jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pang-ning Teng | J. Schaaf | Tamara Abulez | Brian L. Hood | Katlin N. Wilson | Tracy J. Litzi | David Mitchell | Kelly A. Conrads | Allison L. Hunt | Victoria Olowu | Julie Oliver | Fred S. Park | Marshé Edwards | AiChun Chiang | Praveen Kumar | Christopher M. Tarney | Kathleen M. Darcy | Neil T. Phippen | G. Larry Maxwell | Thomas P. Conrads | Nicholas W. Bateman
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-07-29)
Field: Chemistry — Spectroscopy

ProteoMixture: A Cell Type Deconvolution Tool for Bulk Tissue Proteomics Data Summary Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms, ESTIMATE and Consensus TME (r ≥ 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomics data are available at ( https://lmdomics.org/ProteoMixture/ . Laser capture microdissection ; Proteomics ; Deconvolution ; Proteome ; Cell type ; Biology ; Stroma ; Computational biology ; Interactome ; Quantitative proteomics ; Cell ; Transcriptome ; Bioinformatics ; Gene expression ; Computer science ; Gene ; Immunology ; Algorithm ; Genetics ; Immunohistochemistry Advanced Proteomics Techniques and Applications ; Ferroptosis and cancer prognosis ; Cancer, Lipids, and Metabolism
Authors: Andry Clarel Raobelina | Gilles Chaix | Andriambelo Radonirina Razafimahatratra | Sarobidy Pascal Rakotoniaina | Tahiana Ramananantoandro
Source: Wood and Fiber Science (article, 2023-07-23)
Field: Chemistry — Organic Chemistry

Use of a Portable Near Infrared Spectrometer for Wood Identification of Four Dalbergia Species from Madagascar This study focused on the use of Near InfraRed (NIR) Spectroscopy to address the lack of tools and skills for wood identiﬁcation of Dalbergia species from Madagascar. Two sample sets of 41 wood blocks and 41 wood cores belonging to four Dalbergia species (D. abrahamii, D. chlorocarpa, D.greveana, and D. pervillei) were collected in the northern and western regions of Madagascar. Sapwood and heartwood NIR spectra were measured onwood at 12% moisture content by using a portable VIAVI MicroNIR 1700 spectrometer. Four discrimination models corresponding to sapwood and heartwood of the two sample forms were developed using Partial Least Square Discriminant Analysis (PLSDA). Good accuracy of 83.3% and 81.8% were obtained from the heartwood-based PLSDA models respectively for wood blocks and wood cores samples. All D. chlorocarpa samples were well-classiﬁed by the two models. Results highlighted the potential of portable NIR Spectroscopy as a helpful tool tosupport sustain- able management and trade of Madagascar’s Dalbergia species. Further studies are, however, needed for its operational use in identiﬁcation routine. Dalbergia ; Species identification ; Hardwood ; Spectrometer ; Near-infrared spectroscopy ; Botany ; Softwood ; Chemistry ; Environmental science ; Biology ; Physics ; Optics ; Zoology Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Spectroscopy and Chemometric Analyses
Authors: Chenhao Cui | Nicola Caporaso | Jiawei Chen | Tom Fearn
Source: Journal of Food Engineering (article, 2023-08-10)
Field: Chemistry — Analytical Chemistry

Farinograph characteristics of wheat flour predicted by near infrared spectroscopy with an ensemble modelling method Farinograph ; Partial least squares regression ; Wheat flour ; Mathematics ; Softening ; Absorption of water ; Statistics ; Chemistry ; Food science ; Materials science ; Composite material Spectroscopy and Chemometric Analyses ; Food composition and properties ; Meat and Animal Product Quality
Authors: Simone Vasta | Simone Figorilli | Luciano Ortenzi | Simona Violino | Corrado Costa | Lavinia Moscovini | Francesco Tocci | Federico Pallottino | Alberto Assirelli | Alessio Saviane | Silvia Cappellozza
Source: Sensors (article, 2023-01-12)
Field: Materials Science — Biomaterials

Automated Prototype for Bombyx mori Cocoon Sorting Attempts to Improve Silk Quality and Production Efficiency through Multi-Step Approach and Machine Learning Algorithms Cocoon sorting is one of the most labor-demanding activities required both at the end of the agricultural production and before the industrial reeling process to obtain an excellent silk quality. In view of the possible relaunch of European sericulture, the automatization of this production step is mandatory both to reduce silk costs and to standardize fiber quality. The described research starts from this criticality in silk production (the manual labor required to divide cocoons into different quality classes) to identify amelioration solutions. To this aim, the automation of this activity was proposed, and a first prototype was designed and built. This machinery is based on the use of three cameras and imaging algorithms identifying the shape and size of the cocoons and outside stains, a custom-made light sensor and an AI model to discard dead cocoons. The current efficiency of the machine is about 80 cocoons per minute. In general, the amelioration obtained through this research involves both the application of traditional sensors/techniques to an unusual product and the design of a dedicated sensor for the identification of dead/alive pupae inside the silk cocoons. A general picture of the overall efficiency of the new cocoon-sorting prototype is also outlined. SILK ; Sorting ; Automation ; Quality (philosophy) ; Sericulture ; Production (economics) ; Production efficiency ; Computer science ; sort ; Artificial intelligence ; Manufacturing engineering ; Algorithm ; Engineering ; Engineering drawing ; Bombyx mori ; Mechanical engineering ; Telecommunications Silk-based biomaterials and applications ; Silkworms and Sericulture Research ; Insect and Arachnid Ecology and Behavior
Authors: Wayne A. Hendrickson
Source: IUCrJ (article, 2023-08-10)
Field: Materials Science — Materials Chemistry

Facing the phase problem The marvel of X-ray crystallography is the beauty and precision of the atomic structures deduced from diffraction patterns. Since these patterns record only amplitudes, phases for the diffracted waves must also be evaluated for systematic structure determination. Thus, we have the phase problem as a central complication, both intellectually for the field and practically so for many analyses. Here, I discuss how we - myself, my laboratory and the diffraction community - have faced the phase problem, considering the evolution of methods for phase evaluation as structural biology developed to the present day. During the explosive growth of macromolecular crystallography, practice in diffraction analysis evolved from a universal reliance on isomorphous replacement to the eventual domination of anomalous diffraction for de novo structure determination. As the Protein Data Bank (PDB) grew and familial relationships among proteins became clear, molecular replacement overtook all other phasing methods; however, experimental phasing remained essential for molecules without obvious precedents, with multi- and single-wavelength anomalous diffraction (MAD and SAD) predominating. While the mathematics-based direct methods had proved to be inadequate for typical macromolecules, they returned to crack substantial selenium substructures in SAD analyses of selenomethionyl proteins. Native SAD, exploiting the intrinsic S and P atoms of biomolecules, has become routine. Selenomethionyl SAD and MAD were the mainstays of structural genomics efforts to populate the PDB with novel proteins. A recent dividend has been paid in the success of PDB-trained artificial intelligence approaches for protein structure prediction. Currently, molecular replacement with AlphaFold models often obviates the need for experimental phase evaluation. For multiple reasons, we are now unfazed by the phase problem. Cryo-EM analysis is an attractive alternative to crystallography for many applications faced by today's structural biologists. It simply finesses the phase problem; however, the principles and procedures of diffraction analysis remain pertinent and are adopted in single-particle cryo-EM studies of biomolecules. Diffraction ; Protein Data Bank ; Phase problem ; Protein Data Bank (RCSB PDB) ; Phaser ; Molecular replacement ; Phase (matter) ; Crystallography ; Structural genomics ; Structural biology ; Computer science ; Chemistry ; Protein structure ; Physics ; Crystal structure ; Optics ; Biochemistry ; Quantum mechanics Enzyme Structure and Function ; Biochemical and Molecular Research ; Protein Structure and Dynamics
Authors: He Wang | K. P. Lim | Weijia Kong | Huanhuan Gao | Bertrand Jern Han Wong | Ser Xian Phua | Tiannan Guo | Wilson Wen Bin Goh
Source: Scientific Data (article, 2023-12-02)
Field: Chemistry — Spectroscopy

MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes. Each dataset contains a balanced two-class design based on well-characterized and widely studied cell lines (A549 vs K562 or HCC1806 vs HS578T) with 48 or 36 biological and technical replicates altogether, allowing for investigation of a multitude of technical issues. These datasets allow for investigation of inter-connections between class and batch factors, or to develop approaches to compare and integrate data from DDA and DIA platforms. Computer science ; Proteomics ; Batch processing ; Benchmarking ; Resource (disambiguation) ; Proteome ; Data mining ; Data science ; Bioinformatics ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Single-cell and spatial transcriptomics
Authors: Liyan Ming | Irene Zabala Gutiérrez | Paloma Rodríguez‐Sevilla | Jorge Rubio‐Retama | Daniel Jaque | Riccardo Marin | Erving Ximendes
Source: Advanced Materials (article, 2023-10-03)
Field: Materials Science — Materials Chemistry

Neural Networks Push the Limits of Luminescence Lifetime Nanosensing Luminescence lifetime-based sensing is ideally suited to monitor biological systems due to its minimal invasiveness and remote working principle. Yet, its applicability is limited in conditions of low signal-to-noise ratio (SNR) induced by, e.g., short exposure times and presence of opaque tissues. Herein this limitation is overcome by applying a U-shaped convolutional neural network (U-NET) to improve luminescence lifetime estimation under conditions of extremely low SNR. Specifically, the prowess of the U-NET is showcased in the context of luminescence lifetime thermometry, achieving more precise thermal readouts using Ag<sub>2</sub> S nanothermometers. Compared to traditional analysis methods of decay curve fitting and integration, the U-NET can extract average lifetimes more precisely and consistently regardless of the SNR value. The improvement achieved in the sensing performance using the U-NET is demonstrated with two experiments characterized by extreme measurement conditions: thermal monitoring of free-falling droplets, and monitoring of thermal transients in suspended droplets through an opaque medium. These results broaden the applicability of luminescence lifetime-based sensing in fields including in vivo experimentation and microfluidics, while, hopefully, spurring further research on the implementation of machine learning (ML) in luminescence sensing. Luminescence ; Materials science ; Context (archaeology) ; Opacity ; Thermal ; Noise (video) ; Convolutional neural network ; SIGNAL (programming language) ; Optoelectronics ; Signal-to-noise ratio (imaging) ; Nanotechnology ; Computer science ; Optics ; Artificial intelligence ; Telecommunications ; Physics Machine Learning in Materials Science ; Optical properties and cooling technologies in crystalline materials ; Thermal properties of materials
Authors: Guojin Qin | Zhenwei Zhang | Xiangqin Hou | Hongfang Lü | Yunfei Huang | Yihuan Wang
Source: Tunnelling and Underground Space Technology (article, 2023-09-09)
Field: Materials Science — Materials Chemistry

Condition assessment of underground corroded pipelines subject to hydrogen damage and combined internal pressure and axial compression Internal pressure ; Hydrogen ; Pipeline transport ; Materials science ; Compression (physics) ; Corrosion ; Compressive strength ; Stress (linguistics) ; Structural engineering ; Composite material ; Cylinder stress ; Ultimate tensile strength ; Chemistry ; Engineering ; Mechanical engineering Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Structural Integrity and Reliability Analysis
Authors: Enrico Pedretti | Paolo Restuccia | Maria Clelia Righi
Source: Computer Physics Communications (article, 2023-06-19)
Field: Materials Science — Materials Chemistry

Xsorb: A software for identifying the most stable adsorption configuration and energy of a molecule on a crystal surface Molecular adsorption is the first important step of many surface-mediated chemical processes, from catalysis to lubrication. This phenomenon is controlled by physical/chemical interactions, which can be accurately described by first-principles calculations. Several computational tools have been developed to study molecular adsorption based on high throughput/automatized approaches in recent years. However, these tools can sometimes be over-sophisticated for non-expert users. Here we present Xsorb, a Python-based program for identifying the accurate adsorption energy and geometry of complex molecules on crystalline (reconstructed) surfaces. The program automatically samples the potential energy surface (PES) that describes the molecule-surface interaction by generating several adsorption configurations through symmetry operations. The set of the most representative ones is automatically identified through a fast pre-optimization scheme. Finally, the PES global minimum is identified through a full structural optimization process. We show the program capabilities through an example consisting of a hydrocarbon molecule, 1-hexene, adsorbed over the (110) surface of iron and the reconstructed (001) surface of diamond. This program, despite its conceptual simplicity, is very effective in reducing the computational workload usually associated with the creation and optimization of several adsorption configurations. Program title: Xsorb CPC Library link to program files: https://doi.org/10.17632/kv97tgybx8.1 Developer's repository link: https://gitlab.com/triboteam/xsorbed/ Licensing provisions: CC by 4.0 Programming language: Python (version 3.7 and above) and Quantum ESPRESSO (for the ab initio calculations). Nature of problem: Identifying the most stable adsorption configuration of a molecule over a substrate and compute its adsorption energy. Solution method: Creating a Python-based code that generates many adsorption configurations with different molecular orientations, performs a preliminary partial geometrical optimization with density functional theory calculations of all these configurations, identifies the most relevant ones for the full geometrical optimization and computes the adsorption energy. Python (programming language) ; Adsorption ; Molecule ; Density functional theory ; Computer science ; Computational science ; Software ; Materials science ; Chemical physics ; Nanotechnology ; Computational chemistry ; Chemistry ; Physical chemistry ; Programming language ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Surface Chemistry and Catalysis
Authors: Junyun Deng | Wenbo Wang | Zhansheng Ning | P. Venugopal | Jelena Popović | Gert Rietveld
Source: IEEE Transactions on Power Electronics (article, 2023-11-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-Frequency Core Loss Modeling Based on Knowledge-Aware Artificial Neural Network High-frequency core loss modeling plays a critical role in the magnetics design of power electronics. However, existing modeling tools fail to achieve both high speed and high precision. The conventional analytical approach enables fast estimations but performs poorly in accuracy. Magnetic loss models aided by loss maps feature high accuracy, but their model parameterization relies on large data. The emerging approach of artificial neural networks (ANNs) provides a promising alternative since it can achieve high speed and accuracy. However, conventional implementations of ANN require a large and accurate dataset for training, which is hard to achieve in magnetic loss modeling. To solve this problem, a knowledge-aware artificial neural network (KANN) is proposed that can achieve high accuracy with small training datasets. After introducing the principle of the proposed KANN, it is applied to high-frequency core loss modeling using the improved generalized Steinmetz equation as additional knowledge. To validate the performance of the proposed KANN-based design method for core loss modeling, it is applied to predict the losses of two ferrite cores in the frequency range of 50–450 kHz. The results show that the proposed method greatly outperforms present loss modeling approaches in accuracy and speed, requiring only a limited training dataset. An automatic loss modeling tool based on the new method is provided together with its open-source code. Computer science ; Artificial neural network ; Backpropagation ; Feature (linguistics) ; Artificial intelligence ; Electronic engineering ; Engineering Magnetic Properties and Applications ; Induction Heating and Inverter Technology ; Electric Motor Design and Analysis
Authors: Wen Sha | Kang Hu | Shizhuang Weng
Source: Foods (article, 2023-04-10)
Field: Chemistry — Analytical Chemistry

Statistic and Network Features of RGB and Hyperspectral Imaging for Determination of Black Root Mold Infection in Apples Apples damaged by black root mold (BRM) lose moisture, vitamins, and minerals as well as carry dangerous toxins. Determination of the infection degree can allow for customized use of apples, reduce financial losses, and ensure food safety. In this study, red-green-blue (RGB) imaging and hyperspectral imaging (HSI) are combined to detect the infection degree of BRM in apple fruits. First, RGB and HSI images of healthy, mildly, moderately, and severely infected fruits are measured, and those with effective wavelengths (EWs) are screened from HSI by random frog. Second, the statistic and network features of images are extracted by using color moment and convolutional neural network. Meanwhile, random forest (RF), K-nearest neighbor, and support vector machine are used to construct classification models with the above two features of RGB and HSI images of EWs. Optimal results with the 100% accuracy of training set and 96% accuracy of prediction set are obtained by RF with the statistic and network features of the two images, outperforming the other cases. The proposed method furnishes an accurate and effective solution for determining the BRM infection degree in apples. RGB color model ; Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Random forest ; Convolutional neural network ; Computer science ; Set (abstract data type) ; Statistic ; Support vector machine ; Mathematics ; Computer vision ; Statistics Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Smart Agriculture and AI
Authors: Karen Brigitte Mejía-Correal | V. Marcelo | Enoc Sanz‐Ablanedo | José Ramón Rodríguez Pérez
Source: Agronomy (article, 2023-08-29)
Field: Chemistry — Analytical Chemistry

Total Soluble Solids in Grape Must Estimation Using VIS-NIR-SWIR Reflectance Measured in Fresh Berries Total soluble solids (TSS) is a key variable taken into account in determining optimal grape maturity for harvest. In this work, partial least square (PLS) regression models were developed to estimate TSS content for Godello, Verdejo (white), Mencía, and Tempranillo (red) grape varieties based on diffuse spectroscopy measurements. To identify the most suitable spectral range for TSS prediction, the regression models were calibrated for four datasets that included the following spectral ranges: 400–700 nm (visible), 701–1000 nm (near infrared), 1001–2500 nm (short wave infrared) and 400–2500 nm (the entire spectral range). We also tested the standard normal variate transformation technique. Leave-one-out cross-validation was implemented to evaluate the regression models, using the root mean square error (RMSE), coefficient of determination (R2), ratio of performance to deviation (RPD), and the number of factors (F) as evaluation metrics. The regression models for the red varieties were generally more accurate than the models of those for the white varieties. The best regression model was obtained for Mencía (red): R2 = 0.72, RMSE = 0.55 °Brix, RPD = 1.87, and factors n = 7. For white grapes, the best result was achieved for Godello: R2 = 0.75, RMSE = 0.98 °Brix, RPD = 1.97, and factors n = 7. The methodology used and the results obtained show that it is possible to estimate TSS content in grapes using diffuse spectroscopy and regression models that use reflectance values as predictor variables. Spectroscopy is a non-invasive and efficient technique for determining optimal grape maturity for harvest. Mean squared error ; Coefficient of determination ; Linear regression ; Partial least squares regression ; Regression analysis ; Standard error ; Mathematics ; Regression ; Near-infrared spectroscopy ; Brix ; Statistics ; Chemistry ; Food science ; Optics Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Fermentation and Sensory Analysis