested. The findings suggest that the decision fusion of HSI and E nose data through a PLSR model is an effective, accurate, and efficient method for evaluating shrimp MC. The demonstrated capability of this approach makes it a valuable tool for quality control and market monitoring of dried shrimp products. Partial least squares regression ; Hyperspectral imaging ; Electronic nose ; Calibration ; Shrimp ; Sensor fusion ; Data set ; Artificial intelligence ; Water content ; Fusion ; Mathematics ; Environmental science ; Pattern recognition (psychology) ; Computer science ; Remote sensing ; Statistics ; Engineering ; Ecology ; Biology ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Abdul Haseeb | Miguel X. Fernandes | Jörgen Samuelsson
Source: Journal of Chromatography A (article, 2024-06-06)
Field: Chemistry — Spectroscopy

Modelling the pH dependent retention and competitive adsorption of charged and ionizable solutes in mixed-mode and reversed-phase liquid chromatography This study investigated the influence of pH on the retention of solutes using a mixed-mode column with carboxyl (-COOH) groups acting as weak cation exchanger bonded to the terminal of C18 ligands (C18-WCX column) and a traditional reversed-phase C18 column. First, a model based on electrostatic theory was derived and successfully used to predict the retention of charged solutes (charged, and ionizable) as a function of mobile phase pH on a C18-WCX column. While the Horváth model predicts the pH-dependent retention of ionizable solutes in reversed-phase liquid chromatography (RPLC) solely based on solute ionization, the developed model incorporates the concept of surface potential generated on the surface of the stationary phase and its variation with pH. To comprehensively understand the adsorption process, adsorption isotherms for these solutes were individually acquired on the C18-WCX and reversed-phase C18 columns. The adsorption isotherms followed the Langmuir model for the uncharged solute and the electrostatically modified Langmuir model for charged solutes. The elution profiles for the single components were calculated from these isotherms using the equilibrium dispersion column model and were found to be in close agreement with the experimental elution profiles. To enable modelling of two-component cases involving charged solute(s), a competitive adsorption isotherm model based on electrostatic theory was derived. This model was later successfully used to calculate the elution profiles of two components for scenarios involving (a) a C18 Column: two charged solutes, (b) a C18 Column: one charged and one uncharged solute, and (c) a C18-WCX Column: two charged solutes. The strong alignment between the experimental and calculated elution profiles in all three scenarios validated the developed competitive adsorption model. Chemistry ; Chromatography ; Adsorption ; Reversed-phase chromatography ; Phase (matter) ; Hydrophilic interaction chromatography ; High-performance liquid chromatography ; Mixed mode ; Organic chemistry Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Protein purification and stability
Authors: Yuming Jiang | Jesse G. Meyer
Source: Journal of Proteome Research (article, 2024-07-15)
Field: Chemistry — Spectroscopy

Rapid Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry Noninvasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography-mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method that integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein corona enrichment for high-throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 min collection time, which enables a potential throughput of approximately 1000 samples daily. The identified proteins are involved in valuable pathways, and 44 of the proteins are FDA-approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation of 17%. Moreover, different protein corona profiles were observed among various NPs based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichment. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies. Mass spectrometry ; Proteome ; Chemistry ; Chromatography ; Proteomics ; Nanoparticle ; Plasma ; Tandem mass spectrometry ; Materials science ; Nanotechnology ; Biochemistry ; Physics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis