jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinglong Wang | Kangjie Xu | Lingling Ma | Ruoxi Sun | Kun Wang | Wang Rui-yan | Junli Zhang | Wenwen Tao | Kai Linghu | Shuyao Yu | Jingwen Zhou
Source: Briefings in Bioinformatics (article, 2024-11-14)
Field: Materials Science — Biomaterials

Diffusion model assisted designing self-assembling collagen mimetic peptides as biocompatible materials Collagen self-assembly supports its mechanical function, but controlling collagen mimetic peptides (CMPs) to self-assemble into higher-order oligomers with numerous functions remains challenging due to the vast potential amino acid sequence space. Herein, we developed a diffusion model to learn features from different types of human collagens and generate CMPs; obtaining 66% of synthetic CMPs could self-assemble into triple helices. Triple-helical and untwisting states were probed by melting temperature (Tm); hence, we developed a model to predict collagen Tm, achieving a state-of-art Pearson's correlation (PC) of 0.95 by cross-validation and a PC of 0.8 for predicting Tm values of synthetic CMPs. Our chemically synthesized short CMPs and recombinantly expressed long CMPs could self-assemble, with the lowest requirement for hydrogel formation at a concentration of 0.08% (w/v). Five CMPs could promote osteoblast differentiation. Our results demonstrated the potential for using computer-aided methods to design functional self-assembling CMPs. Triple helix ; Biocompatible material ; Diffusion ; Sequence (biology) ; Function (biology) ; Materials science ; Chemistry ; Biophysics ; Nanotechnology ; Biomedical engineering ; Biochemistry ; Stereochemistry ; Physics ; Cell biology ; Biology Collagen: Extraction and Characterization ; Orthodontics and Dentofacial Orthopedics ; Protein Hydrolysis and Bioactive Peptides
Authors: James Dark | Rémi Delaporte-Mathurin | T. Schwarz‐Selinger | E.A. Hodille | Jonathan Mougenot | Y. Charles | C. Grisolia
Source: Nuclear Fusion (article, 2024-06-11)
Field: Materials Science — Materials Chemistry

Modelling neutron damage effects on tritium transport in tungsten Abstract A damage-induced hydrogen trap creation model is proposed, and parameters for tungsten are identified using experimental data. The methodology for obtaining these parameters using thermo-desorption analysis spectra data is outlined. Self-damaged and optionally annealed tungsten samples have undergone TDS analysis, which has been analysed to identify the properties of extrinsic traps induced by the damage and to determine how they evolve with damage and annealing temperature. A parametric study investigated the impact of the damage rate and temperature on tritium inventories in tungsten. Tritium transport simulations have been performed with FESTIM considering a 1D model of a 2 mm sample of tungsten with damage rates and temperatures varying from 0– <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:mrow> </mml:math> dpa/fpy and 600–1300 K, respectively. The results show that after 24 h simultaneous exposure to neutron damage at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:mrow> </mml:math> dpa/FPY and tritium implantation at 700 K, tritium inventories can increase by up to four orders of magnitude when damaged at a rate of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:mrow> </mml:math> dpa/FPY compared to undamaged, defect-free tungsten, increasing further to five orders of magnitude after one full power year. The time taken to reach retention saturation shows the need for kinetic models of trapping properties on time scales relevant to reactor operation. The trap-creation model parameterisation procedure can be used to investigate neutron damage effects on other fusion-relevant materials such as EUROFER, Inconel and more. Materials science ; Tungsten ; Tritium ; Analytical Chemistry (journal) ; Algorithm ; Physics ; Chemistry ; Computer science ; Nuclear physics ; Metallurgy Fusion materials and technologies ; Nuclear Materials and Properties ; Nuclear materials and radiation effects
Authors: Petr Lapčík | Klara Synkova | Lucia Janáčová | Pavla Bouchalová | David Potěšil | Rudolf Nenutil | Pavel Bouchal
Source: Scientific Data (article, 2024-07-18)
Field: Chemistry — Spectroscopy

A hybrid DDA/DIA-PASEF based assay library for a deep proteotyping of triple-negative breast cancer Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and deeper proteome coverage is needed for its molecular characterization. We present comprehensive library of targeted mass spectrometry assays specific for TNBC and demonstrate its applicability. Proteins were extracted from 105 TNBC tissues and digested. Aliquots were pooled, fractionated using hydrophilic chromatography and analyzed by LC-MS/MS in data-dependent acquisition (DDA) parallel accumulation-serial fragmentation (PASEF) mode on timsTOF Pro LC-MS system. 16 individual lysates were analyzed in data-independent acquisition (DIA)-PASEF mode. Hybrid library was generated in Spectronaut software and covers 244,464 precursors, 168,006 peptides and 11,564 protein groups (FDR = 1%). Application of our library for pilot quantitative analysis of 16 tissues increased identification numbers in Spectronaut 18.5 and DIA-NN 1.8.1 software compared to library-free setting, with Spectronaut achieving the best results represented by 190,310 precursors, 140,566 peptides, and 10,463 protein groups. In conclusion, we introduce assay library that offers the deepest coverage of TNBC proteome to date. The TNBC library is available via PRIDE repository (PXD047793). Triple-negative breast cancer ; Breast cancer ; Proteome ; Computational biology ; Chemistry ; Mass spectrometry ; Computer science ; Cancer ; Biology ; Chromatography ; Biochemistry ; Genetics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Shudong Zhuang | Wen Yang | Yuxiang Zhou | Ying Zou | Chang Liu | Le Zhang | Miao Tong | Jinlong Ma
Source: Scientific Reports (article, 2024-10-31)
Field: Materials Science — Materials Chemistry

Temperature field analysis and compensation improvement of load cell During the operation of load cell, heat is generated by the strain gauge and the electronics on the PCB board, which leads to temperature gradients within the sensor itself. These temperature gradients are unstable at different ambient temperatures. Compensation inaccuracies can also occur when compensating for sensor measurements at different temperatures This paper proposes a method to change the position of temperature compensation resistors to address errors caused by the temperature field effect of the strain gauge sensor itself. Without affecting the sensor's strain measurement, the correctness of the proposed method is demonstrated through steady-state thermal simulation results in ANSYS and experimental results, effectively addressing errors caused by unstable temperature gradients during the operation of strain gauge sensors. Field (mathematics) ; Computer science ; Compensation (psychology) ; Mathematics ; Psychology High voltage insulation and dielectric phenomena ; Industrial Automation and Control Systems ; Elevator Systems and Control
Authors: R. Zhu | Lu Liu | Yuhang Wu | Huan Chen | Weimin Lou | Peilun Yang | Ming Kong
Source: Smart Materials and Structures (article, 2024-05-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling and parameter identification of rate-dependent hysteresis behavior based on modified-generalized Prandtl–Ishlinskii model Abstract The intrinsic characteristic of piezoelectric actuators (PEA), known as hysteresis, has been demonstrated to diminish the capability and stability of the system significantly. This paper proposes a modified-generalized Prandtl–Ishlinskii (MGPI) model to describe the rate-dependent hysteresis in piezoelectric actuators. The developed model incorporates a voltage change rate function to replace the first part of the generalized Prandtl–Ishlinskii (GPI) model. Additionally, the model integrates the cubic polynomial into the envelope function, along with the dynamic thresholds and weights. When describing the hysteresis of the piezoelectric actuator (PEA), the model parameters are identified using the Improved Grey Wolf Optimizer (IGWO) algorithm. To prevent the algorithm from getting trapped in local optima, the cubic chaotic mapping is utilized for population initialization, as well as a nonlinear convergence factor, and the Levy flight strategy factor is introduced to update the Wolf pack’s position. The rate-dependent hysteresis behavior of a PEA under excitation in the 1–200 Hz frequency range was experimentally measured. The measured data were used to demonstrate the validity of the proposed MGPI model. The relative root-mean-square error and the relative maximum error of the MGPI model are 1.41% and 6.00%, respectively, which are lower than those of the GPI model, which are 3.15% and 10.58%. Under the composite frequency driving, the outputs of the GPI model and MGPI model were compared with the measured data of the PEA, the results suggest that the MGPI model and the IGWO algorithm can more accurately describe the rate-dependent hysteresis of the piezoelectric actuators. Hysteresis ; Prandtl number ; Identification (biology) ; Control theory (sociology) ; Biological system ; Mathematics ; Statistical physics ; Applied mathematics ; Physics ; Computer science ; Mechanics ; Artificial intelligence ; Condensed matter physics ; Convection ; Control (management) Magnetic Properties and Applications ; Piezoelectric Actuators and Control ; Metallurgy and Material Forming
Authors: Y. Justin Raj | A. Bovas Herbert Bejaxhin | S. Rajkumar | L. Selvarajan | R. Premkumar
Source: International Journal of Engineering (article, 2024-10-21)
Field: Materials Science — Materials Chemistry

Enhancing Efficiency in Thermoelectric Removal Process: A Comprehensive Study on P/M-Made Al-Cu-ZrB2 Nano-Composite Electrodes on SS205 Alloy The thermoelectric energy between the material and the cutting electrode is essential to the EDM process. This research assesses the new Nano electrodes and input settings for SS205 electric discharge machining (EDM). In order to remove the product electro thermally, parameters such as current, pulse on, and pulse off are taken into account during sequential, independent discharges between the EDM tool and the specimen while using EDM oil as a dielectric fluid. Together with the SS205 electrode work piece, the electrode, which is made of 90% aluminium reinforced with 5% copper and 5% zirconium di-boride, is used for testing. The P/M process is used to create it. To optimize, we use the full-factorial central composite design based on response surface methodology (RSM) to create the set of experiments (20 runs). Analysis of variance (ANOVA) then confirms the suitability of the suggested models. ANOVA determines which process factors significantly influence performance attributes. Die-sinker EDM using an Al-Cu-ZrB2 composite electrode shows promise for lowering production expenses and wear. SEM is used to analyze surface imperfections both before and after machining. Parameters such as Ton of 50μs, T off of 80μs, and SV of 12 amps provide a wear rate of 0.0008 g/min, which is minimal. At 0.0107 g/min, high removal rates are achieved with Ton of 70μs, T off of 90μs, and a SV of 12 amps. The settings for better surface roughness are being optimized further, with the best SR obtained at 2.91μm. Lastly, in order to assess the effectiveness of the chosen desirability technique, the optimized outcomes are verified against the experimental findings. Thermoelectric effect ; Composite number ; Materials science ; Alloy ; Electrode ; Nano- ; Process (computing) ; Metallurgy ; Composite material ; Computer science ; Chemistry ; Physics Advanced Thermoelectric Materials and Devices ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: А. С. Кононихин | Natalia Starodubtseva | Alexander G. Brzhozovskiy | Alisa Tokareva | Д. Н. Каширина | Natalia V. Zakharova | Anna E. Bugrova | Maria I. Indeykina | Liudmila Kh. Pastushkova | И. М. Ларина | Vladimir A. Mitkevich | A. A. Makarov | Е. Н. Николаев
Source: Biomedicines (article, 2024-10-21)
Field: Chemistry — Spectroscopy

Absolute Quantitative Targeted Monitoring of Potential Plasma Protein Biomarkers: A Pilot Study on Healthy Individuals The results suggest a different contribution of specific (patho)physiological pathways (e.g., immune system and blood coagulation) to the development of socially significant diseases in Europeans and North Americans, and they should be taken into account when refining diagnostic panels. Medicine Advanced Proteomics Techniques and Applications ; Computational Drug Discovery Methods ; Advanced Biosensing Techniques and Applications
Authors: Sonali Bhardwaj | Mitchell Bulluss | Ana D’Aubeterre | Afshin Derakhshani | Regan Penner | MaryAnn Mahajan | Vinit B. Mahajan | Antoine Dufour
Source: Protein Science (article, 2024-03-27)
Field: Chemistry — Spectroscopy

Integrating the analysis of human biopsies using post‐translational modifications proteomics Proteome diversities and their biological functions are significantly amplified by post-translational modifications (PTMs) of proteins. Shotgun proteomics, which does not typically survey PTMs, provides an incomplete picture of the complexity of human biopsies in health and disease. Recent advances in mass spectrometry-based proteomic techniques that enrich and study PTMs are helping to uncover molecular detail from the cellular level to system-wide functions, including how the microbiome impacts human diseases. Protein heterogeneity and disease complexity are challenging factors that make it difficult to characterize and treat disease. The search for clinical biomarkers to characterize disease mechanisms and complexity related to patient diagnoses and treatment has proven challenging. Knowledge of PTMs is fundamentally lacking. Characterization of complex human samples that clarify the role of PTMs and the microbiome in human diseases will result in new discoveries. This review highlights the key role of proteomic techniques used to characterize unknown biological functions of PTMs derived from complex human biopsies. Through the integration of diverse methods used to profile PTMs, this review explores the genetic regulation of proteoforms, cells of origin expressing specific proteins, and several bioactive PTMs and their subsequent analyses by liquid chromatography and tandem mass spectrometry. Computational biology ; Proteomics ; Human proteome project ; Human disease ; Proteome ; Posttranslational modification ; Disease ; Biology ; Proteogenomics ; Microbiome ; Shotgun proteomics ; Bioinformatics ; Genome ; Genomics ; Medicine ; Genetics ; Biochemistry ; Gene ; Pathology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Jiezhong Shi | Yifei Wan | Haoyang Jia | Gregor Skeldon | Dirk Jan Cornelissen | Katrina Wesencraft | Junxi Wu | Gail McConnell | Quan Chen | Dongsheng Liu | Wenmiao Shu
Source: Angewandte Chemie International Edition (article, 2024-10-26)
Field: Materials Science — Surfaces, Coatings and Films

Printing Cell Embedded Sacrificial Strategy for Microvasculature using Degradable DNA Biolubricant Microvasculature is essential for the continued function of cells in tissue and is fundamental in the fields of tissue engineering, organ repair and drug screening. However, the fabrication of microvasculature is still challenging using existing strategies. Here, we developed a general PRINting Cell Embedded Sacrificial Strategy (PRINCESS) and successfully fabricated microvasculatures using degradable DNA biolubricant. This is the first demonstration of direct cell printing to fabricate microvasculature, which eliminates the need for a subsequent cell seeding process and the associated deficiencies. Utilizing the shear-thinning property of DNA hydrogels as a novel sacrificial, cell-laden biolubricant, we can print a 70 μm endothelialized microvasculature, breaking the limit of 100 μm. To our best knowledge, this is the smallest endothelialized microvasculature that has ever been bioprinted so far. In addition, the self-healing property of DNA hydrogels allows the creation of continuous branched structures. This strategy provides a new platform for constructing complex hierarchical vascular networks and offers new opportunity towards engineering thick tissues. The extremely low volume of sacrificial biolubricant paves the way for DNA hydrogels to be used in practical tissue engineering applications. The high-resolution bioprinting technique also exhibits great potential for printing lymphatics, retinas and neural networks in the future. DNA ; Chemistry ; Biochemical engineering ; Computer science ; Polymer science ; Biomedical engineering ; Nanotechnology ; Engineering ; Materials science ; Biochemistry Polymer Surface Interaction Studies ; Electrospun Nanofibers in Biomedical Applications ; Nanofabrication and Lithography Techniques
Authors: Emily Xi Tan | Jingxiang Tang | Yong Xiang Leong | In Yee Phang | Yih Hong Lee | Chi Seng Pun | Xing Yi Ling
Source: Angewandte Chemie (article, 2024-02-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Creating 3D Nanoparticle Structural Space via Data Augmentation to Bidirectionally Predict Nanoparticle Mixture's Purity, Size, and Shape from Extinction Spectra Abstract Nanoparticle (NP) characterization is essential because diverse shapes, sizes, and morphologies inevitably occur in as‐synthesized NP mixtures, profoundly impacting their properties and applications. Currently, the only technique to concurrently determine these structural parameters is electron microscopy, but it is time‐intensive and tedious. Here, we create a three‐dimensional (3D) NP structural space to concurrently determine the purity, size, and shape of 1000 sets of as‐synthesized Ag nanocubes mixtures containing interfering nanospheres and nanowires from their extinction spectra, attaining low predictive errors at 2.7–7.9 %. We first use plasmonically‐driven feature enrichment to extract localized surface plasmon resonance attributes from spectra and establish a lasso regressor (LR) model to predict purity, size, and shape. Leveraging the learned LR, we artificially generate 425,592 augmented extinction spectra to overcome data scarcity and create a comprehensive NP structural space to bidirectionally predict extinction spectra from structural parameters with &lt;4 % error. Our interpretable NP structural space further elucidates the two higher‐order combined electric dipole, quadrupole, and magnetic dipole as the critical structural parameter predictors. By incorporating other NP shapes and mixtures′ extinction spectra, we anticipate our approach, especially the data augmentation, can create a fully generalizable NP structural space to drive on‐demand, autonomous synthesis‐characterization platforms. Nanoparticle ; Materials science ; Nanotechnology ; Crystallography ; Chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Machine Learning in Materials Science
Authors: Alessandra Olarini | Marina Cocchi | Vincent Motto‐Ros | Ludovic Duponchel | Cyril Ruckebusch
Source: Chemometrics and Intelligent Laboratory Systems (article, 2024-07-09)
Field: Chemistry — Analytical Chemistry

Exploratory analysis of hyperspectral imaging data Characterizing sample composition and visualizing the distribution of its chemical compounds is a prominent topic in various research and applied fields. Integrating spatial and spectral information, hyperspectral imaging (HSI) plays a pivotal role in this pursuit. While self-modelling curve resolution techniques, like multivariate curve resolution - alternating least squares (MCR-ALS), and clustering methods, such as K-means, are widely used for HSI data analysis, their effectiveness in complex scenarios, where the structure of the data deviates from the models’ assumptions, deserves further investigation. The choice of a data analysis method is most often driven by research question at hand and prior knowledge of the sample. However, overlooking the structure of the investigated data, i.e. linearity, geometry, homogeneity, might lead to erroneous or biased results. Here, we propose an exploratory data analysis approach, based on the geometry of the data points cloud, to investigate the structure of HSI datasets and extract their main characteristics, providing insight into the results obtained by the above-mentioned methods. We employ the principle of essential information to extract archetype (most linearly dissimilar) spectra and archetype single-wavelength images. These spectra and images are then discussed and contrasted with MCR-ALS and K-means clustering results. Two datasets with varying characteristics and complexities were investigated: a powder mixture analyzed with Raman spectroscopy and a mineral sample analyzed with Laser Induced Breakdown Spectroscopy (LIBS). We show that the proposed approach enables to summarize the main characteristics of hyperspectral imaging data and provides a more accurate understanding of the results obtained by traditional data modelling methods, driving the choice of the most suitable one. Hyperspectral imaging ; Cluster analysis ; Exploratory data analysis ; Chemical imaging ; Computer science ; Principal component analysis ; Chemometrics ; Homogeneity (statistics) ; Sample (material) ; Pattern recognition (psychology) ; Data mining ; Artificial intelligence ; Multivariate statistics ; Imaging spectroscopy ; Machine learning ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification