jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anne Richmond | Janet Robertson | Hannah M. Smith | Robert F. Hillary | Aleksej Zelezniak | Spyros I. Vernardis | Aleksandra D. Chybowska | Arturas Grauslys | Jure Mur | A.M. Campbell | Camilla Drake | Hannah Grant | Poppy Adkin | Matthew White | Charles Brigden | Christoph B. Messner | David J. Porteous | Caroline Hayward | Andrew M. McIntosh | Daniel L. McCartney | Markus Ralser | Riccardo E. Marioni
Source: medRxiv (preprint, 2025-08-15)
Field: Chemistry — Spectroscopy

Genome-wide analysis of 439 mass spectrometry-based proteomic profiles in a population of 15,035 Scottish individuals Abstract Understanding the genetic architecture of the circulating proteome can help determine if a protein is causally linked to disease. Previous large-scale genome-wide association studies (GWAS) of proteins have mostly been conducted to pre-defined, targeted subsets of the proteome, and have often concentrated on low abundance proteins, many of which don’t exert their main function in serum. Mass spectrometry-based proteomics facilitates the study of high-abundance proteins and their isoforms, focussing on proteins active in blood. In 15,035 individuals from Generation Scotland, we performed GWAS of 439 highly abundant serum protein groups as identified and quantified by liquid chromatography tandem mass spectrometry. We identified 1,553 independent SNP signals for 398 proteins (P Bonferroni &lt; 1.2×10 −10 ). Two-sample Mendelian Randomisation (MR) analyses were applied to test if the 398 proteins with significant SNP signals were causally associated with 79 common causes of morbidity and mortality. We report putative causal associations between 13 proteins and 17 outcomes including neuropsychiatric and cardiovascular conditions. Large scale genome-wide analyses of the high abundance proteome complement targeted approaches for the discovery of causal pathways of disease. Mass spectrometry ; Computational biology ; Population ; Genome ; Biology ; Genetics ; Medicine ; Chemistry ; Chromatography ; Environmental health ; Gene Advanced Proteomics Techniques and Applications ; Genetic Associations and Epidemiology ; Metabolomics and Mass Spectrometry Studies
Authors: Andrew J. Balmer | Gemma G. R. Murray | Stephanie W. Lo | Olivier Restif | Lucy A. Weinert
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-16)
Field: Chemistry — Spectroscopy

Antimicrobial Resistance Cartography: A Generalisable Framework for Studying Multivariate Drug Resistance Abstract The global rise in antimicrobial resistance (AMR) has motivated large-scale surveillance studies of bacterial pathogens. Although these datasets contain rich information on drug resistance, they are both complex and multivariate, with each isolate’s susceptibility measured across several antibiotics. Current analytical methods for these datasets overwhelmingly focus on genotypes, while tools to study multivariate resistance phenotypes–the direct targets of selection and clinical intervention–remain severely limited. To address this gap, we develop “Antimicrobial Resistance Cartography”, a set of methods for interpreting high-dimensional drug resistance in large isolate collections. As a proof-of-concept, we analysed 3,628 Streptococcus pneumoniae isolates from the Active Bacterial Core (ABC) surveillance program, each with MICs measured for six beta-lactam antibiotics. We demonstrate how this toolkit simplifies visualisation of antibiotic resistance data across multiple drugs, resolves common issues such as missing or censored values, and identifies genetic mutations driving correlated resistance changes across multiple antibiotics. We characterise penicillin-binding protein (PBP) substitutions that increase minimum inhibitory concentration to multiple beta-lactam subclasses, revealing subtle differences in their correlated phenotypic effects, and identifying potential constraints on resistance evolution across genetic backgrounds. AMR cartography therefore provides a versatile framework for quantifying and visualising correlated resistance across antibiotics, enhancing the interpretability of complex susceptibility data for both research and public health. Interpretability ; Antibiotic resistance ; Drug resistance ; Multivariate statistics ; Resistance (ecology) ; Selection (genetic algorithm) ; Set (abstract data type) ; Antimicrobial drug Analytical Chemistry and Chromatography ; Antibiotics Pharmacokinetics and Efficacy ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Jay Johal | Graeme M. Day
Source: Nature Communications (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Exploring organic chemical space for materials discovery using crystal structure prediction-informed evolutionary optimisation Organic molecular crystals offer a broad spectrum of potential applications. The vast number of possible molecules is both an opportunity and a challenge, because of the prohibitive expense of exhaustively searching chemical space to find novel molecules with promising solid-state properties. Computational methods can be applied to direct experimental discovery programmes using high-throughput or guided searches of chemical space. However, to date, such approaches have largely focused on molecular properties, ignoring the often significant effects of the arrangement of molecules in their crystal structure on the molecule's effectiveness for the chosen application. Here, we present an evolutionary algorithm for searching chemical space that incorporates crystal structure prediction into the evaluation of candidate molecules, allowing their fitness to be evaluated based on the predicted materials' properties. As a demonstration, the crystal structure-aware evolutionary algorithm is applied here to a search space of organic molecular semiconductors, demonstrating that the inclusion of crystal structure prediction in the fitness assessment outperforms searches based on molecular properties alone in identifying molecules with high electron mobilities. Chemical space ; Crystal structure prediction ; Crystal (programming language) ; Molecule ; Space (punctuation) ; Evolutionary algorithm Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Noora Sissala | Haris Babačić | Isabelle Rose Leo | Xiaofang Cao | Jenny Forshed | Lars E. Eriksson | Janne Lehtiö | Claudia Fredolini | Mikael Åberg | Maria Pernemalm
Source: Research Square (preprint, 2025-05-05)
Field: Chemistry — Spectroscopy

Comparative evaluation of in-depth mass spectrometry and Olink Explore 3072 for plasma proteome profiling Profiling (computer programming) ; Proteome ; Mass spectrometry ; Plasma ; Chemistry ; Computational biology ; Chromatography ; Computer science ; Biology ; Physics ; Biochemistry ; Nuclear physics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Sara Vignati | Alessio Tugnolo | Marco Torrente | Alessia Pampuri | R. Guidetti | R. Beghi | Valentina Giovenzana
Source: Journal of Agricultural Engineering (article, 2025-05-16)
Field: Chemistry — Analytical Chemistry

Cost-effective IoT hyperspectral prototype for distributed agri-food product monitoring Hyperspectral imaging (HSI) is a non-destructive technique that is employed to assess quality parameters and integrate monitoring across the supply chain in the context of Industry 4.0. Although promising, HSI faces challenges such as high cost and equipment requirements. However, advances in technology and 3D printing are enabling low-cost solutions that still need to be validated in the field. This work presents the development of a low-cost hyperspectral prototype, built using 3D elements and commercially available electronic components, and operating in the spectral range from 400 nm to 1000 nm. Furthermore, two types of gratings have been compared. In the first part of the study, the calibration process using RGB LEDs and a halogen lamp is described in detail. The second part of the study presents the results of a few applications on a food matrix under controlled light conditions, which were conducted to evaluate the performance of the prototype. The extracted spectra were normalised and subsequently pre-processed with either SNV (Standard Normal Variate) transform or the Savitzky–Golay (SG) derivative. Finally, the data were explored with PCA (Principal Component Analysis), which confirmed the ability of the prototype to distinguish samples of different colours (first trial), assess the decay of different apple samples (second trial) and differentiate between healthy and damaged tissues (third trial). The experimental results were consistent with the anticipated outcomes, and both types of grating demonstrated favourable performance. Hyperspectral imaging ; Context (archaeology) ; Computer science ; RGB color model ; Calibration ; Principal component analysis ; Quality assurance ; Artificial intelligence ; Process engineering ; Pattern recognition (psychology) ; Remote sensing ; Mathematics ; Engineering ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: 尹有为 Yin Youwei | 宋旭锋 Song Xufeng | 杨博 Yang Bo | 王玉兰 Wang Yulan | 史思琦 Shi Siqi
Source: Acta Optica Sinica (article, 2025-01-01)
Field: Chemistry — Spectroscopy

扩散式非色散红外CO2传感器及其温度补偿 研究了一种扩散式非色散红外CO2传感器，重点提高其测量精度和稳定性。精确测量CO2体积分数对环境监测和生物医学研究至关重要。基于NDIR的CO2传感器具有响应快、精度高的优点，但在环境温度的影响下会出现测量偏差。针对这一问题，提出了一种单位面拟合的温度补偿方法。通过传感器标定实验、体积分数测量实验和温度补偿实验验证了传感器的性能。同时还比较了不同温度补偿算法的效果，结果表明，在本实验条件下，单元表面拟合的温度补偿方法优于其他方法。在传感器量程为20%的条件下，其最大相对误差为1.36%。该研究结果为开放式气室结构的CO2传感器提供了一种有效的温度补偿策略，有助于提高传感器的测量精度和可靠性。 Computer science Spectroscopy and Laser Applications
Authors: Ruipeng Li | Siqing Wang | Wei Zhang | Huiwu Liu | Longxing Li | Shurong Yuan | Dong Wang | Guanjun Zhang
Source: Electronics (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

FDTD Simulation on Signal Propagation and Induced Voltage of UHF Self-Sensing Shielding Ring for Partial Discharge Detection in GIS Partial discharge (PD) is not only the primary manifestation of insulation deterioration in gas-insulated switchgear (GIS) but also a critical indicator of the equipment’s insulation condition. PD in GIS typically occurs at media interfaces such as the surface of the basin insulator and is characterized by high randomness and low amplitude. Conventional built-in ultra-high frequency sensors exhibit limitations in early warning and detection performance. This study proposes and demonstrates a self-sensing shielding ring embedded within the basin insulator, functioning as a novel UHF sensor. Finite-difference time-domain (FDTD) is a numerical method used to solve problems involving electromagnetic fields. Based on actual GIS structural parameters, a FDTD simulation platform is constructed and a built-in sensor is used as a control to evaluate the receiving performance of the self-sensing shielding ring for PD signals. Time-domain array simulations are conducted to investigate the influence of radial, angular and axial positions on the observed performance. The results show that the proposed shielding ring exhibits broadband and low-reflection characteristics, achieving an average S11 of −6.347 dB, which is significantly lower than those of the built-in sensors (−1.270 dB and −1.274 dB). The results demonstrate that the self-sensing shielding ring enables high sensitivity and the wideband detection of partial discharge, providing a new design approach and technical foundation for online early-warning systems in GIS. Partial discharge ; Electromagnetic shielding ; Ultra high frequency ; Finite-difference time-domain method ; Switchgear ; Wideband ; Broadband ; Voltage ; SIGNAL (programming language) High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Power Transformer Diagnostics and Insulation
Authors: Jian Hua | Hanwen Yuan | Wanquan Deng | T. Wang | Ebong Nathan Jeremiah | Zekun Yu
Source: Processes (article, 2025-04-09)
Field: Chemistry — Analytical Chemistry

The Influence of Parabolic Static Mixers on the Mixing Performance of Heavy Oil Dilution The static mixer is one of the key equipment for dilution transportation of heavy oil. To enhance the mixing performance of heavy oil dilution, a static mixer featuring a parabolic blade has been developed through an innovative redesign of the traditional Kenics blade. Numerical simulations of the parabolic static mixer were conducted using Fluent 2022 R1 software. The coefficients of concentration variation (COV) and pressure drop (∆P) served as evaluation indexes, and the effects of parabolic focal length (P), torsion angle (α), and length–diameter ratio (Ar) of the mixing blade on mixing performance were thoroughly analyzed. The research indicates that setting the mixing blade parameters to P = 60, α = 180°, and Ar = 1.5 results in improved mixing performance compared to the traditional Kenics static mixer, achieving a COV of 0.036, which signifies nearly complete mixing of heavy oil and light oil. As parabolic P increases, ∆P exhibits a decreasing trend, while the COV begins to show a significant difference at the outlet of the third mixing blade. As α increases, ∆P rises, while the COV decreases. A decrease in Ar causes ∆P to increase sharply. Although heavy oil and light oil can mix rapidly over a short distance, their influence on the final mixing effect is relatively minor. This study offers significant theoretical insights and practical implications for high-efficiency heavy oil dilution transportation technology. Dilution ; Mixing (physics) ; Materials science ; Physics ; Thermodynamics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Drilling and Well Engineering
Authors: Sophia Arruda Da Costa E. Silva | N McDonald | Arushi Chamaria | Joseph M. Stujenske
Source: Neurophotonics (review, 2025-01-28)
Field: Chemistry — Electrochemistry

Population imaging of internal state circuits relevant to psychiatric disease: a review Internal states involve brain-wide changes that subserve coordinated behavioral and physiological responses for adaptation to changing environments and body states. Investigations of single neurons or small populations have yielded exciting discoveries for the field of neuroscience, but it has been increasingly clear that the encoding of internal states involves the simultaneous representation of multiple different variables in distributed neural ensembles. Thus, an understanding of the representation and regulation of internal states requires capturing large population activity and benefits from approaches that allow for parsing intermingled, genetically defined cell populations. We will explain imaging technologies that permit recording from large populations of single neurons in rodents and the unique capabilities of these technologies in comparison to electrophysiological methods. We will focus on findings for appetitive and aversive states given their high relevance to a wide range of psychiatric disorders and briefly explain how these approaches have been applied to models of psychiatric disease in rodents. We discuss challenges for studying internal states which must be addressed with future studies as well as the therapeutic implications of findings from rodents for improving treatments for psychiatric diseases. Computer science ; Population ; Data science ; Mental disease ; Neuroscience ; Psychiatry ; Medicine ; Psychology Electrochemical Analysis and Applications ; Neuroscience and Neural Engineering
Authors: Keren Zhang | Xi Wang | Wan He | Jiani Wang | An He | Binbin Zheng | Yangqiu Li | Ruilian Xu | Weina Gao | Henry Lam | Ruijun Tian
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-27)
Field: Chemistry — Spectroscopy

Evaluation of the nano Liquid Chromatography coupled with Zeno TOF 7600 Mass Spectrometer for Cell Type-Resolved Spatial Proteomics Abstract In this study, we systematically evaluated the nanoLC-Zeno TOF platform using data-independent acquisition (DIA) and identified 3,142 to 7,059 protein groups from 1 to 200 ng of standard K562 digest. Additionally, a targeted acquisition strategy detected 184 peptides with enhanced signal using Zeno activation. To achieve spatial cell-type resolved proteomics, we developed a workflow integrating image-guided laser capture microdissection for analysis of gastric cancer tissue. A 30-minute nanoLC gradient facilitated the quantification of 2,514 proteins from 0.2 mm² regions representing cancer cells, cancer-associated fibroblasts (CAFs), and immune cells, as defined by multiplex immunohistochemistry. This spatial proteomics approach revealed cellular heterogeneity within the tumor microenvironment of a clinical gastric cancer sample, and targeted validation identified differential expression of 12 cancer-associated proteins comprising 103 peptides between cancer cell-enriched and CAFs-enriched regions. This workflow offers a practical strategy for spatial visual proteomic analysis in clinical tissue samples. Mass spectrometry ; Zeno's paradoxes ; Chromatography ; Proteomics ; Nano- ; Spectrometer ; Chemistry ; Analytical Chemistry (journal) ; Materials science ; Physics ; Optics ; Biochemistry ; Mathematics Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Glycosylation and Glycoproteins Research
Authors: Jonathan Decker | Vincent Florens Hasse | Julian Kunkel
Source: Preprints.org (preprint, 2025-04-08)
Field: Materials Science — Materials Chemistry

Q8S: Emulation of Heterogeneous Kubernetes Clusters Using QEMU Kubernetes has emerged as the industry standard for container orchestration in cloud environments. Its scheduler dynamically places container instances across cluster nodes based on predefined rules and algorithms. Various efforts exist to extend and improve upon the Kubernetes scheduler, however, as the majority of Kubernetes clusters operate on homogeneous hardware, most scheduling algorithms are also only developed for homogeneous systems. Heterogeneous infrastructures require specialized tuning to optimize workload assignment for which researchers and developers working on scheduling systems require access to heterogeneous hardware for development and testing, which may not be available. While simulations like CloudSim or K8sSim can provide insights, as simulations the level of detail they can offer to validate new schedulers is limited. To address this, we introduce Q8S, a tool for emulating heterogeneous Kubernetes clusters on OpenStack using QEMU. Q8S emulations provide a higher level of detail than simulations and can be used to train machine learning scheduling algorithms. By providing a more realistic environment, Q8S enables researchers and developers to test and refine their scheduling algorithms, ultimately leading to more efficient and effective heterogeneous cluster management. We release our implementation of Q8S as open source such that it can be further customized. Emulation ; Computer science ; Operating system ; Psychology Nanocluster Synthesis and Applications ; Distributed systems and fault tolerance ; Advanced Data Storage Technologies
Authors: Daniel Neumann | Miriam Kesselmeier | Louisa Redeker | Florian Schmidt | Torsten Thalheim | Thomas Peschel | Steffen Haerterich | Frank Meineke | Sven Schmiedl | Petra Thuermann | André Scherag | Markus Loeffler
Source: Studies in health technology and informatics (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Reliability of Multicentric Extracted EMR Data: Quantifying Amitriptyline Use in Elderly Patients – A POLAR_MI Study The utilisation of routine clinical data is increasingly vital for analysing care quality and supporting clinical decisions. This study within the POLAR_MI consortium developed a FHIR-based distributed validation method to quantify amitriptyline prescriptions in patients aged ≥65 years. Data from 10 German university hospitals were aggregated and validated through bidirectional checks, achieving 100% accuracy for inpatient and 82.7% for outpatient amitriptyline identification. High inter-rater reliability (Krippendorff's Alpha = 0.9814) confirmed data validity. Our approach demonstrated the feasibility of using standardised FHIR specifications for robust, privacy-compliant evidence generation in large-scale pharmacovigilance studies. Amitriptyline ; Reliability (semiconductor) ; Medicine ; Computer science ; Internal medicine ; Physics Solid-state spectroscopy and crystallography ; Advanced MRI Techniques and Applications ; Statistical Methods and Inference
Authors: Esko Juuso
Source: Linköping electronic conference proceedings (article, 2025-01-13)
Field: Chemistry — Analytical Chemistry

Nonlinearity analysis of variables for modelling and control Nonlinearities become essential in various systems when the operating area widens. The linear models are special cases for narrow areas. The behaviour is often asymmetric and can become gradually steeper or flatter depending on the case. These nonlinear effects can be analysed from data distributions for chosen operating areas. Further extensions require recursive analysis. The widely used Gaussian distribution is seldom valid for a wide area. The variable specific scaling can be presented with two second order polynomial defined by five parameters interpreted as the operating point and four corner points of the feasible range. These parameters define the shape factors which may require adjusting to fill the only requirement that the functions need to be monotonously increasing. Alternative constraints provide good solutions for combining expert knowledge with the data-based analysis. If the nonlinear behaviour is analysed correctly, only linear interactions are needed in the models. As the analysis is based on the same methodology, different applications can be combined by using appropriate process data. The smooth operation and high quality of products is the main goal of all these applications, and this can be achieved by combining these indicators with process control in the same way as it has been one for smaller indicators used in lime kiln control and water treatment. Different parts of the methodology have been tested in versatile applications. The main benefit is that the same structures can be used in various applications since the scaling functions take care of linking to the real world. Computer science ; Nonlinear system ; Control (management) ; Artificial intelligence ; Physics Spectroscopy and Chemometric Analyses ; Advanced Scientific Research Methods ; Biometric Identification and Security
Authors: Trần Anh Dũng | Nguyễn Hoàng Hiệp | Nusret Tan
Source: Journal of Science and Technology in Civil Engineering (JSTCE) - HUCE (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Ảnh hưởng đồng thời của cốt liệu lớn tái chế và tỷ lệ nước/xi măng đến cường độ chịu nén và mô đun đàn hồi của bê tông cốt liệu tái chế Cốt liệu tái chế (CLTC) từ chất thải rắn xây dựng (CTRXD) được thúc đẩy sử dụng trong sản xuất bê tông với mục tiêu phát triển bền vững trong lĩnh vực xây dựng. Giải pháp này giúp giảm khai thác tài nguyên, hạn chế phát thải CO₂ và tăng cường quản lý CTRXD. Tuy nhiên, bê tông cốt liệu tái chế (BTCLTC) vẫn tồn tại một số hạn chế, đặc biệt là tính không đồng nhất do sự biến động trong hàm lượng và chất lượng của (CLTC). Nghiên cứu này khảo sát ảnh hưởng đồng thời của cốt liệu lớn tái chế (CLLTC) và tỷ lệ nước/xi măng (N/X) đến tính chất cơ học của BTCLTC, cụ thể là cường độ chịu nén và mô đun đàn hồi, dựa trên phân tích cơ sở dữ liệu thực nghiệm đã được công bố. Từ đó, các công thức đề xuất được hiệu chỉnh dựa trên công thức của tiêu chuẩn Eurocode 2 (EC2), để dự báo chính xác hơn mô đun đàn hồi BTCLTC khi xét đến ảnh hưởng đồng thời của CLLTC (hàm lượng và chất lượng) và tỷ lệ N/X. Southeast asia ; Foundation (evidence) ; South asia Structural mechanics and materials ; Geodetic Measurements and Engineering Structures