jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shu Yang | Siyu Wu | Binyang Li | Y Liu | Fei Li | Jing Qi | Qunying Wang | Xiaohui Liang | Tiannan Guo | Zhongzhi Luan
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-05-14)
Field: Chemistry — Spectroscopy

BiATNovo: An Attention-based Bidirectional De Novo Sequencing Framework for Data-Independent-Acquisition Mass Spectrometry Abstract De novo sequencing from tandem mass spectra (MS/MS) data is a key technique for identifying novel peptides. In theory, the Data-Independent Acquisition (DIA) method can fragment all precursor ions in an unbiased and non-targeted fashion. However, each spectrum contains fragments from multiple precursor ions, and the unclear relationship between these ions and their fragments poses a significant challenge to the accuracy of de novo sequencing algorithms. Here we present BiATNovo, an attention-based bidirectional de novo peptide sequencing framework. BiATNovo comprises a bidirectional attention-based model and a bidirectional fusion-reranking post-processing module, which enables efficient capture of relationships between tandem mass spectra, fragment ions, and peptide patterns, while also expanding the candidate set to select the optimal sequence. This framework improves peptide prediction accuracy, particularly for long peptide sequences, and mitigates the imbalance where the initial amino acids are predicted more accurately than the last ones. Evaluation results demonstrate that BiATNovo outperforms existing algorithms, including DeepNovo-DIA and PepNet, in both peptid-level and amino acid-level. Furthermore, when extended to DDA datasets, BiATNovo achieves comparable performance to state-of-the-art models. Tandem mass spectrometry ; Peptide ; Fragment (logic) ; Computer science ; Computational biology ; Set (abstract data type) ; Mass spectrometry ; Amino acid ; Chemistry ; Algorithm ; Biology ; Chromatography ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Klaus Regenauer‐Lieb | Manman Hu
Source: Heliyon (article, 2023-11-23)
Field: Materials Science — Materials Chemistry

Emergence of precursor instabilities in geo-processes: Insights from dense active matter We present the hypothesis that investigation of precursor mechanisms to large scale instabilities, that have so far been overlooked in geo-processes, is possible. These precursor processes are evident in multicomponent materials, such as granular matter, when driven far from equilibrium on its microscale. The material is then classified as "dense active matter" with unexpected behaviour by non-local dissipation of internal energy releasing its dynamic incompatibility with the macroscopic gradients as self-excitation waves under external forcing. These instabilities are known in solid mechanics as flutter instabilities, nucleating at what is more widely known as an "exceptional point" in a variety of systems when two or more eigenvalues of the system coalesce. The common principle to connect processes at and across their characteristic scales is investigated using a minimalist formulation by coupling the scalar field variables of solid and fluid pressures in a compacting porous medium. We present a multiphysics generalisation of the phenomenon to the exciting findings of fluctuations with oscillatory exponential growth which nucleate at the exceptional point for inception of complex conjugate eigenmodes and propose a rigorous theory based on the extension of Onsager's theorem to non-local processes. Future work will need to compare model predictions to carefully designed laboratory experiments and expand the work to bridge the scale of the laboratory to the scale of field applications including design of new sensors tuned for detecting exceptional points preceding collapse of materials. Microscale chemistry ; Multiphysics ; Statistical physics ; Active matter ; Physics ; Mechanics ; Dissipation ; Classical mechanics ; Macroscopic scale ; Scalar (mathematics) ; Mathematics ; Finite element method ; Geometry Material Dynamics and Properties ; Granular flow and fluidized beds ; Theoretical and Computational Physics
Authors: Chongxue Bie | Peter C.M. van Zijl | Jiadi Xu | Xiaolei Song | Nirbhay N. Yadav
Source: NMR in Biomedicine (review, 2023-04-01)
Field: Materials Science — Materials Chemistry

Radiofrequency labeling strategies in chemical exchange saturation transfer MRI Chemical exchange saturation transfer (CEST) MRI has generated great interest for molecular imaging applications because it can image low-concentration solute molecules in vivo with enhanced sensitivity. CEST effects are detected indirectly through a reduction in the bulk water signal after repeated perturbation of the solute proton magnetization using one or more radiofrequency (RF) irradiation pulses. The parameters used for these RF pulses-frequency offset, duration, shape, strength, phase, and interpulse spacing-determine molecular specificity and detection sensitivity, thus their judicious selection is critical for successful CEST MRI scans. This review article describes the effects of applying RF pulses on spin systems and compares conventional saturation-based RF labeling with more recent excitation-based approaches that provide spectral editing capabilities for selectively detecting molecules of interest and obtaining maximal contrast. Magnetization transfer ; Excitation ; Nuclear magnetic resonance ; Saturation (graph theory) ; Radio frequency ; Chemistry ; Materials science ; Magnetic resonance imaging ; Analytical Chemistry (journal) ; Physics ; Radiology ; Computer science Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Louna Alsouki | Laurent Duval | Clément Marteau | Rami El Haddad | François Wahl
Source: HAL (Le Centre pour la Communication Scientifique Directe) (article, 2023-01-26)
Field: Chemistry — Analytical Chemistry

Dual-sPLS: une famille de régressions par PLS (moindres carrés partiels) duale parcimonieuse pour la sélection d'attributs et la prédiction, avec parcimonie accordable ; évaluation sur des données simulées et en proche infrarouge (PIR) Relating a set of variables X to a response y is crucial in chemometrics. A quantitative prediction objective can be enriched by qualitative data interpretation, for instance by locating the most influential features. When high-dimensional problems arise, dimension reduction techniques can be used. Most notable are projections (e.g. Partial Least Squares or PLS ) or variable selections (e.g. lasso). Sparse partial least squares combine both strategies, by blending variable selection into PLS. The variant presented in this paper, Dual-sPLS, generalizes the classical PLS1 algorithm. It provides balance between accurate prediction and efficient interpretation. It is based on penalizations inspired by classical regression methods (lasso, group lasso, least squares, ridge) and uses the dual norm notion. The resulting sparsity is enforced by an intuitive shrinking ratio parameter. Dual-sPLS favorably compares to similar regression methods, on simulated and real chemical data. Code is provided as an open-source package in R: \url{https://CRAN.R-project.org/package=dual.spls}. Partial least squares regression ; Feature selection ; Dual (grammatical number) ; Feature (linguistics) ; Selection (genetic algorithm) ; Pattern recognition (psychology) ; Computer science ; Near-infrared spectroscopy ; Least-squares function approximation ; Artificial intelligence ; Mathematics ; Algorithm ; Statistics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Sergey Prosandeev | Sergei Prokhorenko | Yousra Nahas | Yali Yang | Changsong Xu | Julie Grollier | Diyar Talbayev | Brahim Dkhil | L. Bellaïche
Source: Neuromorphic Computing and Engineering (article, 2023-02-28)
Field: Materials Science — Materials Chemistry

Designing polar textures with ultrafast neuromorphic features from atomistic simulations Abstract This review summarizes recent works, all using a specific atomistic approach, that predict and explain the occurrence of key features for neuromorphic computing in three archetypical dipolar materials, when they are subject to THz excitations. The main ideas behind such atomistic approach are provided, and illustration of model relaxor ferroelectrics, antiferroelectrics, and normal ferroelectrics are given, highlighting the important potential of polar materials as candidates for neuromorphic computing. Some peculiar emphases are made in this Review, such as the connection between neuromorphic features and percolation theory, local minima in energy path, topological transitions and/or anharmonic oscillator model, depending on the material under investigation. By considering three different and main polar material families, this work provides a complete and innovative toolbox for designing polar-based neuromorphic systems. Neuromorphic engineering ; Anharmonicity ; Maxima and minima ; Toolbox ; Dipole ; Computer science ; Energy landscape ; Statistical physics ; Physics ; Materials science ; Artificial intelligence ; Artificial neural network ; Condensed matter physics ; Mathematics ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Perovskite Materials and Applications ; Advanced Memory and Neural Computing
Authors: Julian Schwarz | Michael Niebauer | Maria Koleśnik‐Gray | Maximilian Szabo | Leander Baier | Phanish Chava | Artur Erbe | Vojislav Krstić | Mathias Rommel | Andreas Hutzler
Source: Small Methods (article, 2023-07-18)
Field: Materials Science — Materials Chemistry

Correlating Optical Microspectroscopy with 4×4 Transfer Matrix Modeling for Characterizing Birefringent Van der Waals Materials Van der Waals materials exhibit intriguing properties for future electronic and optoelectronic devices. As those unique features strongly depend on the materials' thickness, it has to be accessed precisely for tailoring the performance of a specific device. In this study, a nondestructive and technologically easily implementable approach for accurate thickness determination of birefringent layered materials is introduced by combining optical reflectance measurements with a modular model comprising a 4×4 transfer matrix method and the optical components relevant to light microspectroscopy. This approach is demonstrated being reliable and precise for thickness determination of anisotropic materials like highly oriented pyrolytic graphite and black phosphorus in a range from atomic layers up to more than 100 nm. As a key feature, the method is well-suited even for encapsulated layers outperforming state of-the-art techniques like atomic force microscopy. van der Waals force ; Birefringence ; Materials science ; Anisotropy ; Highly oriented pyrolytic graphite ; Optoelectronics ; Matrix (chemical analysis) ; Transfer-matrix method (optics) ; Transfer matrix ; Optics ; Ellipsometry ; Nanotechnology ; Graphite ; Composite material ; Chemistry ; Computer science ; Thin film ; Physics ; Molecule Graphene research and applications ; 2D Materials and Applications ; Transition Metal Oxide Nanomaterials
Authors: Erhan Kilinc | A.M.T. Bell | Paul A. Bingham
Source: Journal of the American Ceramic Society (article, 2023-12-08)
Field: Materials Science — Ceramics and Composites

Dynamic high‐temperature crystallization and processing properties of industrial soda–lime–silica glasses Abstract In situ dynamic crystallization properties of industrial soda–lime–silica glasses at realistic processing temperatures have not yet been explored. Hence, we collected in situ high‐temperature X‐ray diffraction patterns for 10 different industrially manufactured soda–lime–silica glasses as a function of temperature between 900 and 1200°C to investigate the phase relations in their devitrified melts. The high‐temperature X‐ray diffraction study was complemented by measuring the liquidus temperature of those glasses by the temperature gradient technique. A multiple variable regression analysis was applied to the experimental and modeled data to produce a predictive model for the rate of solidification and liquidus temperature based on glass composition. We have demonstrated that forms of quartz (SiO 2 ) and Na 2 CaSiO 4 , which are not traditionally identified by room temperature X‐ray diffraction studies of commercial soda–lime–silica glasses, are the dominant crystalline phases at 800 and 900°C. Upon further heating, different forms of cristobalite become the primary phase field prior to the formation of X‐ray amorphous melts, irrespective of the glass composition. Sporadic unidentified as well as high‐temperature stable SiO 2 polymorphs that are not recoverable to room temperature were also observed. In contrast to the literature, wollastonite (CaSiO 3 ) and devitrite (Na 2 Ca 3 Si 6 O 16 ), which are the main predictor variables in previously developed liquidus temperature models, were not observed prior to the formation of X‐ray amorphous glass melts, and hence their influence on liquidus temperature may be questionable. It was also found that the difference between glass processing and liquidus temperatures can be excessively high, and such large temperature differences can potentially be exploited and reduced to enable decreases in melting or processing temperatures of industrial soda–lime–silica glass melts. Liquidus ; Cristobalite ; Materials science ; Crystallization ; Amorphous solid ; Mineralogy ; Phase (matter) ; Diffraction ; Analytical Chemistry (journal) ; Quartz ; Soda-lime glass ; Supercooling ; Chemical engineering ; Thermodynamics ; Chemistry ; Optics ; Metallurgy ; Crystallography ; Composite material ; Alloy Glass properties and applications ; Mineralogy and Gemology Studies ; Pigment Synthesis and Properties
Authors: Yuming Wang | Youshan Qu | Hui Zhao | Xuewu Fan
Source: Sensors (article, 2023-07-24)
Field: Chemistry — Analytical Chemistry

Construction, Spectral Modeling, Parameter Inversion-Based Calibration, and Application of an Echelle Spectrometer We have developed a compact, asymmetric three-channel echelle spectrometer with remarkable high-spectral resolution capabilities. In order to achieve the desired spectral resolution, we initially establish a theoretical spectral model based on the two-dimensional coordinates of spot positions corresponding to each wavelength. Next, we present an innovative and refined method for precisely calibrating echelle spectrometers through parameter inversion. Our analysis delves into the complexities of the nonlinear two-dimensional echelle spectrogram. We employ a variety of optimization techniques, such as grid exploration, simulated annealing, genetic algorithms, and genetic simulated annealing (GSA) algorithms, to accurately invert spectrogram parameters. Our proposed GSA algorithm synergistically integrates the strengths of global and local searches, thereby enhancing calibration accuracy. Compared to the conventional grid exploration method, GSA reduces the error function by 22.8%, convergence time by 2.16 times, and calibration accuracy by 7.05 times. Experimental validation involves calibrating a low-pressure mercury lamp, resulting in an average spectral accuracy error of 0.0257 nm after performing crucial parameter inversion. Furthermore, the echelle spectrometer undergoes a laser-induced breakdown spectroscopy experiment, demonstrating exceptional spectral resolution and sub-10 ns time-resolved capability. Overall, our research offers a comprehensive and efficient solution for constructing, modeling, calibrating, and applying echelle spectrometers, significantly enhancing calibration accuracy and efficiency. This work contributes to the advancement of spectrometry and opens up new possibilities for high-resolution spectral analysis across various research and industry domains. Spectrometer ; Simulated annealing ; Spectral resolution ; Inversion (geology) ; Calibration ; Spurious relationship ; Spectrogram ; Computer science ; Remote sensing ; Algorithm ; Grid ; Optics ; Physics ; Spectral line ; Mathematics ; Artificial intelligence ; Geology Spectroscopy and Chemometric Analyses ; Laser-induced spectroscopy and plasma ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Aastha Gour | Asst Professor | Ashishmishraa Sindhujasankarana | Reza Ehsania | Cristina Davisb | Bharat Niketamoda | Jadhav | Smeetanaikwadi | P Revathi | M Hemalatha | H Al-Hiary | S Bani-Ahmad | M Reyalat | M Braik | Z Alrahamneh | Wu Shenweizheng | Chen Yachun | Wei Zhanliang | Hongda | R Mrunalini | Badnakhe | R Prashant | Deshmukh | Joanna Sekulska-Nalewajko | Jaroslawgoclawski | Stephen Gang Wu | Forrest Sheng Bao | Eric Youxu | Yu -Xuan Wang | Yi -Fan Chang | S Abu-Naser | K Kashkash | M Fayyad | K Attakitmongcol | A Srikaew
Source:  (article, 2023-05-08)
Field: Chemistry — Analytical Chemistry

Grape Disease Detection Network Based On Multi Task Learning and Attention Features In this procedure, we recommend the primary causes of a significant reduction in grape production are grape diseases.Therefore, it is critical to create a technique for diagnosing diseases of grape leaves.We use deep learning methods to identify grape diseases because they have recently demonstrated impressive success in a variety of computer vision problems.A Convolutional Neural Network (CNN) architecture built on an integrated approach is suggested in this procedure.The architecture of the suggested CNNs is intended to identify leaves that have common grape diseases.As a result, the performance of the suggested technique will yield better outcomes.The primary goal of this procedure is to use grape leaves to predict plant leaf disease on the other side, to improve the functionality of the procedure. Computer science ; Task (project management) ; Disease ; Artificial intelligence ; Machine learning ; Medicine ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Computing and Algorithms
Authors: Nader Ekramirad | Alfadhl Yahya Khaled | Kevin D. Donohue | Raul T. Villanueva | Akinbode A. Adedeji
Source: Agriculture (article, 2023-04-08)
Field: Chemistry — Analytical Chemistry

Classification of Codling Moth-Infested Apples Using Sensor Data Fusion of Acoustic and Hyperspectral Features Coupled with Machine Learning Codling moth (CM) is a major apple pest. Current manual method of detection is not very effective. The development of nondestructive monitoring and detection methods has the potential to reduce postharvest losses from CM infestation. Previous work from our group demonstrated the effectiveness of hyperspectral imaging (HSI) and acoustic methods as suitable techniques for nondestructive CM infestation detection and classification in apples. However, both have limitations that can be addressed by the strengths of the other. For example, acoustic methods are incapable of detecting external CM symptoms but can determine internal pest activities and morphological damage, whereas HSI is only capable of detecting the changes and damage to apple surfaces and up to a few mm inward; it cannot detect live CM activity in apples. This study investigated the possibility of sensor data fusion from HSI and acoustic signals to improve the detection of CM infestation in apples. The time and frequency domain acoustic features were combined with the spectral features obtained from the HSI, and various classification models were applied. The results showed that sensor data fusion using selected combined features (mid-level) from the sensor data and three apple varieties gave a high classification rate in terms of performance and reduced the model complexity with an accuracy up to 94% using the AdaBoost classifier, when only six acoustic and six HSI features were applied. This result affirms that the sensor fusion technique can improve CM infestation detection in pome fruits such as apples. Hyperspectral imaging ; Codling moth ; Infestation ; Sensor fusion ; Postharvest ; Artificial intelligence ; Computer science ; Remote sensing ; Pattern recognition (psychology) ; Acoustics ; Horticulture ; Biology ; Botany ; Lepidoptera genitalia ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Insect Pheromone Research and Control
Authors: Qipeng Tan | Bin Song | Linong Wang | Shaocheng Wu
Source: IET Science Measurement & Technology (article, 2023-03-23)
Field: Materials Science — Materials Chemistry

Study on characteristics gases of metal protrusions partial discharge in 10‐kV air‐insulated switchgear Abstract Air‐insulated switchgear plays an important role in distribution network and partial discharge (PD) is one of the common faults in its operation. The method of gas component analysis, which is to detect chemical gas products, is a novel one to discover PD. In order to study the feasibility of the method, the monitoring experiment of characteristic gases, comprising carbon monoxide (CO), nitrogen dioxide (NO 2 ), and ozone (O 3 ), of metal protrusion PD is carried out. This paper conducts the comparative experiments on three main factors affecting PD: voltage, air humidity and degree of protrusion, as well as studies how they affect the formation of characteristic gases. According to the experimental result, higher applied voltage promotes the formation of characteristic gases in the same conditions. In addition, with the increase of humidity from 40% to 75%, the generation of gases is inhibited. Furthermore, when the needle–plate distance is 30 mm, the gas concentration has almost no change within 6 h, which is different from that of 10 mm. In conclusion, the difference in gases concentration reveals the influence of three main factors on the generation of characteristic gases and provides an analytical basis for PD detection by gas composition analysis. Switchgear ; Partial discharge ; Humidity ; Carbon monoxide ; Ozone ; Materials science ; Voltage ; Analytical Chemistry (journal) ; Carbon dioxide ; Dielectric gas ; Electric discharge in gases ; Electrode ; Chemistry ; Electrical engineering ; Composite material ; Thermodynamics ; Environmental chemistry ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Aerosol Filtration and Electrostatic Precipitation
Authors: Nathaniel Kwok | Zita E. H. Aretz | Sumiko Takao | Zheng Ser | Paolo Cifani | Alex Kentsis
Source: Journal of Proteome Research (article, 2023-07-07)
Field: Chemistry — Spectroscopy

Integrative Proteogenomics Using ProteomeGenerator2 Recent advances in nucleic acid sequencing now permit rapid and genome-scale analysis of genetic variation and transcription, enabling population-scale studies of human biology, disease, and diverse organisms. Likewise, advances in mass spectrometry proteomics now permit highly sensitive and accurate studies of protein expression at the whole proteome-scale. However, most proteomic studies rely on consensus databases to match spectra to peptide and protein sequences, and thus remain limited to the analysis of canonical protein sequences. Here, we develop ProteomeGenerator2 (PG2), based on the scalable and modular ProteomeGenerator framework. PG2 integrates genome and transcriptome sequencing to incorporate protein variants containing amino acid substitutions, insertions, and deletions, as well as noncanonical reading frames, exons, and other variants caused by genomic and transcriptomic variation. We benchmarked PG2 using synthetic data and genomic, transcriptomic, and proteomic analysis of human leukemia cells. PG2 can be integrated with current and emerging sequencing technologies, assemblers, variant callers, and mass spectral analysis algorithms, and is available open-source from https://github.com/kentsisresearchgroup/ProteomeGenerator2. Proteogenomics ; Computational biology ; Biology ; Proteome ; Proteomics ; Genome ; Genomics ; DNA sequencing ; Transcriptome ; Cancer genome sequencing ; Genetics ; Gene ; Gene expression Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Machine Learning in Bioinformatics