jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aleksei Makogon | Frédéric Kanoufi | Viacheslav Shkirskiy
Source: ChemElectroChem (article, 2024-02-05)
Field: Materials Science — Materials Chemistry

Is Unsupervised Dimensionality Reduction Sufficient to Decode the Complexities of Electrochemical Impedance Spectra? Abstract As electrochemical research undergoes rapid technological progression, the acquisition of substantial amounts of electrochemical impedance spectra (EIS) becomes increasingly feasible. Yet, this advancement introduces intricate challenges in data processing, automation, and interpretation. This paper delves into the sufficiency of unsupervised machine learning (ML) and in particular dimensionality reduction methods in decoding EIS complexities, examining its strengths, limitations, and potential pathways for optimization. As we navigated the intricacies of non‐linear dimensionality reduction, spotlighting t‐distributed stochastic neighbor embedding (t‐SNE) and uniform manifold approximation and projection (UMAP) algorithms, a pattern emerged: these techniques excel at categorizing divergent impedance spectra but show limitations when faced with analogous circuit configurations, especially those substituting a capacitor with a constant phase element. This observation not only underscores a limitation but also accentuates that unsupervised ML approaches, alone, may not fully unravel the nuances of EIS spectra. In the concluding section of our manuscript, we discuss the implications of this finding from a practical standpoint, particularly for electrochemists seeking to apply these methods in their work. Dimensionality reduction ; Nonlinear dimensionality reduction ; Computer science ; Reduction (mathematics) ; Electrical impedance ; Unsupervised learning ; Decoding methods ; Artificial intelligence ; Curse of dimensionality ; Projection (relational algebra) ; Dielectric spectroscopy ; Pattern recognition (psychology) ; Machine learning ; Algorithm ; Chemistry ; Electrochemistry ; Mathematics ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Machine Learning and ELM ; Fuel Cells and Related Materials
Authors: Yunxi Dong | Bowen Zheng | Fan Yang | Hong Tang | Huan Zhao | Yi Huang | Tian Gu | Juejun Hu | Hualiang Zhang
Source: Advanced Optical Materials (article, 2024-12-25)
Field: Materials Science — Surfaces, Coatings and Films

Full‐Color, Wide Field‐of‐View Metalens Imaging via Deep Learning Abstract Chromatic aberration has been the main showstopper for metalenses when it comes to imaging applications with broadband sources such as ambient light. In wide field‐of‐view metalenses, this challenge becomes far more severe due to exacerbated lateral chromatic aberrations. In this paper, it is demonstrated, for the first time, full‐color wide field‐of‐view imaging using a fisheye metalens coupled with deep learning computational processing. This approach is capable of restoring panoramic images with enhanced signal‐to‐noise ratio while effectively correcting chromatic aberration, distortion, and vignetting. Furthermore, it is shown that the deep learning algorithm is robust against various lighting conditions and object distances, making it a versatile solution for practical imaging applications involving wide field‐of‐view metalenses. Vignetting ; Chromatic aberration ; Optics ; Distortion (music) ; Chromatic scale ; Artificial intelligence ; Materials science ; Deep learning ; Broadband ; SIGNAL (programming language) ; Light field ; Computer science ; Computer vision ; Lens (geology) ; Physics ; Optoelectronics Optical Coatings and Gratings ; Advanced optical system design ; Metamaterials and Metasurfaces Applications
Authors: Hang Xue | Xiping Xu | Yang Yang | Dongmei Hu | Guocheng Niu
Source: Preprints.org (preprint, 2024-01-29)
Field: Chemistry — Analytical Chemistry

Rapid and Non-Destructive of Moisture Content in Maize seeds Using Hyperspectral Imaging The moisture content of maize seed is an important indicator for evaluating seed quality and a fundamental item in grain testing. The experiment used direct drying method to measure the moisture content of 80 different varieties of maize samples, and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 1100~2498nm. By comparing seven spectral preprocessing methods, the PLSR model established after Normalize pretreatment had the best effect. The characteristic wavelengths were selected by Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS) and Uninformative Variable Elimination (UVE). Twelve prediction models were established for full band spectra and characteristic wavelengths based on PLSR, PCR and SVM. By comparing the performance of the prediction models, it was found the Normalize-SPA-PLSR algorithm was optimized. The values of RC2 and RP2 in the model were higher, which were 0.9936 and 0.9933, respectively, while the values of RMSEP and RMSECV were lower, which were 0.0357 and 0.0380, respectively. The Normalize-SPA-PLSR model was used as a visual prediction model for moisture content of maize seed, the 20 maize varieties in the prediction set were visualized to obtain visualized color images of moisture content. The color differences between different moisture content images were significant. The result indicated that hyperspectral imaging could accurately, rapidly, and non-destructive predict moisture content of maize seed, which provided technical support for moisture content detection in the process of maize harvest, storage and processing. Hyperspectral imaging ; Water content ; Content (measure theory) ; Environmental science ; Agronomy ; Agricultural engineering ; Mathematics ; Remote sensing ; Biology ; Geography ; Geology ; Engineering ; Geotechnical engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Grzegorz Ficak | A. Łukaszek-Sołek | Marek Hawryluk
Source: Materials (review, 2024-11-05)
Field: Materials Science — Materials Chemistry

Durability of Forging Tools Used in the Hot Closed Die Forging Process—A Review The article presents the classification of the wear mechanisms of forging tools. The durability of dies can be enhanced through a variety of methods, including the selection of appropriate hot working tool steel, the application of effective heat treatment, the utilization of advanced surface engineering techniques, and the incorporation of lubricating and cooling agents. Two popular methods of tool regeneration, such as re-profiling and laser regeneration, are presented. The issue of numerical wear prediction based on the Archard model, the correlation of this model with experimental results, low-cycle fatigue (HTLCF), and an alternative method based on artificial neural networks are discussed. The paper aims to present currently known wear mechanisms and the methods of increasing and predicting tool durability. Forging ; Durability ; Process (computing) ; Tool wear ; Mechanical engineering ; Manufacturing engineering ; Materials science ; Computer science ; Engineering ; Composite material ; Machining Metal Alloys Wear and Properties ; Metal and Thin Film Mechanics ; High Entropy Alloys Studies
Authors: Maria Stella Murfuni | Licia Elvira Prestagiacomo | Annarita Giuliano | Caterina Gabriele | Sara Signoretti | Giovanni Cuda | Marco Gaspari
Source: International Journal of Molecular Sciences (article, 2024-05-09)
Field: Chemistry — Spectroscopy

Evaluation of PAC and FASP Performance: DIA-Based Quantitative Proteomic Analysis The aim of this study was to compare filter-aided sample preparation (FASP) and protein aggregation capture (PAC) starting from a three-species protein mix (<i>Human</i>, <i>Soybean</i> and <i>Pisum sativum</i>) and two different starting amounts (1 and 10 µg). Peptide mixtures were analyzed by data-independent acquisition (DIA) and raw files were processed by three commonly used software: Spectronaut, MaxDIA and DIA-NN. Overall, the highest number of proteins (mean value of 5491) were identified by PAC (10 µg), while the lowest number (4855) was identified by FASP (1 µg). The latter experiment displayed the worst performance in terms of both specificity (0.73) and precision (0.24). Other tested conditions showed better diagnostic accuracy, with specificity values of 0.95-0.99 and precision values between 0.61 and 0.86. In order to provide guidance on the data analysis pipeline, the accuracy diagnostic of three software was investigated: (i) the highest sensitivity was obtained with Spectronaut (median of 0.67) highlighting the ability of Spectronaut to quantify low-abundance proteins, (ii) the best precision value was obtained by MaxDIA (median of 0.84), but with a reduced number of identifications compared to Spectronaut and DIA-NN data, and (iii) the specificity values were similar (between 0.93 and 0.99). The data are available on ProteomeXchange with the identifier PXD044349. Computational biology ; Data science ; Computer science ; Medicine ; Chemistry ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
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 | Matthew D. Wilkerson | Praveen Kumar | Christopher M. Tarney | Kathleen M. Darcy | Neil T. Phippen | G. Larry Maxwell | Thomas P. Conrads | Nicholas W. Bateman
Source: iScience (article, 2024-02-12)
Field: Chemistry — Spectroscopy

ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data 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 (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or Consensus<sup>TME</sup>. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/. Deconvolution ; Computational biology ; Computer science ; Chemistry ; Data science ; Nanotechnology ; Biology ; Materials science ; Algorithm Advanced Proteomics Techniques and Applications ; Ferroptosis and cancer prognosis ; Single-cell and spatial transcriptomics
Authors: Jonathan A. Jones
Source: Progress in Nuclear Magnetic Resonance Spectroscopy (preprint, 2024-02-09)
Field: Chemistry — Spectroscopy

Controlling NMR spin systems for quantum computation Nuclear magnetic resonance is arguably both the best available quantum technology for implementing simple quantum computing experiments and the worst technology for building large scale quantum computers that has ever been seriously put forward. After a few years of rapid growth, leading to an implementation of Shor's quantum factoring algorithm in a seven-spin system, the field started to reach its natural limits and further progress became challenging. Rather than pursuing more complex algorithms on larger systems, interest has now largely moved into developing techniques for the precise and efficient manipulation of spin states with the aim of developing methods that can be applied in other more scalable technologies and within conventional NMR. However, the user friendliness of NMR implementations means that they remain popular for proof-of-principle demonstrations of simple quantum information protocols. Computation ; Quantum computer ; Spin (aerodynamics) ; Quantum ; Computer science ; Physics ; Theoretical physics ; Quantum mechanics ; Algorithm Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications
Authors: Mei‐Huei Lin | Yan‐Ting Kuo | José Danglad‐Flores | Eric T. Sletten | Peter H. Seeberger
Source: Chemistry - A European Journal (article, 2024-03-28)
Field: Chemistry — Organic Chemistry

Parametric Analysis of Donor Activation for Glycosylation Reactions The chemical synthesis of complex oligosaccharides relies on efficient and highly reproducible glycosylation reactions. The outcome of a glycosylation is contingent upon several environmental factors, such as temperature, acidity, the presence of residual moisture, as well as the steric, electronic, and conformational aspects of the reactants. Each glycosylation proceeds rapidly and with a high yield within a rather narrow temperature range. For better control over glycosylations and to ensure fast and reliable reactions, a systematic analysis of 18 glycosyl donors revealed the effect of reagent concentration, water content, protecting groups, and structure of the glycosyl donors on the activation temperature. With these insights, we parametrize the first step of the glycosylation reaction to be executed reliably and efficiently. Glycosylation ; Chemistry ; Reagent ; Yield (engineering) ; Steric effects ; Glycosyl ; Glycosyl donor ; Combinatorial chemistry ; Organic chemistry ; Biochemistry ; Materials science Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Enzyme Production and Characterization
Authors: Guillermo Jiménez-Hernández | Fidel Ortega-Gavilán | M. Gracia Bagur-González | Antonio González‐Casado
Source: Foods (article, 2024-01-05)
Field: Chemistry — Analytical Chemistry

Discrimination/Classification of Edible Vegetable Oils from Raman Spatially Solved Fingerprints Obtained on Portable Instrumentation Currently, the combination of fingerprinting methodology and environmentally friendly and economical analytical instrumentation is becoming increasingly relevant in the food sector. In this study, a highly versatile portable analyser based on Spatially Offset Raman Spectroscopy (SORS) obtained fingerprints of edible vegetable oils (sunflower and olive oils), and the capability of such fingerprints (obtained quickly, reliably and without any sample treatment) to discriminate/classify the analysed samples was evaluated. After data treatment, not only unsupervised pattern recognition techniques (as HCA and PCA), but also supervised pattern recognition techniques (such as SVM, kNN and SIMCA), showed that the main effect on discrimination/classification was associated with those regions of the Raman fingerprint related to free fatty acid content, especially oleic and linoleic acid. These facts allowed the discernment of the original raw material used in the oil's production. In all the models established, reliable qualimetric parameters were obtained. Fingerprint (computing) ; Edible oil ; Artificial intelligence ; Pattern recognition (psychology) ; Chemometrics ; Sunflower oil ; Vegetable oil ; Olive oil ; Computer science ; Chemistry ; Food science ; Chromatography Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Xingmei Qi | Han Wang | Kezhen Wang | Yu Wang | Axel Leppert | Igor A. Iashchishyn | Xueying Zhong | Yizhong Zhou | Ruifang Liu | Anna Rising | Michael Landreh | Jan Johansson | Gefei Chen
Source: Advanced Functional Materials (article, 2024-02-20)
Field: Materials Science — Biomaterials

Spiders Use Structural Conversion of Globular Amyloidogenic Domains to Make Strong Silk Fibers Abstract Spider silk—an environmentally friendly protein‐based material—is widely recognized for its extraordinary mechanical properties. Biomimetic spider silk‐like fibers made from recombinant spider silk proteins (spidroins) currently falls short compared to natural silks in terms of mechanical performance. In this study, it is discovered that spiders use structural conversion of molecular enhancers—conserved globular 127‐residue spacer domains—to make strong silk fibers. This domain lacks poly‐Ala motifs but interestingly contains motifs that are similar to human amyloidogenic motifs, and that it self‐assembles into amyloid‐like fibrils through a non‐nucleation‐dependent pathway, likely to avoid the formation of cytotoxic intermediates. Incorporating this spacer domain into a recombinant chimeric spidroin facilitates self‐assembly into silk‐like fibers, increases fiber molecular homogeneity, and markedly enhances fiber mechanical strength. These findings highlight that spiders employ diverse strategies to produce silk with exceptional mechanical properties. The spacer domain offers a way to enhance the properties of recombinant spider silk‐like fibers and other functional materials. Spider silk ; SILK ; Materials science ; Spider ; Fibril ; Biophysics ; Recombinant DNA ; Polymer science ; Nanotechnology ; Chemistry ; Biology ; Composite material ; Biochemistry Silk-based biomaterials and applications ; Biochemical and Structural Characterization ; Antimicrobial Peptides and Activities
Authors: Zongcheng Xu | Yujie Li | Bin Han | Yue Wang | Quan Yuan | Yanan Li | Weiyan He | Junhua Hao | Liang Wu | Jianquan Yao
Source: Materials (article, 2024-04-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

All-Silicon Polarization-Insensitive Metamaterial Absorber in the Terahertz Range All-silicon terahertz absorbers have attracted considerable interest. We present a design and numerical study of an all-silicon polarization-insensitive terahertz metamaterial absorber. The meta-atoms of the metamaterial absorber are square silicon rings which can be viewed as gratings. By properly optimizing the structure of the meta-atom, we achieve a broadband absorptivity that is above 90% ranging from 0.77 THz to 2.53 THz, with a relative bandwidth of 106.7%. Impedance matching reduces the reflection of the terahertz waves and the (0, ±1)-order diffraction induce the strong absorption. The absorption of this absorber is insensitive to the polarization of the terahertz wave and has a large incident angle tolerance of up to 60 degrees. The all-silicon metamaterial absorber proposed here provides an effective way to obtain broadband absorption in the terahertz regime. Metamaterial absorbers have outstanding applications in terahertz communication and imaging. Terahertz radiation ; Metamaterial absorber ; Metamaterial ; Materials science ; Optics ; Silicon ; Optoelectronics ; Molar absorptivity ; Broadband ; Polarization (electrochemistry) ; Absorption (acoustics) ; Tunable metamaterials ; Physics ; Chemistry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Gul Muneer | Sofani Tafesse Gebreyesus | Ciao‐Syuan Chen | T D Lee | Fengchao Yu | Cheng‐An J. Lin | Min‐Shu Hsieh | Alexey I. Nesvizhskii | Chao‐Chi Ho | Sung‐Liang Yu | Hsiung‐Lin Tu | Yu‐Ju Chen
Source: Advanced Science (article, 2024-10-14)
Field: Chemistry — Spectroscopy

Mapping Nanoscale‐To‐Single‐Cell Phosphoproteomic Landscape by Chip‐DIA Protein phosphorylation plays a crucial role in regulating disease phenotypes and serves as a key target for drug development. Mapping nanoscale-to-single-cell samples can unravel the heterogeneity of cellular signaling events. However, it remains a formidable analytical challenge due to the low detectability, abundance, and stoichiometry of phosphorylation sites. Here, we present a Chip-DIA strategy, integrating a microfluidic-based phosphoproteomic chip (iPhosChip) with data-independent acquisition mass spectrometry (DIA-MS) for ultrasensitive nanoscale-to-single-cell phosphoproteomic profiling. The iPhosChip operates as an all-in-one station that accommodates both quantifiable cell capture/imaging and the entire phosphoproteomic workflow in a highly streamlined and multiplexed manner. Coupled with a sample size-comparable library-based DIA-MS strategy, Chip-DIA achieved ultra-high sensitivity, detecting 1076±158 to 15869±1898 phosphopeptides from 10±0 to 1013±4 cells, and revealed the first single-cell phosphoproteomic landscape comprising druggable sites and basal phosphorylation-mediated networks in lung cancer. Notably, the sensitivity and coverage enabled the illumination of heterogeneous cytoskeleton remodeling and cytokeratin signatures in patient-derived cells resistant to third-generation EGFR therapy, stratifying mixed-lineage adenocarcinoma-squamous cell carcinoma subtypes, and identifying alternative targeted therapy for late-stage patients. With flexibility in module design and functionalization, Chip-DIA can be adapted to other PTM-omics to explore dysregulated PTM landscapes, thereby guiding therapeutic strategies toward precision oncology. Phosphoproteomics ; Computational biology ; Biology ; Phosphorylation ; Nanotechnology ; Protein phosphorylation ; Cell biology ; Materials science ; Protein kinase A Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Fan Lin | Dengjie Chen | Cheng Liu | Jincheng He
Source: Applied Sciences (article, 2024-03-06)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics This study pioneered a non-destructive testing approach to evaluating the physicochemical properties of golden passion fruit by developing a platform to analyze the fruit’s electrical characteristics. By using dielectric properties, the method accurately predicted the soluble solids content (SSC), Acidity and pulp percentage (PP) in passion fruit. The investigation entailed measuring the relative dielectric constant (ε′) and dielectric loss factor (ε″) for 192 samples across a spectrum of 34 frequencies from 0.05 to 100 kHz. The analysis revealed that with increasing frequency and fruit maturity, both ε′ and ε″ showed a declining trend. Moreover, there was a discernible correlation between the fruit’s physicochemical indicators and dielectric properties. In refining the dataset, 12 outliers were removed using the Local Outlier Factor (LOF) algorithm. The study employed various advanced feature extraction techniques, including Recursive Feature Elimination with Cross-Validation (RFECV), Permutation Importance based on Random Forest Regression (PI-RF), Permutation Importance based on Linear Regression (PI-LR) and Genetic Algorithm (GA). All the variables and the selected variables after screening were used as inputs to build Extreme Gradient Boosting (XGBoost) and Categorical Boosting (Cat-Boost) models to predict the SSC, Acidity and PP in passion fruit. The results indicate that the PI-RF-XGBoost model demonstrated superior performance in predicting both the SSC (R2 = 0.9240, RMSE = 0.2595) and the PP (R2 = 0.9092, RMSE = 0.0014) of passion fruit. Meanwhile, the GA-CatBoost model exhibited the best performance in predicting Acidity (R2 = 0.9471, RMSE = 0.1237). In addition, for the well-performing algorithms, the selected features are mainly concentrated within the frequency range of 0.05–6 kHz, which is consistent with the frequency range highly correlated with the dielectric properties and quality indicators. It is feasible to predict the quality indicators of fruit by detecting their low-frequency dielectric properties. This research offers significant insights and a valuable reference for non-destructive testing methods in assessing the quality of golden passion fruit. Passion fruit ; Passion ; Materials science ; Quality (philosophy) ; Psychology ; Horticulture ; Philosophy ; Social psychology ; Biology ; Epistemology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis