jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ludovic Berthier | Giulio Biroli | M. Lisa Manning | Francesco Zamponi
Source: arXiv (Cornell University) (preprint, 2024-01-17)
Field: Materials Science — Materials Chemistry

Yielding and plasticity in amorphous solids The physics of disordered media, from metallic glasses to colloidal suspensions, granular matter and biological tissues, offers difficult challenges because it often occurs far from equilibrium, in materials lacking symmetries and evolving through complex energy landscapes. Here, we review recent theoretical efforts to provide microscopic insights into the mechanical properties of amorphous media using approaches from statistical mechanics as unifying frameworks. We cover both the initial regime corresponding to small deformations, and the yielding transition marking a change between elastic response and plastic flow. We discuss the specific features arising for systems evolving near a jamming transition, and extend our discussion to recent studies of the rheology of dense biological and active materials. Jamming ; Rheology ; Amorphous solid ; Plasticity ; Statistical physics ; State of matter ; Granular matter ; Active matter ; Nanotechnology ; Materials science ; Physics ; Granular material ; Condensed matter physics ; Chemistry ; Biology ; Thermodynamics ; Quantum mechanics Material Dynamics and Properties ; Blood properties and coagulation ; Granular flow and fluidized beds
Authors: Felipe Tirado | Xaviera A. López-Cortés | Vicente Macaya Mejías | David Zabala‐Blanco | José M. Manríquez-Troncoso | Roberto Ahumada‐García
Source: Journal of Computer Science and Technology (article, 2024-10-18)
Field: Chemistry — Spectroscopy

Predicting Bacterial Antibiotic Resistance using MALDI-TOF Mass Spectrometry Databases with ELM Applications. Early detection of antibiotic resistance is a crucial task, especially for vulnerable patients under prolonged treatments with a single antibiotic. To solve this, machine learning approaches have been reported in the state of art. Researchers have used MALDI-TOF MS in order to predict antibiotic resistance and/or susceptibility in bacterial samples. Weis, et al. implemented LR, LightGBM and ANN to study the antibiotic resistance on bacterial strains of Escherichia Coli, Staphylococcus Aureus, and Klebsiella Pneumoniae. Despite promising results, the models have not achieved perfect accuracy, specifically when the classes are unbalanced. On the other hand, Extreme Learning Machine (ELM) is a training algorithm for forward propagation of single hidden layer neural networks, which converges much faster than traditional methods and offers promising performance along with less programmer intervention. In this way, this study introduced improved ELMs, including two weighted ELMs proposed by Zong, and the SMOTE technique in order to create new synthetic samples of the minority class. After heuristic optimization of ELM hiper-parameters, results demonstrated 85% in accuracy and 85% in geometric mean for the classification problem in the case of weighted ELM 1 subject to the SMOTE technique of oversampling. Computer science ; Mass spectrometry ; Database ; Antibiotic resistance ; Antibiotics ; Microbiology ; Chemistry ; Chromatography ; Biology Mass Spectrometry Techniques and Applications ; Bacterial Identification and Susceptibility Testing ; Advanced Chemical Sensor Technologies
Authors: Hüseyin Güray Çiftçi | Gonca Bilge | Ezgi Aytaç
Source: Journal of Food Process Engineering (article, 2024-03-01)
Field: Chemistry — Analytical Chemistry

Monitoring the quality changes and freshness assessment of eggs based on chemometrics method Abstract Despite their low cost, egg mislabeling is a prevalent problem in the market, resulting in the sale of stale eggs as fresh. In this study, unlike the classical egg freshness parameters such as Haugh Unit and air cell height, UV–Visible (UV‐Vis) spectra, fluorescence spectroscopy, and pH + color properties combined chemometrics methods were used to monitor the eggs' ages. It is known that these methods are able to detect furosine formation and chemical changes during storage. To do this, the qualities of eggs were observed for 15–24 days at three different temperatures: 4°C (refrigeration temperature), 25°C (room temperature), and 35°C (abuse temperature). After conducting a pattern recognition analysis using principal component analysis (PCA), a correlation between storage days and UV–Vis spectra, fluorescence spectra, and pH + color parameters at various temperatures was created using the partial least‐squares (PLS) method. Results showed that PLS models were able to predict eggs' ages successfully. The most reliable age assessment models for eggs were obtained for UV–Vis spectra and fluorescence spectra (ex: 320 nm) of egg samples at 35°C. Real and predicted ages of eggs were presented in validation graphs of UV–Vis spectra and fluorescence spectra (ex: 320 nm) with excellent performance of R 2 P of 0.96 and 0.97, and root mean square error of prediction (RMSEP) of 1.08 and 0.87, respectively. This method could help food safety organizations use UV–Vis and fluorescence spectrometers at various levels of the egg supply chain as an alternative method. Chemometrics ; Partial least squares regression ; Analytical Chemistry (journal) ; Chemistry ; Principal component analysis ; Fluorescence spectroscopy ; Fluorescence ; Spectral line ; Chromatography ; Mathematics ; Statistics ; Optics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Listeria monocytogenes in Food Safety
Authors: Teodora Eliana Petcov | M. Straticiuc | D. Iancu | Dragoș Alexandru Mirea | Roxana Truşcă | Paul Mereuță | Diana Savu | George Dan Mogoşanu | Laurenţiu Mogoantă | Roxana Cristina Popescu | Verena Kopatz | Sorin Ion Jinga
Source: Journal of Functional Biomaterials (article, 2024-06-19)
Field: Materials Science — Biomaterials

Unveiling Nanoparticles: Recent Approaches in Studying the Internalization Pattern of Iron Oxide Nanoparticles in Mono- and Multicellular Biological Structures Nanoparticle (NP)-based solutions for oncotherapy promise an improved efficiency of the anticancer response, as well as higher comfort for the patient. The current advancements in cancer treatment based on nanotechnology exploit the ability of these systems to pass biological barriers to target the tumor cell, as well as tumor cell organelles. In particular, iron oxide NPs are being clinically employed in oncological management due to this ability. When designing an efficient anti-cancer therapy based on NPs, it is important to know and to modulate the phenomena which take place during the interaction of the NPs with the tumor cells, as well as the normal tissues. In this regard, our review is focused on highlighting different approaches to studying the internalization patterns of iron oxide NPs in simple and complex 2D and 3D in vitro cell models, as well as in living tissues, in order to investigate the functionality of an NP-based treatment. Internalization ; Nanotechnology ; Materials science ; Iron oxide nanoparticles ; Nanoparticle ; Multicellular organism ; Cancer ; Nanomedicine ; Cell ; Cancer therapy ; Cancer cell ; Chemistry ; Biology ; Biochemistry Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Nanoplatforms for cancer theranostics
Authors: Nicholas B. Hamilton | Steve Arns | Mee Shelley | Irene Bechis | John C. Shelley
Source: ChemRxiv (preprint, 2024-03-29)
Field: Chemistry — Organic Chemistry

Calculating Apparent pKa Values of Ionizable Lipids in Lipid Nanoparticles Creating new ionizable lipids for use in lipid nanoparticles (LNPs) is an active field of research. One of the critical properties for selecting suitable ionizable lipids is the apparent pKa value of the lipid as formulated in an LNP. We have developed a structure-based, computational methodology for the prediction of the apparent pKa value of ionizable lipids within LNPs. This methodology has been validated for the three most successful ionizable lipids to date which are present in the mRNA LNP COVID-19 vaccines COMIRNATY® (Pfizer/BioNTech) and Spikevax® (Moderna), and the siRNA LNP therapeutic Onpattro® (Alnylam). The calculation was also applied to Lipid A, a variant of the ionizable lipid used in COMIRNATY®. We believe that this new technology permits systematic computational prescreening of ionizable lipids to select the most promising candidates for synthesis and experimental testing, accelerating the formulation improvement process and reducing costs. Chemistry ; Nanoparticle ; Biochemistry ; Biophysics ; Nanotechnology ; Materials science ; Biology Free Radicals and Antioxidants ; Process Optimization and Integration ; Computational Drug Discovery Methods
Authors: Rick Hewitt | Kevin M. Dalton | Derek Mendez | Harrison K. Wang | Margaret A. Klureza | Dennis E. Brookner | Jack B. Greisman | David McDonagh | V. Šrajer | Nicholas K. Sauter | Aaron S. Brewster | Doeke R. Hekstra
Source: Structural Dynamics (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Laue-DIALS: Open-source software for polychromatic x-ray diffraction data Most x-ray sources are inherently polychromatic. Polychromatic ("pink") x-rays provide an efficient way to conduct diffraction experiments as many more photons can be used and large regions of reciprocal space can be probed without sample rotation during exposure-ideal conditions for time-resolved applications. Analysis of such data is complicated, however, causing most x-ray facilities to discard >99% of x-ray photons to obtain monochromatic data. Key challenges in analyzing polychromatic diffraction data include lattice searching, indexing and wavelength assignment, correction of measured intensities for wavelength-dependent effects, and deconvolution of harmonics. We recently described an algorithm, Careless, that can perform harmonic deconvolution and correct measured intensities for variation in wavelength when presented with integrated diffraction intensities and assigned wavelengths. Here, we present Laue-DIALS, an open-source software pipeline that indexes and integrates polychromatic diffraction data. Laue-DIALS is based on the dxtbx toolbox, which supports the DIALS software commonly used to process monochromatic data. As such, Laue-DIALS provides many of the same advantages: an open-source, modular, and extensible architecture, providing a robust basis for future development. We present benchmark results showing that Laue-DIALS, together with Careless, provides a suitable approach to the analysis of polychromatic diffraction data, including for time-resolved applications. Diffraction ; Monochromatic color ; Deconvolution ; Software ; Optics ; Computer science ; Physics ; Wavelength X-ray Diffraction in Crystallography ; Enzyme Structure and Function ; Advanced X-ray Imaging Techniques
Authors: Saara Sippola | Milica Todorović | Emilia Peltola
Source: ACS Omega (article, 2024-08-03)
Field: Materials Science — Materials Chemistry

First-Principles Structure Search Study of 17-β-Estradiol Adsorption on Graphene 17-Beta-estradiol (E2), a steroid hormone synthesized from cholesterol, has various impacts on health and the environment. Currently, the gold standard for its measurement in the body is a conventional blood test (mass spectrometry), but carbon-based electrochemical sensors have been proposed as an alternative due to their advantages, such as rapid analysis time and sensitivity. To improve the atomic-level understanding of the interactions at the substrate surface, we performed density functional theory (DFT) simulations to study the nature of the adsorption of E2 on pristine graphene. Bayesian Optimization Structure Search (BOSS) was employed to reduce human bias in the determination of the most favorable adsorption configurations. Two stable adsorption minimum configurations were found. Analysis of their electronic properties indicates that E2 physisorbs on graphene. Embarking upon complex carbonaceous materials, the importance of finding all possible minimum candidates with automated structure search tools is highlighted. Computational investigations facilitate tailoring substrate materials with outstanding performance and applications in neuroscientific research, fertility monitoring, and clinical trials. Combining them with experimental research carries significant potential to advance sensor design beyond the current state-of-the-art. Graphene ; Adsorption ; Density functional theory ; Nanotechnology ; Materials science ; Substrate (aquarium) ; Computer science ; Sensitivity (control systems) ; Computational chemistry ; Chemistry ; Electronic engineering ; Physical chemistry ; Engineering 2D Materials and Applications ; Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties
Authors: Abolfazl Azadi | Sirous Shakeri | Hossein Azadi
Source: Environment Development and Sustainability (article, 2024-04-02)
Field: Materials Science — Biomaterials

Impact of different land uses on potassium forms and soil properties: the case of southwestern Iran Potassium ; Soil science ; Geography ; Environmental science ; Geology ; Chemistry ; Organic chemistry Clay minerals and soil interactions ; Agricultural Science and Fertilization ; Plant Micronutrient Interactions and Effects
Authors: Abdul Rehman Basharat | Xingzhao Xiong | Tian Xu | Yong Zang | Liangliang Sun | Xiaowen Liu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-04-09)
Field: Chemistry — Spectroscopy

TopDIA: A Software Tool for Top-Down Data-Independent Acquisition Proteomics Top-down mass spectrometry is widely used for proteoform identification, characterization, and quantification owing to its ability to analyze intact proteoforms. In the last decade, top-down proteomics has been dominated by top-down data-dependent acquisition mass spectrometry (TD-DDA-MS), and top-down data-independent acquisition mass spectrometry (TD-DIA-MS) has not been well studied. While TD-DIA-MS produces complex multiplexed tandem mass spectrometry (MS/MS) spectra, which are challenging to confidently identify, it selects more precursor ions for MS/MS analysis and has the potential to increase proteoform identifications compared with TD-DDA-MS. Here we present TopDIA, the first software tool for proteoform identification by TD-DIA-MS. It generates demultiplexed pseudo MS/MS spectra from TD-DIA-MS data and then searches the pseudo MS/MS spectra against a protein sequence database for proteoform identification. We compared the performance of TD-DDA-MS and TD-DIA-MS using <i>Escherichia coli</i> K-12 MG1655 cells and demonstrated that TD-DIA-MS with TopDIA increased proteoform and protein identifications compared with TD-DDA-MS. Computer science ; Proteomics ; Software ; Data science ; Software engineering ; Data mining ; Computational biology ; Biology ; Programming language Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Advanced Biosensing Techniques and Applications
Authors: Syed Imran Ali | Shaine Mohammadali Lalji | Awan Zahoor | Saud Hashmi | Nusrat Husain | Firoz Khan | Awatef Salem Balobaid | Ashraf Yahya | Muneeb Burney | Muhammad Qasim | Muhammad Asad | Muhammad Junaid
Source: Chemical Papers (article, 2024-11-16)
Field: Chemistry — Analytical Chemistry

Understanding live oil composition effect on asphaltene precipitation as a function of temperature change during depressurization using machine learning techniques The study aims to determine the live crude oil compositional feature’s effect on asphaltene precipitation as a function of temperature. In this study, we have applied different modern feature engineering techniques incorporated with machine learning to understand the importance of governing composition features affecting the asphaltene precipitation as a function of temperature during depressurization. To achieve this purpose, different feature selection techniques integrated with the famous random forest (RF) algorithm were applied to the high pressure high temperature (HPTP) experimental data of ten live crude oil samples available in the published literature having outcome as asphaltene precipitation increase or decrease as a result of temperature rise. All data were visualized by using different techniques. Since the data was scarce in the literature, therefore, to avoid overfitting issues the recursive feature elimination with a fourfold cross-validation technique was applied. Random forest algorithm was trained on 60% of the dataset, while testing was done on the remaining 40% dataset. An accuracy of 100% was achieved during the training phase, while it decreased to zero when applied to the testing dataset. The results were validated using a gradient boosting machine (GBM) and found to be in excellent agreement. However, the implementation of other advanced data science techniques aided in improving the accuracy of the testing phase but to very little margin, i.e., from 0 to 25%. Generally, Heavy ends, Light ends and API were found to be the important features in deciding the trend of asphaltene precipitation with temperature changes. Crude oils with higher Heavy ends or decreased API were found to increase asphaltene precipitation when temperature rises. Since, due to the complex relationship of asphaltene precipitation concerning temperature, the study will help in the prediction of the expected trend of asphaltene precipitation for different types of crude oil under field conditions when the temperature will change during production. Asphaltene ; Random forest ; Overfitting ; Feature selection ; Precipitation ; Computer science ; Artificial intelligence ; Machine learning ; Environmental science ; Feature (linguistics) ; Petroleum engineering ; Materials science ; Geology ; Meteorology ; Artificial neural network ; Physics Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Zheng-Lan Zhou | Zhu Junwen
Source: Journal of Optics (article, 2024-11-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dual-band circular dichroism in chiral metamaterial absorber based on vanadium dioxide Abstract Chiral metamaterial absorbers are instrumental in a wide array of applications, encompassing multifaceted optoelectronic devices, information-encrypting metamaterials, and thermal-controlled imaging and detection systems. In this study, we showcase a Ꞩ-shaped chiral metamaterial integrated with a vanadium dioxide substrate, which enables dynamic tuning of terahertz (THz) dual-band circular dichroism (CD). This absorber boasts spin-selective absorption, stemming from the selective excitation of the plasmon polaron mode on its horizontal microrod. Notably, right-handed circularly polarized light (CPL) experiences near-perfect absorption at resonance, whereas left-handed circular polarization is absorbed to a much lesser degree, yielding pronounced CD. This exceptional chiral selectivity facilitates dual-band CD effects, with peak values reaching 0.814 and 0.784. Furthermore, by manipulating the conductivity of vanadium dioxide, we can dynamically adjust both the CD and absorption peaks of the chiral metamaterial. The maximum modulation depth of the CD response can attain values as high as 0.807 and 0.776. Our work presents a novel design strategy for the active control and modulation of CPL, thereby expanding the horizons of metamaterial absorber applications within the THz spectrum. Metamaterial ; Optics ; Circular dichroism ; Vanadium dioxide ; Materials science ; Multi-band device ; Dual (grammatical number) ; Dichroism ; Vibrational circular dichroism ; Optoelectronics ; Physics ; Nanotechnology ; Chemistry ; Crystallography ; Telecommunications ; Thin film ; Computer science Metamaterials and Metasurfaces Applications ; Electromagnetic Scattering and Analysis
Authors: Jaewon Jang | Minsu Park | Yeonsang Park
Source: Light Science & Applications (article, 2024-02-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Non-interleaved chiral metasurfaces and neural networks enhance the spatial resolution of polarimetry Non-interleaved chiral metasurfaces for high-spatial-resolution polarimetry are proposed and demonstrated. Furthermore, a convolutional neural network is incorporated to analyze interferometric images with the polarization state of light, and it results in accurate Stokes parameters. Polarimetry ; Convolutional neural network ; Polarization (electrochemistry) ; Interferometry ; Stokes parameters ; Image resolution ; Resolution (logic) ; Computer science ; Optics ; Physics ; Artificial intelligence ; Chemistry ; Scattering Metamaterials and Metasurfaces Applications ; Optical Polarization and Ellipsometry ; Orbital Angular Momentum in Optics
Authors: Weiming Guo | Yuan Liu | Yu Han | Huan Tang | Xinyuan Fan | Chu Wang | Peng R. Chen
Source: National Science Review (article, 2024-05-28)
Field: Chemistry — Spectroscopy

Amplifiable protein identification via residue-resolved barcoding and composition code counting Ultrasensitive protein identification is of paramount importance in basic research and clinical diagnostics but remains extremely challenging. A key bottleneck in preventing single-molecule protein sequencing is that, unlike the revolutionary nucleic acid sequencing methods that rely on the polymerase chain reaction (PCR) to amplify DNA and RNA molecules, protein molecules cannot be directly amplified. Decoding the proteins via amplification of certain fingerprints rather than the intact protein sequence thus represents an appealing alternative choice to address this formidable challenge. Herein, we report a proof-of-concept method that relies on residue-resolved DNA barcoding and composition code counting for amplifiable protein fingerprinting (AmproCode). In AmproCode, selective types of residues on peptides or proteins are chemically labeled with a DNA barcode, which can be amplified and quantified via quantitative PCR. The operation generates a relative ratio as the residue-resolved 'composition code' for each target protein that can be utilized as the fingerprint to determine its identity from the proteome database. We developed a database searching algorithm and applied it to assess the coverage of the whole proteome and secretome via computational simulations, proving the theoretical feasibility of AmproCode. We then designed the residue-specific DNA barcoding and amplification workflow, and identified different synthetic model peptides found in the secretome at as low as the fmol/L level for demonstration. These results build the foundation for an unprecedented amplifiable protein fingerprinting method. We believe that, in the future, AmproCode could ultimately realize single-molecule amplifiable identification of trace complex samples without further purification, and it may open a new avenue in the development of next-generation protein sequencing techniques. Proteome ; Biology ; Computational biology ; DNA ; Barcode ; Nucleic acid ; DNA barcoding ; Residue (chemistry) ; Biochemistry ; Molecular biology ; Computer science Advanced Proteomics Techniques and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Peiqian Chen | Bing Dong | Weiwu Yao
Source: Computational and Structural Biotechnology Journal (article, 2024-06-12)
Field: Chemistry — Physical and Theoretical Chemistry

Numerical simulation study of nanoparticle diffusion in gray matter This study constructed 2D brain ISS models that reflected the actual ECS and simulated the diffusion of NPs within it. The study found that uncharged small NPs could effectively diffuse within the ISS and that the cell membrane potential had a limited effect on the diffusion of charged NPs. The model and findings of this study can aid the design of nanomedicines and nanocarriers for the diagnosis and treatment of brain diseases. Gray (unit) ; Nanoparticle ; Diffusion ; Statistical physics ; Nanotechnology ; Computer science ; Materials science ; Physics ; Thermodynamics ; Medicine ; Nuclear medicine Electrostatics and Colloid Interactions ; Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles