jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vinita Chauhan Kushwah | Ritika Chauhan | Ram Kumar Dhaked
Source: Journal of Modern Biology and Drug Discovery (article, 2024-05-28)
Field: Chemistry — Organic Chemistry

Fluorescence Thermal Shift Assay based High Through-put Screening in Small Molecule Drug Discovery: A Brief Overview Background: The Early drug discovery process was majorly phenotypic, lacking target specificity, and mechanism of action causing short and/or long-term toxicological impacts on the patients leading to several drug withdrawals from the market. Biochemical procedures and methods used in drug discovery and development make it lengthy in terms of time requiring approx. 15 years with the investment of billions of dollars for a new drug molecule. Technological advancement leading to the identification of protein structures through crystallography paved a new path toward targeted drug discovery. Objective: This paper provides a brief overview of the use of the fluorescence thermal shift assay (FTSA) as a high throughput (HTS) screening technique in small molecule drug discovery. Methods: The article discusses the principles of thermal denaturation techniques and how they are applied in drug discovery. It highlights the advantages of FTSA over other biophysical methods, including its label-free, cost-effective, and high HTS nature. The review also presents a comparative analysis of FTSA with techniques such as isothermal titration calorimetry (ITC), mass spectrometry (MS), nuclear magnetic resonance (NMR), surface plasmon resonance (SPR), X-ray crystallography(X-RD), circular dichroism (CD), and differential scanning calorimetry (DSC). Results: The FTSA technique is described as a powerful tool that addresses the limitations of larger protein requirements, specific assay development, and low HTS associated with other thermal denaturation methods. Unlike biochemical and cellular assays that measure secondary effects, FTSA directly estimates the binding of small molecules to target proteins. Conclusion: The review concludes that FTSA is the only biophysical method that satisfies the three key prerequisites of being label-free, cost-effective, and suitable for high HTS screening in small molecule drug discovery. Drug discovery ; Fluorescence ; Small molecule ; Drug ; Chemistry ; Computational biology ; Medicine ; Pharmacology ; Biology ; Physics ; Biochemistry ; Optics Click Chemistry and Applications ; Computational Drug Discovery Methods ; Advanced Biosensing Techniques and Applications
Authors: William Bang Lomholdt | Matthew Helmi Leth Larsen | Cuauhtémoc Núñez Valencia | Jakob Schiøtz | Thomas W. Hansen
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Measuring the Interpretability of High-Resolution Transmission Electron Microscopy Images ConclusionDetermining the SNR of an image can tell us something about the interpretability and if it makes statistical sense to continue a more thorough analysis.Here we have attempted alternatives to the traditional method of determining SNR which may not be well-suited to estimate the interpretability of HRTEM images.We have correlated this with data analysis based on convolutional neural networks. Interpretability ; Transmission electron microscopy ; High-resolution transmission electron microscopy ; Electron tomography ; Resolution (logic) ; Electron microscope ; Microscopy ; Scanning transmission electron microscopy ; Materials science ; Artificial intelligence ; Optics ; Computer science ; Computer vision ; Physics ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: V.S. Skvortsov | A.I. Voronina | A.V. Rybina
Source: Biomedical Chemistry Research and Methods (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Alignment and Normalization of Mass Spectrometry Data Using the Hydrophobicity Index This paper presents a program for the alignment of data from mass spectrometry experiments by retention time on a chromatographic column. The program uses the experimentally obtained data set as a reference against which the alignment procedure is performed. The primary advantage of this approach consists in its capacity to align data sets that had significant variations in both peptide composition and substance amount, such as individual fractions derived from multivariate separation. To illustrate this, two datasets were employed. The first dataset contains data obtained after multivariate separation, while the second dataset exhibited comparable peptide composition across all samples. The second dataset was used to assess the efficacy of the alignment program in normalizing signal intensity between individual samples. The results were compared with the normalization results obtained by the Progenesis LC-MS program. The normalization multipliers obtained for 22 of the 24 samples exhibited good correlation with those calculated by the Progenesis LC-MS (R² = 0.68). The program is freely available at http://lpcit.ibmc.msk.ru/AlignRT. Normalization (sociology) ; Mass spectrometry ; Index (typography) ; Chemistry ; Chromatography ; Analytical Chemistry (journal) ; Materials science ; Computer science ; World Wide Web Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Steven R. Spurgeon | Waqwoya Abebe | J. Strube | Christina Doty | Derek Hopkins | K Fiedler | Matthew J. Olszta | Nathan R. Tallent
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Describing Atomic Order Through Foundational Microscopy Microscopy ; Order (exchange) ; Atomic force microscopy ; Materials science ; Nanotechnology ; Physics ; Optics ; Economics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Mona Knoblich | Mohammad Al Ktash | Frank Wackenhut | Tim Englert | Jan Stiedl | H. Wittel | Simon Green | Timo Jacob | Barbara Boldrini | Edwin Ostertag | Karsten Rebner | Marc Brecht
Source: Sensors (article, 2024-07-19)
Field: Chemistry — Analytical Chemistry

Rapid Detection of Cleanliness on Direct Bonded Copper Substrate by Using UV Hyperspectral Imaging In the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a promising in-line method for rapid, contactless, and large-scale detection of contamination; thus, UV hyperspectral imaging (225-400 nm) was utilized to characterize the cleanliness of direct bonded copper in a non-destructive way. In total, 11 levels of cleanliness were prepared, and a total of 44 samples were measured to develop multivariate models for characterizing and predicting the cleanliness levels. The setup included a pushbroom imager, a deuterium lamp, and a conveyor belt for laterally resolved measurements of copper surfaces. A principal component analysis (PCA) model effectively differentiated among the sample types based on the first two principal components with approximately 100.0% explained variance. A partial least squares regression (PLS-R) model to determine the optimal sonication time showed reliable performance, with <i>R</i><sup>2</sup><sub>cv</sub> = 0.928 and RMSECV = 0.849. This model was able to predict the cleanliness of each pixel in a testing sample set, exemplifying a step in the manufacturing process of direct bonded copper substrates. Combined with multivariate data modeling, the in-line UV prototype system demonstrates a significant potential for further advancement towards its application in real-world, large-scale processes. Hyperspectral imaging ; Principal component analysis ; Partial least squares regression ; Multivariate statistics ; Pixel ; Copper ; Sample (material) ; Chemometrics ; Biological system ; Materials science ; Computer science ; Artificial intelligence ; Chemistry ; Chromatography ; Metallurgy ; Machine learning Spectroscopy and Chemometric Analyses ; Thermography and Photoacoustic Techniques ; Industrial Vision Systems and Defect Detection
Authors: Noah Daniëls | Floris Geerts
Source: arXiv (Cornell University) (preprint, 2024-10-10)
Field: Materials Science — Polymers and Plastics

A note on the VC dimension of 1-dimensional GNNs Graph Neural Networks (GNNs) have become an essential tool for analyzing graph-structured data, leveraging their ability to capture complex relational information. While the expressivity of GNNs, particularly their equivalence to the Weisfeiler-Leman (1-WL) isomorphism test, has been well-documented, understanding their generalization capabilities remains critical. This paper focuses on the generalization of GNNs by investigating their Vapnik-Chervonenkis (VC) dimension. We extend previous results to demonstrate that 1-dimensional GNNs with a single parameter have an infinite VC dimension for unbounded graphs. Furthermore, we show that this also holds for GNNs using analytic non-polynomial activation functions, including the 1-dimensional GNNs that were recently shown to be as expressive as the 1-WL test. These results suggest inherent limitations in the generalization ability of even the most simple GNNs, when viewed from the VC dimension perspective. Dimension (graph theory) ; Mathematical economics ; Computer science ; Mathematics ; Combinatorics Synthesis and properties of polymers ; MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: S. Ragoni | Janet Elizabeth Seger | C. Anson | David Tlustý
Source: arXiv (Cornell University) (preprint, 2024-10-09)
Field: Materials Science — Materials Chemistry

Machine learning opportunities for online and offline tagging of photo-induced and diffractive events in continuous readout experiments The increasing data rates in modern high-energy physics experiments such as ALICE at the LHC and the upcoming ePIC experiment at the Electron-Ion Collider (EIC) present significant challenges in real-time event selection and data storage. This paper explores the novel application of machine learning techniques, to enhance the identification of rare low-multiplicity events, such as ultraperipheral collisions (UPCs) and central exclusive diffractive processes. We focus on utilising machine learning models to perform early event classification, even before full event reconstruction, in continuous readout systems. We estimate data rates and disk space requirements for photoproduction and central exclusive diffractive processes in both ALICE and ePIC. We show that machine learning techniques can not only optimize data selection but also significantly reduce storage requirements in continuous readout environments, providing a scalable solution for the upcoming era of high-luminosity particle physics experiments. Computer science ; Online learning ; Online and offline ; Artificial intelligence ; World Wide Web ; Operating system Machine Learning in Materials Science ; Cell Image Analysis Techniques
Authors: Janet Zhong | Heming Wang | Alexander N. Poddubny | Shanhui Fan
Source: arXiv (Cornell University) (preprint, 2024-12-13)
Field: Materials Science — Materials Chemistry

Topological nature of edge states for one-dimensional systems without symmetry protection We numerically verify and analytically prove a winding number invariant that correctly predicts the number of edge states in one-dimensional, nearest-neighbor (between unit cells), two-band models with any complex couplings and open boundaries. Our winding number uses analytical continuation of the wave-vector into the complex plane and involves two special points on the full Riemann surface band structure that correspond to bulk eigenvector degeneracies. Our winding number is invariant under unitary or similarity transforms. We emphasize that the topological criteria we propose here differ from what is traditionally defined as a topological or trivial phase in symmetry-protected classification studies. It is a broader invariant for our model that supports nonzero energy edge states and its transition does not coincide with the gap closing condition. When the relevant symmetries are applied, our invariant reduces to well-known Hermitian and non-Hermitian symmetry-protected topological invariants. Symmetry (geometry) ; Topology (electrical circuits) ; Theoretical physics ; Enhanced Data Rates for GSM Evolution ; Physics ; Symmetry protected topological order ; Computer science ; Mathematics ; Topological order ; Quantum mechanics ; Geometry ; Combinatorics ; Telecommunications ; Quantum Quasicrystal Structures and Properties ; Quantum chaos and dynamical systems
Authors: Marina Romele | Anwar Zabirov | Rafał Chatys | Władimir Szestakow
Source: Composites Theory And Practice (article, 2024-06-30)
Field: Materials Science — General Materials Science

Solving technological problems of flight services at airport involving composite materials Composite number ; Aeronautics ; Aerospace engineering ; Computer science ; Engineering Material Properties and Applications ; Engineering Technology and Methodologies ; Industrial Engineering and Technologies
Authors: Gautam Saxena | Qin Fu | Aleksandra Binek | Jennifer E. Van Eyk
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-12-17)
Field: Chemistry — Spectroscopy

Quantifying the ∼75-95% of Peptides in DIA-MS Datasets that were not Previously Quantified Abstract We demonstrate an algorithm termed GoldenHaystack (GH) that, compared to the leading DIA-MS algorithm, (a) quantifies and identifies with better FDR accuracy the peptides found in FASTA search spaces (∼5-25% of analytes in DIA-MS datasets), (b) quantifies the remaining ∼75-95% of analytes that were previously unquantified, and (c) runs ∼40-200x faster (or ∼1-10x faster than the LC-MS). Specifically, without a FASTA or spectral library, GH can deconvolute and accurately quantify chimeric LC-MS spectra. The central idea that enables this claim is: for sufficiently sized projects (e.g., ≥ ∼50 LC-MS files), pairs of peptides that co-elute in one subset of LC-MS files do not exactly co-elute in a different subset of files. GH thus analyzes a project holistically: it uses multi -partite matching to match fragment ions across all samples, separates and regroups the fragment ions into unique analyte signatures, reduces stochastic noise, and then quantifies those unique analyte signatures. Computational biology ; Computer science ; Biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Stefanuto, Pierre-Hugues | Gaida, Meriem | Cain, C | Synovec, R | Focant, Jean-François
Source: Open Repository and Bibliography (University of Liège) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Bringing machine learning into tile-based processing for complex GC×GC-TOFMS datasets peer reviewed Feature (linguistics) ; Feature selection ; Data processing ; Support vector machine ; Training set Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Mandar T. Naik
Source: African Journal of Biomedical Research (article, 2024-12-12)
Field: Chemistry — Spectroscopy

Development And Validation Of A Novel Reverse-Phase HPLC Method For Impurity Profiling In Advanced Anti-Diabetic Drugs: Amycretin And Teplizumab The study focuses on developing and validating a novel RP-HPLC method tailored for impurity profiling in Amycretin and Teplizumab, two advanced anti-diabetic drugs. Addressing challenges posed by complex impurity profiles and molecular structures, the method achieved high sensitivity with LOD and LOQ values of 0.02% and 0.05%, respectively, and precise separation of impurities under varied conditions. Validation adhered to ICH guidelines, confirming accuracy (98.5–101.2%), precision (RSD &lt; 2%), and robustness. Stress studies revealed distinct degradation pathways; for instance, light exposure resulted in minimal impurity formation (0.12%) for Amycretin, while heat and basic conditions produced higher impurity levels (0.15%) for Teplizumab. Batch testing across 10 samples demonstrated consistent compliance with regulatory limits (≤ 0.20%). Optimized gradient profiles, along with column selection, ensured baseline resolution with retention times for APIs and impurities observed at 6.2 and 7.8 minutes for Amycretin, and 9.4 and 10.3 minutes for Teplizumab. The method's efficacy in identifying impurities and degradation products underscores its utility in routine quality control and regulatory compliance. This validated approach offers a robust tool for impurity profiling in pharmaceutical analysis, setting a benchmark for future studies aimed at enhancing drug safety and efficacy. Impurity ; Profiling (computer programming) ; Chromatography ; Chemistry ; Computer science Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Protein Interaction Studies and Fluorescence Analysis
Authors: Jennifer Wenzel | A. Herr | A. Winderling | Matthias R. Schmidt | Artis Kons | Arianna Lanza | Xia Jiang | Anders S. Larsen | Anders Ø. Madsen | Toms Rekis
Source: Acta Crystallographica Section A Foundations and Advances (article, 2024-08-26)
Field: Materials Science — Materials Chemistry

Testing <i>PhAI</i> for solving crystal structures from electron diffraction and powder X-ray diffraction data Powder diffraction ; X-ray ; Diffraction ; Crystallography ; Materials science ; X-ray crystallography ; Electron backscatter diffraction ; Electron ; Electron diffraction ; Optics ; Physics ; Chemistry ; Nuclear physics X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies ; Powder Metallurgy Techniques and Materials
Authors: Jianwei Li | S. Yu | M. J. Guo | X. Y. Shen | Qi Ouyang | Fangting Li
Source: arXiv (Cornell University) (preprint, 2024-12-02)
Field: Chemistry — Spectroscopy

The Thermodynamic Model to Study the Slow Afterhyperpolarization in a Single Neuron at Different ATP Levels The neuron consumes energy from ATP hydrolysis to maintain a far-from-equilibrium steady state inside the cell, thus all physiological functions inside the cell are modulated by thermodynamics. The neurons that manage information encoding, transferring, and processing with high energy consumption, displaying a phenomenon called slow afterhyperpolarization after burst firing, whose properties are affected by the energy conditions. Here we constructed a thermodynamical model to quantitatively describe the sAHP process generated by $Na^+-K^+$ ATPases(NKA) and the Calcium-activated potassium(K(Ca)) channels. The model simulates how the amplitude of sAHP is effected by the intracellular ATP concentration and ATP hydrolysis free energy $Δ$ G. The results show a trade-off between NKA and the K(Ca)'s modulation on the sAHP's energy dependence, and also predict an alteration of sAHP's behavior under insufficient ATP supply if the proportion of NKA and K(Ca)'s expression quantities is changed. The research provides insights in understanding the maintenance of neural homeostasis and support furthur researches on metabolism-related and neurodegenerative diseases. Afterhyperpolarization ; Neuron ; Biological neuron model ; Neuroscience ; Psychology ; Electrophysiology Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications ; Spectroscopy and Quantum Chemical Studies
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Phys. Chem. Chem. Phys., 2024, 26, 4771 DOI: 10.1039/D4CP90029D This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Chemistry Chemical and Physical Properties of Materials ; Advanced Physical and Chemical Molecular Interactions ; Machine Learning in Materials Science
Authors: Gaida, Meriem | Caitlin N Cain, | Robert Synovec, | Focant, Jean-François | Stefanuto, Pierre-Hugues
Source: Open Repository and Bibliography (University of Liège) (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Bringing machine learning into tile-based processing for complex GC×GC-TOFMS datasets peer reviewed Feature (linguistics) ; Feature selection ; Data processing ; Support vector machine ; Training set Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Wuryanto Hadinugroho | Yudy Tjahjono | Kuncoro Foe | Senny Yesery Esar | Caroline Caroline | Maria Annabella Jessica | Hendy Wijaya
Source: Current Research in Pharmacology and Drug Discovery (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Characterization of 2-((4-(chloromethyl)benzoyl)oxy)benzoate acid for analgesic tablet dosage form formulation The 2-((4-(chloromethyl)benzoyl)oxy)benzoic acid (4CH<sub>2</sub>Cl) is a potential analgesic compound derived from salicylic acid and 4-chloromethyl benzoyl chloride. Characterization required 4CH<sub>2</sub>Cl for the formulation of tablet dosage forms. This study aims investigate the effect of SSG, PVP-K30, and the combination of SSG*PVP K-30 on the formulation of 4CH<sub>2</sub>Cl tablets. Additionally, this study aimed to obtain the optimum 4CH<sub>2</sub>Cl tablet composition. The experiment followed the two-factor simplex lattice design and direct compression method. The analgesic activity of 4CH<sub>2</sub>Cl in the optimal tablet was investigated using the hot-plate methods. The ANOVA of linear models is acceptable and the polynomial coefficients of quadratic models are similar to those of linear models. The coefficient of the linear model shows that SSG and PVP K-30 increase the Carr index (16.26; 20.61), Hausner ratio (1.19; 1.29), hardness (4.19; 9.39), friability (0.48; 0.67), disintegration time (0.34; 7.50), and drug release (85.29; 97.69). The coefficient of the quadratic model shows that SSG*PVP K-30 increased the Carr index (1.90), Hausner ratio (0.04), hardness (1.88), friability (0.06), and drug release (4.56), and decreased disintegration time (-0.30). SSG and PVP K-30 increased Carr index, Hausner ratio, hardness, friability, disintegration time, and drug release. The combination of SSG*PVP K-30 has the same effect, except that the disintegration time decreased. The optimum tablet formula is 4CH<sub>2</sub>Cl (300 mg), Ne (75 mg), SSG (33.60 mg), PVP K-30 (22.40 mg), MCC (40 mg), and SDL (up to 800 mg). 4CH<sub>2</sub>Cl tablets can be a candidate and choice for new analgesic drugs in the future. Analgesic ; Chemistry ; Benzoic acid ; Benzoates ; Organic chemistry ; Pharmacology ; Medicine Analytical Methods in Pharmaceuticals ; Drug Solubulity and Delivery Systems ; Analytical Chemistry and Chromatography
Authors: A. D. Asmedianova | A. S. Bagishev | O. A. Logutenkо | A. I. Titkov
Source: Электрохимия (article, 2024-10-04)
Field: Materials Science — Materials Chemistry

Fabrication of NiO-Ce&lt;sub&gt;0.8&lt;/sub&gt;Gd&lt;sub&gt;0.2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt;-based anode for a solid oxide fuel cell using inkjet 3D printing and study of its microstructure A paste composition for inkjet 3D printing based on the NiO-Ce0.8Gd0.2O2 composite was developed and an anode billet for a solid oxide fuel cell of planar geometry was made using direct inkjet 3D printing. Effect of the printing mode and thermal annealing on the morphology and structure of the samples has been studied. The anode billet was reduced and the resulting sample was characterized by a number of physicochemical methods. Non-blocking I/O ; Anode ; Fabrication ; Materials science ; Chemistry ; Physical chemistry ; Electrode ; Medicine Advancements in Solid Oxide Fuel Cells ; Catalytic Processes in Materials Science ; Advanced Memory and Neural Computing