jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anjana Aravind | R. Nandakumar | Mukhtar Ahmed | Mahammad Nisar | Akhina Palollathil | Anagha Kanichery | Sourav Sreelan | KP Munavvar Sinan | Rex Devasahayam Arokia Balaya | M. Vijayakumar | Thottethodi Subrahmanya Keshava Prasad | Rajesh Raju
Source: Life Science Alliance (article, 2024-05-07)
Field: Chemistry — Spectroscopy

REMEMProt: a resource of membrane-enriched proteome profiles, their disease associations, and biomarker status The differential expression of plasma membrane proteins is integrally analyzed for their diagnosis, prognosis, and therapeutic applications in diverse clinical manifestations. Necessarily, distinct membrane protein enrichment methods and mass spectrometry platforms are employed for their global and relative quantitation. First of its kind to explore, we compiled membrane-associated proteomes in human and mouse systems into a database named, Resource of Experimental Membrane-Enriched Mass spectrometry-derived Proteome (REMEMProt). It currently hosts 14,626 proteins (9,507 proteins in <i>Homo sapiens</i>; 5,119 proteins in <i>Mus musculus</i>) with information on their membrane-protein enrichment methods, experimental/physiological context of detection in cells or tissues, transmembrane domain analysis, and their current attribution as biomarkers. Based on these annotations and the transmembrane domain analysis in proteins or their binary/complex protein-protein interactors, REMEMProt facilitates the assessment of the plasma membrane localization potential of proteins through batch query. A cross-study enrichment analysis platform is enabled in REMEMProt for comparative analysis of proteomes using novel/modified membrane enrichment methods and evaluation of methods for targeted enrichment of membrane proteins. REMEMProt data are made freely accessible to explore and download at https://rememprot.ciods.in/. Proteome ; Membrane protein ; Transmembrane protein ; Computational biology ; Biology ; Proteomics ; Transmembrane domain ; Context (archaeology) ; Chemistry ; Membrane ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Advanced biosensing and bioanalysis techniques ; Mass Spectrometry Techniques and Applications
Authors: Hamed Hamzehbahmani | Ruth V. Sabariego | Benjamin Ducharne
Source: IEEE Transactions on Instrumentation and Measurement (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Knowledge-Based Analysis of Interlaminar Faults for Condition Monitoring of Magnetic Cores With Predominant Focus on Axial Offset Between the Fault Points Condition monitoring and fault diagnosis of electromagnetic devices is a normal practice to prevent unpredicted downtime and catastrophic failure. In this sense, inter-laminar faults (ILFs) detection or fault diagnosis in the magnetic cores is a key objective. This paper aims to present advanced experimental measurements and numerical analysis to study influence of ILFs on soft magnetic properties of magnetic cores with Grain Oriented Electrical Steels (GOES). The predominant focus of these studies and associated analysis is ILFs with axial off-set between the short circuit points. To carry out the experimental measurements, stacks of four standard Epstein size strips of GOES were assembled. Each stack was subjected to minor ILF with axial off-set from 0 to 200 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mm</i> . The test samples were magnetised under controlled sinusoidal induction at a frequency of 50 Hz, and peak inductions of 1.1 T to 1.7 T. Impacts of each fault scenario on soft magnetic properties of the test samples was investigated by monitoring and interpreting the dynamic hysteresis loops (DHLs). In favour of supporting the practical measurements, accurate time-domain finite element (FE) models were also undertaken to reproduce the DHLs, and to visualise distribution of inter-laminar eddy currents and power loss caused by ILFs. Eddy current ; Offset (computer science) ; Condition monitoring ; Materials science ; Magnetic field ; Finite element method ; Fault (geology) ; Downtime ; Focus (optics) ; Mechanics ; Electronic engineering ; Structural engineering ; Computer science ; Engineering ; Electrical engineering ; Physics ; Geology ; Reliability engineering Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Anya Greenberg | Kaylia Reynolds | Michelle T. McNulty | Matthew G. Sampson | Hyun Min Kang | Dongwon Lee
Source: medRxiv (preprint, 2024-04-20)
Field: Chemistry — Analytical Chemistry

Accurate cross-platform GWAS analysis via two-stage imputation Abstract In genome-wide association studies (GWAS), combining independent case-control cohorts has been successful in increasing power for meta and joint analyses. This success sparked interest in extending this strategy to GWAS of rare and common diseases using existing cases and external controls. However, heterogeneous genotyping data can cause spurious results. To harmonize data, we propose a new method, two-stage imputation (TSIM), where cohorts are imputed separately, merged on intersecting high-quality variants, and imputed again. We show that TSIM minimizes cohort-specific bias while controlling imputation-derived errors. Merging arthritis cases and UK Biobank controls using TSIM, we replicated known associations without introducing false positives. Furthermore, GWAS using TSIM performed comparably to the meta-analysis of nephrotic syndrome cohorts genotyped on five different platforms, demonstrating TSIM’s ability to harmonize heterogeneous genotyping data. With the plethora of publicly available genotypes, TSIM provides a GWAS framework that harmonizes heterogeneous data, enabling analysis of small and case-only cohorts. Genome-wide association study ; Imputation (statistics) ; Computer science ; Machine learning ; Biology ; Single-nucleotide polymorphism ; Missing data ; Genetics Spectroscopy and Chemometric Analyses ; Statistical Methods and Inference ; Bayesian Methods and Mixture Models
Authors: Mathews Chirindo | Ettienne Cox | Kahesh Duness
Source: MATEC Web of Conferences (article, 2024-01-01)
Field: Chemistry — Spectroscopy

An optical gas imaging technique based on strobed illumination Gas leakage from equipment poses undesirable safety, environmental and operational impacts. Many optimal gas imaging techniques exist which detect and visualize gas plumes. However, most of these techniques struggle to produce clear images when the temperature gradient between the scene background and the gas plume is small. This paper presents an optical gas imaging technique that is based on strobed illumination, wherein the strobing frequency of the illuminating device is associated with the camera frame rate. Experimental test results are presented to show the improved detection of volatile organic compound gases during strobed illumination under dark room laboratory conditions where the percentage contrast value of the illuminated gas relative to its background varies by 50.8% The test results for the detection of sulphur hexafluoride gas and liquid petroleum gas under an outside environment are also presented. Liquefied petroleum gas ; Leakage (economics) ; Optics ; Sulfur hexafluoride ; Plume ; Frame rate ; Environmental science ; Materials science ; Chemistry ; Physics ; Engineering ; Waste management Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: Kento Ishii | Tomohiro Sato | Masayoshi Fuji
Source: Journal of the Japan Society of Powder and Powder Metallurgy (article, 2024-06-17)
Field: Materials Science — Ceramics and Composites

Simulation Technologies Related to the Process Design of Non-firing Solidified Ceramics A non-firing solidification method using surface-activated ceramic powders with mechanochemical treatment and solidified without a high-temperature sintering process has been studied in our group. In this method, the surfaces of ceramic particles are activated by grinding, and this activated state is used as a driving force to bond the particles together. It is essential to computationally understand the mechanical activation and solidification processes in the non-firing process at various molecular and bulk scales. This paper first outlines the differences between the non-firing solidification process and the conventional sintering process. Next, we introduce our simulation results of the non-firing solidification process and discuss future prospects. Computer science Advanced ceramic materials synthesis ; Material Selection and Properties ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Ugur Mertyurek | Riley Cumberland | William Wieselquist
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Core Physics Characteristics of Extended Enrichment and Higher Burnup Boiling Water Reactor Fuel Burnup ; Boiling water reactor ; Nuclear engineering ; Boiling ; Core (optical fiber) ; Nuclear reactor core ; Environmental science ; Materials science ; Waste management ; Physics ; Engineering ; Thermodynamics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Engineering Thermal-Hydraulics
Authors: Yoshiyuki Kobayashi | Sakiko Saito | Tatsuya Murahori
Source: Sensors (article, 2024-06-14)
Field: Materials Science — Polymers and Plastics

Classification of Fashion Models’ Walking Styles Using Publicly Available Data, Pose Detection Technology, and Multivariate Analysis: From Past to Current Trendy Walking Styles Understanding past and current trends is crucial in the fashion industry to forecast future market demands. This study quantifies and reports the characteristics of the trendy walking styles of fashion models during real-world runway performances using three cutting-edge technologies: (a) publicly available video resources, (b) human pose detection technology, and (c) multivariate human-movement analysis techniques. The skeletal coordinates of the whole body during one gait cycle, extracted from publicly available video resources of 69 fashion models, underwent principal component analysis to reduce the dimensionality of the data. Then, hierarchical cluster analysis was used to classify the data. The results revealed that (1) the gaits of the fashion models analyzed in this study could be classified into five clusters, (2) there were significant differences in the median years in which the shows were held between the clusters, and (3) reconstructed stick-figure animations representing the walking styles of each cluster indicate that an exaggerated leg-crossing gait has become less common over recent years. Accordingly, we concluded that the level of leg crossing while walking is one of the major changes in trendy walking styles, from the past to the present, directed by the world's leading brands. Multivariate statistics ; Principal component analysis ; Computer science ; Gait analysis ; Gait ; Cluster (spacecraft) ; Workload ; Multivariate analysis ; Artificial intelligence ; Physical medicine and rehabilitation ; Machine learning Textile materials and evaluations ; Diversity and Impact of Dance ; Human Pose and Action Recognition
Authors: Xiaoyan Shi | Fuming Yang | Enzhu Hou | Zhongzhu Liang
Source: Applied Sciences (article, 2024-02-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Integrated Deep Ultraviolet Doublet Metalens for Projection Imaging Metalenses, with their unique modulation of light, are in great demand for many potential applications. As a proof-of-principle demonstration, we focus on designing SiO2 metalenses that operate in the deep ultraviolet region, specifically around 193 nm. Based on the deep ultraviolet metalens proposed in this paper, an integrated deep ultraviolet doublet metalens is further offered. When the incident light is a plane wave with a wavelength of 193 nm, the integrated doublet metalens can reduce the beam size by a factor of 4:1, and the emitted light is flat. The integrated doublet metalens can project the reticle image proportionally, making the projection image clear. The integrated doublet metalens has the best imaging effect at the propagation distance of 2 μm and can tolerate ±3 degrees of incident angle deviation. Our findings establish general and systematic strategies to guide the design of traditional optical lens arrays with excellent integrated doublet metalenses and pave the way for enhanced optical performance in the application of large-relative-aperture deep ultraviolet detection, deep ultraviolet microscope systems, laser beam combining systems, deep ultraviolet lithography systems, etc. Ultraviolet ; Optics ; Physics Metamaterials and Metasurfaces Applications ; Polydiacetylene-based materials and applications ; Optical Coatings and Gratings
Authors: Bin Li | Ji‐ping Zou | Cheng‐tao Su | Feng Zhang | Yande Liu | Jian Wu | Nan Chen | Yihua Xiao
Source: Journal of Chemometrics (article, 2024-01-20)
Field: Chemistry — Analytical Chemistry

Quantitative study on impact damage of honey peaches based on reflection, absorbance, and Kubelka‐Munk spectrum combined with color characteristics Abstract Impact damage is one of the key factors affecting the quality of honey peaches. Quantitative study of impact damage is of great significance for the sorting of postharvest quality of honey peaches. In order to realize the quantitative prediction of impact damage of honey peaches, the impact damage of honey peaches was quantitatively studied based on the fusion of color characteristics with reflection spectra (R), absorbance spectra (A), and Kubelka‐Munk spectra (K‐M). The mechanical parameters of honey peaches during collision were obtained using a single pendulum collision device. Reflectance spectra and color characteristics of damaged honey peaches were obtained by a hyperspectral imaging system. The R spectra were converted into A and K‐M spectra, and the partial least squares regression (PLSR) model was built based on the three spectra and the three spectra combined with color characteristics for quantitative prediction of mechanical parameters. The results show that the prediction performance of the PLSR model is improved by combining color features with spectral information. In order to eliminate the redundant information in the spectral data, the competitive adaptive reweighted sampling (CARS) algorithm was used to select the characteristic wavelengths of the three spectra, and the selected characteristic wavelengths were fused with the color features to establish the PLSR model. The results show that the PLSR model built by the characteristic wavelengths of the A spectrum combined with the color features has the best prediction performance for the mechanical parameters. The R P value for maximum force is 0.862, and the R P value for damage depth is 0.894. The results of this study not only provide the theoretical support for the quality sorting, packaging, and transportation of honey peaches but also provide the reference for the biomechanical properties of various agricultural products. Partial least squares regression ; Hyperspectral imaging ; Absorbance ; Spectral line ; Hue ; Wavelength ; Analytical Chemistry (journal) ; Chemistry ; Biological system ; Mathematics ; Optics ; Artificial intelligence ; Computer science ; Statistics ; Physics ; Chromatography Spectroscopy and Chemometric Analyses ; Bee Products Chemical Analysis ; Meat and Animal Product Quality
Authors: Yixuan Yan | Yichun Shen | Najet Mahmoudi | Peixun Li | James P. Tellam | Richard A. Campbell | David J. Barlow | Katharina Edkins | Andrew G. Leach | M.J. Lawrence
Source: Journal of Colloid and Interface Science (article, 2024-12-18)
Field: Chemistry — Organic Chemistry

Dynamic self-assembled meso-structures formed across a wide concentration range in aqueous solutions of propranolol hydrochloride There is a continuum of particle sizes shown to exist across the entire concentration range, with molecules joining and leaving on the nanosecond timescale, and with the distributions of aggregate sizes varying with drug and salt concentration. Given that propranolol is a highly prescribed (WHO essential) medicine, disfavouring aggregators from consideration in high-throughput screening for potential new drug candidates - as many have advocated - should thus be done cautiously. Aqueous solution ; Propranolol Hydrochloride ; Hydrochloride ; Chemistry ; Chemical engineering ; Propranolol ; Materials science ; Organic chemistry Surfactants and Colloidal Systems ; Analytical Chemistry and Chromatography ; Spectroscopy and Quantum Chemical Studies
Authors: Shenghan Song | Xinyu Xia | Temair Shorty | Tongtong Li | Amy O. Stevens | Chao Zhao | Yi He
Source: Molecules (article, 2024-12-27)
Field: Chemistry — Organic Chemistry

Molecular Dynamics Insights into Peptide-Based Tetrodotoxin Delivery Nanostructures Tetrodotoxin (TTX), a potent Site-1 sodium channel blocker (S1SCB), offers highly effective local anesthetic properties with minimal addiction potential. To fully leverage TTX's capabilities as a local anesthetic, it is crucial to develop a drug delivery system that balances its systemic toxicity with its therapeutic efficacy. Recent studies have shown that peptide mixtures, derived from fragments of Site-1 sodium channel proteins and enhanced with hydrophobic tails (designated MP1 and MP2), can self-assemble into nanostructures that exhibit remarkable sustained-release capabilities for TTX. Despite the profound impact that the addition of a hydrophobic tail has on altering the release behavior of the original peptides, the atomic-level interactions and mechanisms underlying this phenomenon remain poorly understood. In this study, a combination of ColabFold and molecular dynamics (MD) simulations were used to investigate the binding interactions between TTX and the nanostructures formed by MP1 and MP2 at an atomic level. Our findings agree with experimental observations and indicate that the MP1/MP2 nanostructure demonstrates greater stability and higher binding affinity for TTX compared to their non-modified counterparts, P1 and P2. The analysis of the simulations revealed that charged amino acids, specifically aspartic acid (ASP) and glutamic acid (GLU), on the peptides are crucial for strong TTX binding and serve as the primary functional sites. Additionally, the stability of the nanostructure significantly affects TTX binding affinity, elucidating why P1, P2, MP1, and MP2 exhibit different binding capabilities despite containing identical charged residues. The results reported here may provide fundamental information to drive future research and enhance the development of TTX-based drug delivery systems. Tetrodotoxin ; Molecular dynamics ; Biophysics ; Chemistry ; Peptide ; Sodium channel ; Binding site ; Nanostructure ; Amino acid ; Hydrophobic effect ; Nanotechnology ; Biochemistry ; Sodium ; Biology ; Materials science ; Computational chemistry ; Organic chemistry Supramolecular Chemistry and Complexes ; Luminescence and Fluorescent Materials ; Supramolecular Self-Assembly in Materials
Authors: Carlo Delle Donne | Mariagrazia Iuliano | Bart van der Vecht | Guilherme Maciel Ferreira | Hana Jirovská | Thom van der Steenhoven | Axel Dahlberg | Matthew Skrzypczyk | Dario Fioretto | Markus Teller | Pavel Filippov | A. R. -P. Montblanch | Julius Fischer | Benjamin van Ommen | N. Demetriou | Dominik Leichtle | Luka Music | H Ollivier | Ingmar te Raa | Wojciech Kozłowski | T. H. Taminiau | Przemysław Pawełczak | Tracy E. Northup | Ronald Hanson | Stephanie Wehner
Source: arXiv (Cornell University) (preprint, 2024-07-25)
Field: Materials Science — Materials Chemistry

Design and demonstration of an operating system for executing applications on quantum network nodes The goal of future quantum networks is to enable new internet applications that are impossible to achieve using solely classical communication. Up to now, demonstrations of quantum network applications and functionalities on quantum processors have been performed in ad-hoc software that was specific to the experimental setup, programmed to perform one single task (the application experiment) directly into low-level control devices using expertise in experimental physics. Here, we report on the design and implementation of the first architecture capable of executing quantum network applications on quantum processors in platform-independent high-level software. We demonstrate the architecture's capability to execute applications in high-level software, by implementing it as a quantum network operating system -- QNodeOS -- and executing test programs including a delegated computation from a client to a server on two quantum network nodes based on nitrogen-vacancy (NV) centers in diamond. We show how our architecture allows us to maximize the use of quantum network hardware, by multitasking different applications on a quantum network for the first time. Our architecture can be used to execute programs on any quantum processor platform corresponding to our system model, which we illustrate by demonstrating an additional driver for QNodeOS for a trapped-ion quantum network node based on a single $^{40}\text{Ca}^+$ atom. Our architecture lays the groundwork for computer science research in the domain of quantum network programming, and paves the way for the development of software that can bring quantum network technology to society. Computer science ; Quantum computer ; Quantum network ; Human multitasking ; Node (physics) ; Quantum ; Embedded system ; Distributed computing ; Computer architecture ; Physics Diamond and Carbon-based Materials Research ; Quantum Computing Algorithms and Architecture ; Electronic and Structural Properties of Oxides
Authors: Fardeen H. Mozumder | Byung-Jun Yoon
Source: IEEE Access (article, 2024-12-27)
Field: Materials Science — Materials Chemistry

An Effective Pipeline for Training Variational Autoencoders for Synthesizable and Optimized Molecular Design Variational auto-encoders (VAE) for molecular design and optimization have gained popularity due to their efficiency in exploring high-dimensional molecular space to identify novel molecules with various properties of interest. For example, when applied to drug discovery, one may want to optimize small molecules for higher affinity against a specific target, improved bioavailability, and solubility. However, many such existing models face challenges in producing molecules that not only possess the desired properties but can be synthesized in practice. On the other hand, there exist synthetic route decoding models that can decode a molecule’s synthetic route using known reactants and reaction templates and translate it into a synthesizable version of that molecule. However, these models may not be suitable for generating molecules with diverse optimized properties. In this paper, we aim to combine the strengths of the generative and synthetic route decoding models to overcome the shortcomings of the respective models when used separately. Specifically, we propose a practical framework for enhancing the synthesizability of property-optimized molecules suggested by VAE models by retraining the original baseline model with a constrained dataset curated using a synthetic route decoder. To demonstrate its efficacy, we apply the framework to a hierarchical molecular generation model built on a VAE and show that the enhanced model can effectively produce diverse molecules with significantly improved synthesizability. Pipeline (software) ; Computer science ; Training (meteorology) ; Computer architecture ; Artificial intelligence ; Computer engineering ; Parallel computing ; Programming language Machine Learning in Materials Science