jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Seth W. Croslow | Timothy J. Trinklein | Jonathan V. Sweedler
Source: FEBS Letters (review, 2024-01-19)
Field: Chemistry — Spectroscopy

Advances in multimodal mass spectrometry for single‐cell analysis and imaging enhancement Multimodal mass spectrometry (MMS) incorporates an imaging modality with probe-based mass spectrometry (MS) to enable precise, targeted data acquisition and provide additional biological and chemical data not available by MS alone. Two categories of MMS are covered; in the first, an imaging modality guides the MS probe to target individual cells and to reduce acquisition time by automatically defining regions of interest. In the second category, imaging and MS data are coupled in the data analysis pipeline to increase the effective spatial resolution using a higher resolution imaging method, correct for tissue deformation, and incorporate fine morphological features in an MS imaging dataset. Recent methodological and computational developments are covered along with their application to single-cell and imaging analyses. Mass spectrometry imaging ; Modality (human–computer interaction) ; Computer science ; Pipeline (software) ; Mass spectrometry ; Data acquisition ; Molecular imaging ; Artificial intelligence ; Chemistry ; Biology ; Chromatography Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Advanced Proteomics Techniques and Applications
Authors: Nico C. Röttcher | Gun Deniz Akkoc | S.M. Finger | Birk Fritsch | Jonas Möller | Karl J. J. Mayrhofer | Dominik Dworschak
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Accelerating materials research with a comprehensive data management tool: a case study on an electrochemical laboratory Introduction of an SQL and Python-based tool for managing research data from acquisition to publication. The method enables FAIR-compatible data management, minimizes user interaction, and provides customizability for diverse research domains. Data management ; Electrochemistry ; Computer science ; Data science ; Systems engineering ; Engineering ; Chemistry ; Data mining Machine Learning in Materials Science ; Research Data Management Practices ; Electrochemical Analysis and Applications
Authors: Ivan Maliyov | Jia Yin | Jia Yao | Chao Yang | Marco Bernardi
Source: npj Computational Materials (article, 2024-06-10)
Field: Materials Science — Materials Chemistry

Dynamic mode decomposition of nonequilibrium electron-phonon dynamics: accelerating the first-principles real-time Boltzmann equation Abstract Nonequilibrium dynamics governed by electron–phonon ( e -ph) interactions plays a key role in electronic devices and spectroscopies and is central to understanding electronic excitations in materials. The real-time Boltzmann transport equation (rt-BTE) with collision processes computed from first principles can describe the coupled dynamics of electrons and atomic vibrations (phonons). Yet, a bottleneck of these simulations is the calculation of e –ph scattering integrals on dense momentum grids at each time step. Here we show a data-driven approach based on dynamic mode decomposition (DMD) that can accelerate the time propagation of the rt-BTE and identify dominant electronic processes. We apply this approach to two case studies, high-field charge transport and ultrafast excited electron relaxation. In both cases, simulating only a short time window of ~10% of the dynamics suffices to predict the dynamics from initial excitation to steady state using DMD extrapolation. Analysis of the momentum-space modes extracted from DMD sheds light on the microscopic mechanisms governing electron relaxation to a steady state or equilibrium. The combination of accuracy and efficiency makes our DMD-based method a valuable tool for investigating ultrafast dynamics in a wide range of materials. Boltzmann equation ; Physics ; Non-equilibrium thermodynamics ; Relaxation (psychology) ; Electron ; Phonon ; Excitation ; Thermalisation ; Computational physics ; Statistical physics ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Semiconductor materials and devices
Authors: Jieun Jeong | Sang‐Oh Kim
Source: Food Science and Biotechnology (article, 2024-11-11)
Field: Chemistry — Analytical Chemistry

Application of machine learning for quantitative analysis of industrial fermentation using image processing Fermentation ; Convolutional neural network ; Artificial intelligence ; Artificial neural network ; Computer science ; Biological system ; Food science ; Process engineering ; Environmental science ; Chemistry ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Tingting Zhang | Lu Long | Yihu Song | Minyu Yang | Jing Li | Jiduan Yuan | Yuquan Lin | Xingren Shi | Mingjie Li | Xiaotan Yuan | Zhongyi Zhang | Rensen Zeng | Yuanyuan Song | Li Gu
Source: Frontiers in Plant Science (article, 2024-01-15)
Field: Chemistry — Analytical Chemistry

Non-destructive identification of Pseudostellaria heterophylla from different geographical origins by Vis/NIR and SWIR hyperspectral imaging techniques The composition of <i>Pseudostellaria heterophylla</i> (Tai-Zi-Shen, TZS) is greatly influenced by the growing area of the plants, making it significant to distinguish the origins of TZS. However, traditional methods for TZS origin identification are time-consuming, laborious, and destructive. To address this, two or three TZS accessions were selected from four different regions of China, with each of these resources including distinct quality grades of TZS samples. The visible near-infrared (Vis/NIR) and short-wave infrared (SWIR) hyperspectral information from these samples were then collected. Fast and high-precision methods to identify the origins of TZS were developed by combining various preprocessing algorithms, feature band extraction algorithms (CARS and SPA), traditional two-stage machine learning classifiers (PLS-DA, SVM, and RF), and an end-to-end deep learning classifier (DCNN). Specifically, SWIR hyperspectral information outperformed Vis/NIR hyperspectral information in detecting geographic origins of TZS. The SPA algorithm proved particularly effective in extracting SWIR information that was highly correlated with the origins of TZS. The corresponding FD-SPA-SVM model reduced the number of bands by 77.2% and improved the model accuracy from 97.6% to 98.1% compared to the full-band FD-SVM model. Overall, two sets of fast and high-precision models, SWIR-FD-SPA-SVM and SWIR-FD-DCNN, were established, achieving accuracies of 98.1% and 98.7% respectively. This work provides a potentially efficient alternative for rapidly detecting the origins of TZS during actual production. Hyperspectral imaging ; Preprocessor ; Pattern recognition (psychology) ; Computer science ; Support vector machine ; Artificial intelligence ; Classifier (UML) ; Multispectral image ; Remote sensing Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Phytochemicals and Antioxidant Activities
Authors: Na Young Kim | Dukanya Dukanya | Gautam Sethi | Swamy Savvemala Girimanchanaika | Ji-Rui Yang | Omantheswara Nagaraja | Ananda Swamynayaka | Divakar Vishwanath | Keerthikumara Venkantesha | Shreeja Basappa | Arunachalam Chinnathambi | Sulaiman Ali Alharbi | M. Mahendra | Alexey Yu. Sukhorukov | Vijay Pandey | Peter E. Lobie | Basappa Basappa | Kwang Seok Ahn
Source: Translational Oncology (article, 2024-08-18)
Field: Chemistry — Organic Chemistry

Oxazine drug-seed induces paraptosis and apoptosis through reactive oxygen species/JNK pathway in human breast cancer cells Small molecule-driven JNK activation has been found to induce apoptosis and paraptosis in cancer cells. Herein pharmacological effects of synthetic oxazine (4aS, 7aS)-3-((4-(4‑chloro-2-fluorophenyl)piperazin-1-yl)methyl)-4-phenyl-4, 4a, 5, 6, 7, 7a-hexahydrocyclopenta[e] [1,2]oxazine (FPPO; BSO-07) on JNK-driven apoptosis and paraptosis has been demonstrated in human breast cancer (BC) MDA-MB231 and MCF-7 cells respectively. BSO-07 imparted significant cytotoxicity in BC cells, induced activation of JNK, and increased intracellular reactive oxygen species (ROS) levels. It also enhanced the expression of apoptosis-associated proteins like PARP, Bax, and phosphorylated p53, while decreasing the levels of Bcl-2, Bcl-xL, and Survivin. Furthermore, the drug altered the expression of proteins linked to paraptosis, such as ATF4 and CHOP. Treatment with N-acetyl-cysteine (antioxidant) or SP600125 (JNK inhibitor) partly reversed the effects of BSO-07 on apoptosis and paraptosis. Advanced in silico bioinformatics, cheminformatics, density Fourier transform and molecular electrostatic potential analysis further demonstrated that BSO-07 induced apoptosis and paraptosis via the ROS/JNK pathway in human BC cells. Reactive oxygen species ; Apoptosis ; Chemistry ; Cytotoxicity ; Cancer cell ; Cancer research ; Pharmacology ; Cell biology ; Biochemistry ; Cancer ; Biology ; In vitro Synthesis and biological activity ; Quinazolinone synthesis and applications ; Bioactive Compounds and Antitumor Agents
Authors: Shuvodeep De | Shalini J. Rukmani | Xianhui Zhao | Caitlyn M. Clarkson | Frédéric Vautard | Samarthya Bhagia | Monojoy Goswami | Yuanqiang Zhang | Sana Fazal Elyas | Wei Zhao | Jeremy C. Smith | Arthur J. Ragauskas | Soydan Ozcan | Halil Tekinalp | Muqing Si | J. Huang | Ximin He
Source: Advanced Functional Materials (article, 2024-12-30)
Field: Materials Science — Biomaterials

Additive Manufacturing with Cellulose‐Based Composites: Materials, Modeling, and Applications Abstract Recent advances in large‐scale additive manufacturing (AM) with polymer‐based composites have enabled efficient production of high‐performance materials. Cellulose nanomaterials (CNMs) have emerged as bio‐based feedstocks due to their exceptional strength and sustainability. However, challenges such as hornification and poor dispersion in polymer matrices still limit large‐scale CNM–polymer composite manufacturing, requiring novel strategies. This review outlines an approach starting with atomic‐level simulations to link molecular composition to key parameters like bulk density, viscosity, and modulus. These simulations provide data for finite element analysis (FEA), which informs large‐scale experiments and reduces the need for extensive trials. The strategy explores how atomic interactions impact the morphology, adhesion, and mechanical properties of CNM‐based composites in AM processes. The review also discusses current developments in AM, along with predictions of mechanical and thermal properties for structural applications, packaging, flexible electronics, and hydrogel scaffolds. By integrating experimental findings with molecular dynamics (MD) simulations and finite element modeling (FEM), valuable insights for material design, process optimization, and performance enhancement in CNM‐based AM are provided to address ongoing challenges. Materials science ; Finite element method ; Composite material ; Polymer ; Cellulose ; Modulus ; Mechanical engineering ; Nanotechnology ; Structural engineering Advanced Cellulose Research Studies ; Additive Manufacturing and 3D Printing Technologies ; biodegradable polymer synthesis and properties
Authors: Zheng Chang | Shoujia Liu | Jiajun Wang | Yang Lu | Lingyu Ma | Lichao Jiao | Juan Guo | Yafang Yin | Tuo He
Source: Plant Methods (article, 2024-04-24)
Field: Chemistry — Organic Chemistry

Opening the black box: explainable deep-learning classification of wood microscopic image of endangered tree species The research results provide a computer-assisted tool for identifying endangered tree species in laboratory scenarios, which can be used to combat illegal logging and related trade and contribute to the implementation of CITES convention and the conservation of global biodiversity. Identification (biology) ; Endangered species ; Artificial intelligence ; Tree (set theory) ; Computer science ; Feature (linguistics) ; Species identification ; Pattern recognition (psychology) ; Biology ; Ecology ; Mathematics ; Habitat ; Evolutionary biology Wood and Agarwood Research ; Identification and Quantification in Food ; Microbial Metabolism and Applications
Authors: Xiuwei Yan | Sijia Liu | Songlei Wang | Jiarui Cui | Yongrui Wang | Yu Lv | Hui Li | Yingjie Feng | Ruiming Luo | Zhifeng Zhang | Lei Zhang
Source: Foods (article, 2024-01-28)
Field: Chemistry — Analytical Chemistry

Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp<sup>2</sup> value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp<sup>2</sup> of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp<sup>2</sup> of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment. Artificial intelligence ; Computer science ; Convolutional neural network ; Linoleic acid ; Feature (linguistics) ; Feature extraction ; Overfitting ; Pattern recognition (psychology) ; Naive Bayes classifier ; Hyperspectral imaging ; Population ; Artificial neural network ; Support vector machine ; Fatty acid ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Lukáš Klein | Jiří Dvorský | David Seidl | Lukáš Prokop
Source: Engineering Applications of Artificial Intelligence (article, 2024-03-16)
Field: Materials Science — Materials Chemistry

Novel lossy compression method of noisy time series data with anomalies: Application to partial discharge monitoring in overhead power lines In overhead power transmission lines, particularly in regions like natural parks where establishing a safe zone is difficult, the adoption of cross-linked polyethylene insulated covered conductors (CCs) helps prevent outages due to vegetation contact. However, these CCs are susceptible to partial discharge (PD) activity, which can degrade insulation and lead to system failures. Detecting and analyzing PD are essential for maintaining power system reliability and safety. A key challenge in PD monitoring is transmitting the large volumes of PD signal data over unreliable 2G networks, as existing compression methods either compromise on data integrity or are ineffective. This paper introduces a novel lossy compression technique utilizing an autoencoder with skip connections and correction data to address this issue. Unlike previous algorithms that struggle with noisy time series data and fail to preserve crucial anomaly information, our method reconstructs the signal without anomalies, which are subsequently restored using correction data. Achieving a compression factor of about 25 (reducing data to 4.1% of its original size), this approach maintains essential PD signal features for analysis. The effectiveness of our method is validated by three classification algorithms, showing promise for future fault detection, diagnosis, and memory space reduction. This innovative compression solution marks a significant advancement in PD data processing, offering a balanced trade-off between compression efficiency and data fidelity, and paving the way for enhanced remote monitoring in power transmission systems. Computer science ; Lossy compression ; Overhead (engineering) ; Partial discharge ; Data compression ; Real-time computing ; Anomaly detection ; Transmission (telecommunications) ; Reliability (semiconductor) ; SIGNAL (programming language) ; Power (physics) ; Data mining ; Algorithm ; Telecommunications ; Artificial intelligence ; Electrical engineering ; Voltage High voltage insulation and dielectric phenomena ; Power Systems Fault Detection ; Power Transformer Diagnostics and Insulation
Authors: Peng Zhang | Qiang Wu | Yanhan Wang | Yun Huang | Min Xie | Fan Li
Source: Life (article, 2024-03-21)
Field: Chemistry — Analytical Chemistry

Rapid Detection of Tannin Content in Wine Grapes Using Hyperspectral Technology Wine grape quality is influenced by the variety and growing environment, and the quality of the grapes has a significant impact on the quality of the wine. Tannins are a crucial indicator of wine grape quality, and, therefore, rapid and non-destructive methods for detecting tannin content are necessary. This study collected spectral data of Pinot Noir and Chardonnay using a geophysical spectrometer, with a focus on the 500-1800 nm spectrum. The spectra were preprocessed using Savitzky-Golay (SG), first-order differential (1D), standard normal transform (SNV), and their respective combinations. Characteristic bands were extracted through correlation analysis (PCC). Models such as partial least squares (PLS), support vector machine (SVM), random forest (RF), and one-dimensional neural network (1DCNN) were used to model tannin content. The study found that preprocessing the raw spectra improved the models' predictive capacity. The SVM-RF model was the most effective in predicting grape tannin content, with a test set R<sup>2</sup> of 0.78, an RMSE of 0.31, and an RE of 10.71%. These results provide a theoretical basis for non-destructive testing of wine grape tannin content. Wine ; Tannin ; Hyperspectral imaging ; Partial least squares regression ; Mathematics ; Support vector machine ; Random forest ; Artificial intelligence ; Grape wine ; Pattern recognition (psychology) ; Food science ; Biological system ; Environmental science ; Computer science ; Chemistry ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Horticultural and Viticultural Research
Authors: Naeem Ur Rehman | Khalid Saif Ullah | Muhammad Sajid | Ihsanullah Ihsanullah | Abdul Waheed
Source: Advanced Sustainable Systems (review, 2024-03-13)
Field: Materials Science — Biomaterials

Preparation of Sustainable Composite Materials from Bio‐Based Domestic and Industrial Waste: Progress, Problems, and Prospects‐ A Review Abstract Bio‐based waste from households and industries is a big problem for the world, however, turning it into valuable composite materials can offer a promising approach to deal with it. It involves the conversion of waste from different bio‐based sources such as cellulose waste from farming and forestry leftovers, chitin waste from seafood and mushrooms, and keratin waste from hair, nails, and feathers into natural fibers. These fibers are then effectively mixed with other materials to create composite materials having unique properties, such as high strength and stiffness, good thermal and electrical conductivity, and better barrier properties. Developing these materials is not just good for the environment because it reduces landfill waste and the reliance on non‐renewable resources, but it can also make economic sense for producers. In this review, the basic compounds of natural fibers and the development of composite materials from them are explored and discussed in detail. Furthermore, their chemical and mechanical properties are discussed and summarized. In the final section, a brief overview of the challenges and the future research needed in this fast‐evolving field is given. Natural materials ; Waste management ; Composite number ; Materials science ; Engineering ; Composite material ; Polymer science Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties
Authors: Shuangyi Cai | Thomas Hu | Abhijeet Venkataraman | Felix G Rivera Moctezuma | Efe Ozturk | Nicholas Zhang | M. Wang | Tatenda Zvidzai | Sandip Das | A Balakrishna Pillai | Frank Schneider | Suresh S. Ramalingam | You-Take Oh | Shi‐Yong Sun | Ahmet F. Coskun
Source: Nature Biomedical Engineering (article, 2024-10-30)
Field: Chemistry — Organic Chemistry

Spatially resolved subcellular protein–protein interactomics in drug-perturbed lung-cancer cultures and tissues Subcellular localization ; Interactome ; Crosstalk ; Protein subcellular localization prediction ; Computational biology ; Biology ; Proximity ligation assay ; Cell biology ; Phenotype ; Cell ; Cancer research ; Receptor ; Biochemistry ; Gene ; Cytoplasm Click Chemistry and Applications ; Biotin and Related Studies ; Bioinformatics and Genomic Networks
Authors: Yi Wang | Shuran Song
Source: Frontiers in Plant Science (article, 2024-05-01)
Field: Chemistry — Analytical Chemistry

Detection of sweet corn seed viability based on hyperspectral imaging combined with firefly algorithm optimized deep learning The identification of sweet corn seed vitality is an essential criterion for selecting high-quality varieties. In this research, a combination of hyperspectral imaging technique and diverse deep learning algorithms has been utilized to identify different vitality grades of sweet corn seeds. First, the hyperspectral data of 496 seeds, including four viability-grade seeds, are extracted and preprocessed. Then, support vector machine (SVM) and extreme learning machine (ELM) are used to construct the classification models. Finally, the one-dimensional convolutional neural networks (1DCNN), one-dimensional long short-term memory (1DLSTM), the CNN combined with the LSTM (CNN-LSTM), and the proposed firefly algorithm (FA) optimized CNN-LSTM (FA-CNN-LSTM) are utilized to distinguish spectral images of sweet corn seeds viability grade. The findings from the experimental analysis indicate that the deep learning models exhibit a significant advantage over traditional machine learning approaches in the discrimination of seed vitality levels, boasting a classification accuracy exceeding 94.26% in test datasets and achieving an accuracy improvement of at least 3% compared to the best-performing machine learning model. Moreover, the performance of the FA-CNN-LSTM model proposed in this study demonstrated a slight superiority over the other three models. Besides, the FA-CNN-LSTM achieved a classification accuracy of 97.23%, representing a significant improvement of 2.97% compared to the lowest-performing CNN and a 1.49% enhancement over the CNN-LSTM. In summary, this study reveals the potential of integrating deep learning with hyperspectral imaging as a promising alternative for discriminating sweet corn seed vitality grade, showcasing its value in agricultural research and cultivar breeding. Hyperspectral imaging ; Artificial intelligence ; Support vector machine ; Convolutional neural network ; Deep learning ; Computer science ; Machine learning ; Pattern recognition (psychology) ; Algorithm Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture