jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenrong Chen | Zhengming Ding | Yong Zang | Xiaowen Liu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-04-06)
Field: Chemistry — Spectroscopy

Characterization of proteoform post-translational modifications by top-down and bottom-up mass spectrometry in conjunction with UniProt annotations Many proteoforms can be produced from a gene due to genetic mutations, alternative splicing, post-translational modifications (PTMs), and other variations. PTMs in proteoforms play critical roles in cell signaling, protein degradation, and other biological processes. Mass spectrometry (MS) is the primary technique for investigating PTMs in proteoforms, and two alternative MS approaches, top-down and bottom-up, have complementary strengths. The combination of the two approaches has the potential to increase the sensitivity and accuracy in PTM identification and characterization. In addition, protein and PTM knowledgebases, such as UniProt, provide valuable information for PTM characterization and validation. Here, we present a software pipeline called PTM-TBA (PTM characterization by Top-down, Bottom-up MS and Annotations) for identifying and localizing PTMs in proteoforms by integrating top-down and bottom-up MS as well as UniProt annotations. We identified 1,662 mass shifts from a top-down MS data set of SW480 cells, 545 (33%) of which were matched to 12 common PTMs, and 351 of which were localized. PTM-TBA validated 346 of the 1,662 mass shifts using UniProt annotations or a bottom-up MS data set of SW480 cells. UniProt ; Computational biology ; Pipeline (software) ; Computer science ; Mass spectrometry ; Proteome ; Alternative splicing ; Identification (biology) ; Chemistry ; Gene ; Biology ; Biochemistry ; Programming language ; Messenger RNA ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; RNA and protein synthesis mechanisms
Authors: Conner Ganjavi | Michael Eppler | Asli Pekcan | Brett Biedermann | Andre Luis Abreu | Gary S. Collins | Inderbir S. Gill | Giovanni Cacciamani
Source: arXiv (Cornell University) (preprint, 2023-07-21)
Field: Materials Science — Materials Chemistry

Bibliometric Analysis of Publisher and Journal Instructions to Authors on Generative-AI in Academic and Scientific Publishing We aim to determine the extent and content of guidance for authors regarding the use of generative-AI (GAI), Generative Pretrained models (GPTs) and Large Language Models (LLMs) powered tools among the top 100 academic publishers and journals in science. The websites of these publishers and journals were screened from between 19th and 20th May 2023. Among the largest 100 publishers, 17% provided guidance on the use of GAI, of which 12 (70.6%) were among the top 25 publishers. Among the top 100 journals, 70% have provided guidance on GAI. Of those with guidance, 94.1% of publishers and 95.7% of journals prohibited the inclusion of GAI as an author. Four journals (5.7%) explicitly prohibit the use of GAI in the generation of a manuscript, while 3 (17.6%) publishers and 15 (21.4%) journals indicated their guidance exclusively applies to the writing process. When disclosing the use of GAI, 42.8% of publishers and 44.3% of journals included specific disclosure criteria. There was variability in guidance of where to disclose the use of GAI, including in the methods, acknowledgments, cover letter, or a new section. There was also variability in how to access GAI guidance and the linking of journal and publisher instructions to authors. There is a lack of guidance by some top publishers and journals on the use of GAI by authors. Among those publishers and journals that provide guidance, there is substantial heterogeneity in the allowable uses of GAI and in how it should be disclosed, with this heterogeneity persisting among affiliated publishers and journals in some instances. The lack of standardization burdens authors and threatens to limit the effectiveness of these regulations. There is a need for standardized guidelines in order to protect the integrity of scientific output as GAI continues to grow in popularity. Publishing ; Library science ; Generative grammar ; Inclusion (mineral) ; Section (typography) ; Scientific publishing ; Computer science ; Political science ; Sociology ; Social science ; Law ; Artificial intelligence Machine Learning in Materials Science
Authors: Giuseppe Gallitto | Robert Englert | Bálint Kincses | Raviteja Kotikalapudi | Jialin Li | Kevin Hoffschlag | Ulrike Bingel | Tamás Spisák
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-12-04)
Field: Materials Science — Materials Chemistry

External validation of machine learning models - registered models and adaptive sample splitting Abstract Multivariate predictive models play a crucial role in enhancing our understanding of complex biological systems and in developing innovative, replicable tools for translational medical research. However, the complexity of machine learning methods and extensive data pre-processing and feature engineering pipelines can lead to overfitting and poor generalizability. An unbiased evaluation of predictive models necessitates external validation, which involves testing the finalized model on independent data. Despite its importance, external validation is often neglected in practice due to the associated costs. Here we propose that, for maximal credibility, model discovery and external validation should be separated by the public disclosure (e.g. pre-registration) of feature processing steps and model weights. Furthermore, we introduce a novel approach to optimize the trade-off between efforts spent on training and external validation in such studies. We show on data involving more than 3000 participants from four different datasets that, for any “sample size budget”, the proposed adaptive splitting approach can successfully identify the optimal time to stop model discovery so that predictive performance is maximized without risking a low powered, and thus inconclusive, external validation. The proposed design and splitting approach (implemented in the Python package “AdaptiveSplit”) may contribute to addressing issues of replicability, effect size inflation and generalizability in predictive modeling studies. Overfitting ; Generalizability theory ; Computer science ; Machine learning ; Credibility ; Cross-validation ; Artificial intelligence ; Data mining ; Python (programming language) ; Sample (material) ; Predictive modelling ; Sample size determination ; Artificial neural network ; Statistics ; Mathematics Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Meta-analysis and systematic reviews
Authors: Efim Burlutsky | D S Balzamov | Veronika Bronskaya | Olga Kharitonova | L E Khairullina | Olga Solovyeva
Source: International Journal of Technology (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

Influence of Temperature on the Thermal Properties of the Core Material - the Coefficient of Temperature Conductivity, Specific Heat Capacity, Thermal Conductivity The absorption of heat by rocks is accompanied by an increase in the kinetic energy of molecules and atoms and is recorded by a change in rock temperature. The thermal properties of rocks characterize the ability to transfer and absorb heat and ch Thermal conductivity ; Materials science ; Thermal effusivity ; Heat capacity ; Volumetric heat capacity ; Temperature coefficient ; Thermal conduction ; Thermal ; Core (optical fiber) ; Thermodynamics ; Thermal resistance ; Heat transfer coefficient ; Composite material ; Thermal contact conductance ; Heat transfer ; Physics Thermal properties of materials
Authors: Vyacheslav Romanov
Source: Engineering Reports (article, 2023-03-26)
Field: Materials Science — Materials Chemistry

Convoluted filtering for process cycle modeling Abstract Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process‐structure–property‐performance relationships hidden in materials data. The process‐structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process‐structure relations. In this work, a novel convoluted model‐filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe‐based alloy heat treatment cycles. The artifacts were pre‐trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets. Overfitting ; Interpretability ; Computer science ; Artificial intelligence ; Process (computing) ; Filter (signal processing) ; Representation (politics) ; Graph ; Work in process ; Machine learning ; Data mining ; Theoretical computer science ; Artificial neural network ; Engineering ; Computer vision Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Amy Z. Stetten | Felix Kratz | Nathalie Schilderink | Subhash Ayirala | Michael H.G. Duits | Jan Kierfeld | Frieder Mugele
Source: Langmuir (article, 2023-11-08)
Field: Chemistry — Analytical Chemistry

Elastometry of Complex Fluid Pendant Capsules Oil/water interfaces are ubiquitous in nature. Opposing polarities at these interfaces attract surface-active molecules, which can seed complex viscoelastic or even solid interfacial structure. Biorelevant proteins such as hydrophobin, polymers such as PNIPAM, and the asphaltenes in crude oil (CRO) are examples of some systems where such layers can occur. When a pendant drop of CRO is aged in brine, it can form an interfacial elastic membrane of asphaltenes so stiff that it wrinkles and crumples upon retraction. Most of the work studying CRO/brine interfaces focuses on the viscoelastic liquid regime, leaving a wide range of fully solidified, elastic interfaces largely unexplored. In this work, we quantitatively measure elasticity in all phases of drop retraction. In early retraction, the interface shows a fluid viscoelasticity measurable using a Gibbs isotherm or dilatational rheology. Further retraction causes a phase transition to a 2D elastic solid with nonisotropic, nonhomogeneous surface stresses. In this regime, we use new techniques in the elastic membrane theory to fit for the elasticities of these solid capsules. These elastic measurements can help us develop a deeper understanding not only of CRO interfaces but also of the myriad fluid systems with solid interfacial layers. Viscoelasticity ; Rheology ; Elasticity (physics) ; Complex fluid ; Surface forces apparatus ; Drop (telecommunication) ; Polymer ; Solid surface ; Membrane ; Materials science ; Elastic modulus ; Phase transition ; Surface tension ; Thermodynamics ; Chemical physics ; Chemistry ; Composite material ; Physics ; Mechanical engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Nan Wang | Guisheng Wang
Source: Scientia Forestalis (article, 2023-07-12)
Field: Chemistry — Organic Chemistry

Tree species classification using machine learning algorithms with OHS-2 hyperspectral image ABSTRACT Considering the form diversity of tree species composition in the Bagong Mountain National Forest Park of China, we mapped tree species utilizing Machine Learning Algorithms (support vector machines (SVM) and random forest (RF) classifiers) based on the OHS-2 hyperspectral satellite image by different datasets which combined spectral information and hyperspectral-derived vegetation indices (VIs) for improving tree species classification and explored the best performance of them. To verify the improvement, the results of physically-based spectral classifiers (spectral angle mapper (SAM) and maximum likelihood (ML) classifiers) were applied to compare with the results of machine learning algorithms. The results indicated an overall accuracy of 94.01%, 96.08%, 82.9% and 79.3% for SVM, RF, SAM and ML classifiers of the best performance using different datasets. Highest accuracies resulted from two machine learning algorithms classifiers; SVM and RF compared to SAM and ML classifiers. Although SVM outperformed RF when using all hyperspectral bands and VIs, the overall accuracy of the RF classifier is higher when compared to the SVM classifier using VIs combined selected features. Meanwhile, the RF classifier performed better than SVM after removing the redundancy of spectral data in training samples. Moreover, the machine learning algorithms successfully classified a small number of tree species (Cedrus deodara and Pterocarya stenoptera C. DC.) in the study area, but the physical spectroscopy-based method failed to classify these species. Such integration strategy improved the effectiveness of enhancing the accuracy of tree species classification and mapping their distribution on broad spatial and temporal scales using machine learning algorithms and hyperspectral imagery. Hyperspectral imaging ; Pattern recognition (psychology) ; Tree (set theory) ; Artificial intelligence ; Computer science ; Image (mathematics) ; Algorithm ; Machine learning ; Mathematics ; Combinatorics Wood and Agarwood Research ; Spectroscopy and Chemometric Analyses
Authors: Abeer Alshejari | Vassilis Kodogiannis | Stavros Leonidis
Source: Sensors (article, 2023-11-27)
Field: Chemistry — Analytical Chemistry

Combining Feature Selection Techniques and Neurofuzzy Systems for the Prediction of Total Viable Counts in Beef Fillets Using Multispectral Imaging In the food industry, quality and safety issues are associated with consumers' health condition. There is a growing interest in applying various noninvasive sensorial techniques to obtain quickly quality attributes. One of them, hyperspectral/multispectral imaging technique has been extensively used for inspection of various food products. In this paper, a stacking-based ensemble prediction system has been developed for the prediction of total viable counts of microorganisms in beef fillet samples, an essential cause to meat spoilage, utilizing multispectral imaging information. As the selection of important wavelengths from the multispectral imaging system is considered as an essential stage to the prediction scheme, a features fusion approach has been also explored, by combining wavelengths extracted from various feature selection techniques. Ensemble sub-components include two advanced clustering-based neuro-fuzzy network prediction models, one utilizing information from average reflectance values, while the other one from the standard deviation of the pixels' intensity per wavelength. The performances of neurofuzzy models were compared against established regression algorithms such as multilayer perceptron, support vector machines and partial least squares. Obtained results confirmed the validity of the proposed hypothesis to utilize a combination of feature selection methods with neurofuzzy models in order to assess the microbiological quality of meat products. Multispectral image ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Feature selection ; Hyperspectral imaging ; Partial least squares regression ; Support vector machine ; Artificial neural network ; Data mining ; Cluster analysis ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Hongtao Mao | Dong Nie | Xi Chen | Yan-an Cai | Jie Zhao | Xuzheng Zhang | Haoyu Yu | Wanli Ma | Zepeng Lv | Jun Zhou
Source: Frontiers in Energy Research (article, 2023-01-24)
Field: Materials Science — Materials Chemistry

Innovative technology for large-scale photovoltaic consumption using reversible solid oxide cells It is inevitable that renewable energy consumption will increase as installed capacity continues to increase, primarily wind and photovoltaic power generation. Power to Gas (P2G) technology can store electrical energy in the form of chemical energy on a large scale. Reversible solid oxide cell (RSOC) has a very high conversion efficiency in both electrolytic gas production and fuel cell power generation compared with traditional electricity-to-gas devices. For the future integrated energy system, Reversible solid oxide cells are expected to play a significant role in integrating power generation and energy storage. This work proposes a new integrated energy system based on Reversible solid oxide cell for photovoltaic (PV) consumption. The Integrated Electricity-Gas System (IEGS) considers the two modes of electrolysis and power generation of Reversible solid oxide cell in the model. The model takes the minimum running cost as the objective function to linearize part of the model to generate a mixed integer linearization problem and solve it in GAMS. The case study shows that wind power is maximized, and the gas mixture can be transported in natural gas pipelines, improving the economics and stability of Integrated Electricity-Gas System. This work not only can reduce the operating cost of the system but also increase the high penetration of photovoltaic power generation. A quantitative assessment of the impact of hydrogen injection ratio and renewable energy penetration was also carried out. Power to gas ; Photovoltaic system ; Renewable energy ; Electricity generation ; Process engineering ; Electricity ; Distributed generation ; Energy storage ; Environmental science ; Solid oxide fuel cell ; Materials science ; Electrical engineering ; Engineering ; Power (physics) ; Chemistry ; Electrolysis ; Anode ; Physics ; Thermodynamics ; Electrolyte Advancements in Solid Oxide Fuel Cells ; Integrated Energy Systems Optimization ; Hybrid Renewable Energy Systems
Authors: Jamel Salem | Roland Tóbiás | Attila G. Császár | Muneerah Mogren Al‐Mogren | Nejm‐Edine Jaidane | M. Hochlaf
Source: ChemPhysChem (article, 2023-11-02)
Field: Chemistry — Spectroscopy

Temperature‐Dependent Line‐Broadening Effects in CO<sub>2</sub> Caused by Ar This computational study of line-broadening effects is based on an accurate, analytical representation of the intermonomer potential energy surface (PES) of the CO<sub>2</sub> ⋅ Ar van der Waals (vdW) complex. The PES is employed to compute collisional broadening coefficients for rovibrational lines of CO<sub>2</sub> perturbed by Ar. The semiclassical computations are performed using the modified Robert-Bonamy approach, including real and imaginary terms, and the exact trajectory model. The lines investigated are in the 10001←00011, 01101←00001, 00011←00001, and 00031←00001 vibrational bands and the computations are repeated at multiple temperatures. The computed results are in good agreement with the available experimental values, validating both the intermonomer PES developed and the methodology used. For lines in the 01101←00001 band of CO<sub>2</sub> , temperature-dependent Ar-broadening coefficients are reported for the first time. The parameters presented should prove useful, among other applications, for the accurate experimental determination of CO<sub>2</sub> and Ar abundances in planetary atmospheres. Rotational–vibrational spectroscopy ; van der Waals force ; Line (geometry) ; Semiclassical physics ; Computation ; Physics ; Computational physics ; Microsecond ; Representation (politics) ; Spectral line ; Atomic physics ; Chemistry ; Molecular physics ; Quantum mechanics ; Molecule ; Mathematics ; Algorithm Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Atmospheric chemistry and aerosols
Authors: Josiah J. Green | C. I. Grimm | Andre Fristo | Joseph Byrum | Neil L. Kelleher
Source: Journal of Proteome Research (review, 2023-12-14)
Field: Chemistry — Spectroscopy

Parsing 20 Years of Public Data by AI Maps Trends in Proteomics and Forecasts Technology The trends of the last 20 years in biotechnology were revealed using artificial intelligence and natural language processing (NLP) of publicly available data. Implementing this "science-of-science" approach, we capture convergent trends in the field of proteomics in both technology development and application across the phylogenetic tree of life. With major gaps in our knowledge about protein composition, structure, and location over time, we report trends in persistent, popular approaches and emerging technologies across 94 ideas from a corpus of 29 journals in PubMed over two decades. New metrics for clusters of these ideas reveal the progression and popularity of emerging approaches like single-cell, spatial, compositional, and chemical proteomics designed to better capture protein-level chemistry and biology. This analysis of the proteomics literature with advanced analytic tools quantifies the Rate of Rise for a next generation of technologies to better define, quantify, and visualize the multiple dimensions of the proteome that will transform our ability to measure and understand proteins in the coming decade. Data science ; Popularity ; Proteomics ; Computer science ; Field (mathematics) ; Parsing ; Proteome ; Artificial intelligence ; Bioinformatics ; Biology ; Political science Advanced Proteomics Techniques and Applications ; Biomedical Text Mining and Ontologies ; Bioinformatics and Genomic Networks
Authors: Mohammad A. Gharaibeh | Faris M. AL‐Oqla
Source: Mathematical Modelling and Engineering Problems (article, 2023-02-28)
Field: Materials Science — Polymers and Plastics

Investigating and Predicting the Effects of Fiber Chemical Composition and Treatment on the Mechanical Properties of Natural Fiber Composites by Response Surface Method This paper aims to integrate the response surface methodologies to investigate the effect of the cellulose and hemicellulose contents on the mechanical properties of the polypropylene-based composites with green fiber reinforcement conditions.In this study, the tested data are collected from various literature resources demonstrating the green fiber type, the chemical treatment condition, and the resultant mechanical properties, i.e., the tensile modulus and tensile strength.Accordingly, the response surface analysis is utilized to obtain a high-accuracy first order regression model to formulate the influence of the cellulose, hemicellulose, and the treatment condition on the tensile characteristics of the green composite.The results showed that the biocomposites samples with higher cellulose and lower hemicellulose percentages have significantly better tensile modulus properties.However, such samples would have lower tensile strength qualities.Additionally, the presence of chemical treatment can significantly improve the tensile properties of the polypropylene-based composites. Composite material ; Materials science ; Fiber ; Natural fiber Natural Fiber Reinforced Composites ; Textile materials and evaluations