jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthew T Karadzas | Agnès H. Michel | Andreas Mosbach | George Giannakopoulos | Ruairi McGettigan | Gabriel Scalliet | Benoı̂t Kornmann
Source:  (preprint, 2025-02-19)
Field: Materials Science — Biomaterials

SATAY-based chemogenomic screening uncovers antifungal resistance mechanisms and key determinants of ATI-2307 and chitosan sensitivity Abstract Multidrug-resistant fungal pathogens are a serious threat to public health and global food security. Mitigation requires the discovery of antifungal compounds with novel modes of action, along with a comprehensive understanding of the molecular mechanisms governing antifungal resistance. Here, we apply SAturated Transposon Analysis in Yeast (SATAY), a powerful transposon sequencing method in Saccharomyces cerevisiae, to uncover loss- and gain-of-function mutations conferring resistance to 20 different antifungal compounds. These screens identify a diverse array of novel resistance mechanisms and multiple modes of action. SATAY is performed in drug-sensitive strains to generate chemogenomic profiles for compounds that lack activity against conventional laboratory strains. This study therefore provides a significant resource for exploring cellular responses to chemical stresses. We discover that the natural antifungal Chitosan electrostatically interacts with cell wall mannosylphosphate, and that the transporter Hol1 concentrates the novel antifungal ATI-2307 within yeast. This latter finding presents an avenue for drug design initiatives, but also unveils a straightforward evolutionary path to ATI-2307 resistance with minimal fitness cost. Antifungal ; Chitosan ; Key (lock) ; Sensitivity (control systems) ; Chemistry ; Computational biology ; Microbiology ; Computer science ; Biology ; Biochemistry ; Engineering ; Computer security Nanocomposite Films for Food Packaging ; Studies on Chitinases and Chitosanases ; Antifungal resistance and susceptibility
Authors: Jordy Sarmas-Farfan | Antonio Díaz‐Barrios | Teresa Lehmann | Vladimir Alvarado
Source: Energies (article, 2025-06-13)
Field: Chemistry — Analytical Chemistry

Demulsification Kinetics of Water-in-Oil Emulsions of Ecuadorian Crude Oil: Influence of Temperature and Salinity This work focuses on the stability analysis of water-in-oil macroemulsions with a crude oil from the Sacha Field in Ecuador. This field is an important hydrocarbon resource in Ecuador with a typical bottom freshwater drive. The comprehensive stability analysis includes coalescence, water resolution or phase separation, and water–oil interfacial tension and interfacial dilatational viscoelastic modulus measurements over time. Two main parameters, due to their relevance, were controlled in these experiments: water salinity and temperature. The analysis reported here is the first focused on this important resource in Ecuador. Findings shed light on which mechanisms more likely control the stability of these water-in-oil macroemulsions. Results herein suggest that regardless of temperature, low-salinity water favors emulsion stability, likely due to the tendency of a stiffer interface formation at low-ionic strength, as interfacial viscoelasticity measurements show. This implies that the low-ionic strength water from the aquifer can enable the formation of stable emulsions. In contrast, water resolution depends significantly on temperature, possibly due to higher sedimentation rates. The implication is that if emulsions do not break up before cooling off, the emulsion can become more stable. Finally, analysis of the interface buildup rates could explain the observed increase in emulsion stability over time. Salinity ; Kinetics ; Crude oil ; Chemistry ; Emulsion ; Petroleum engineering ; Formation water ; Chromatography ; Pulp and paper industry ; Chemical engineering ; Environmental science ; Geology ; Organic chemistry ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Mohammad H. Javani | Mohammadreza Zandshahvar | Ali Adibi
Source:  (preprint, 2025-08-25)
Field: Materials Science — Surfaces, Coatings and Films

Optimizing Neural Networks for Nanophotonic Inverse Design Through Statistical and Computational Complexity Analysis Optimizing neural networks (NNs) for inverse design in nanophotonics presents unique challenges due to the varying complexity of optical responses across different structures. This study introduces a machine learning-centric framework to systematically evaluate and optimize NN architectures based on both statistical and computational complexity. We assess statistical complexity through bias-variance tradeoff analysis, data efficiency, and latent space dimensionality using autoencoders, revealing how structural intricacy impacts generalization capacity. Computational complexity is analyzed through FLOPS, sparsity pruning, and convergence dynamics, offering insights into resource-efficient model deployment. Across multilayer films, metasurfaces, and gratings, we identify clear trends linking physical complexity to optimal model depth, width, and training regimes. Our findings highlight the importance of architectural tuning and model compression in achieving high accuracy with minimal computational overhead. This work provides a practical roadmap for designing interpretable, efficient, and scalable deep learning models tailored to complex physics-driven design problems. Nanophotonics ; Computer science ; Inverse ; Computational complexity theory ; Artificial neural network ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Mathematics ; Nanotechnology ; Materials science Optical Coatings and Gratings ; Near-Field Optical Microscopy
Authors: Antonietta Eliana Barrasso | Claudio Perone | Roberto Romaniello
Source: Applied Sciences (article, 2025-07-31)
Field: Chemistry — Analytical Chemistry

Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research. Hyperspectral imaging ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Artificial neural network ; Mathematics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Indra Purnama | Suratno Suratno | Anisa Mutamima | Anjar Windarsih | Dita Fitriani | Vonny Indah Sari | Riskina Juwita
Source: E3S Web of Conferences (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Discrimination of Rice Varieties Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Combined with Chemometric Methods Ensuring the authenticity and quality of rice varieties is crucial for protecting market value and maintaining consumer trust. This study evaluates the potential of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric methods to accurately discriminate three Indonesian rice varieties: Inpari42, Pandan Wangi, and Logawa. Spectral data collected in the mid-infrared region (4000−600 cm −1 ) were analyzed using Partial Least Squares-Discriminant Analysis (PLS-DA), sparse PLS-DA (sPLS-DA), and Hierarchical Cluster Analysis (HCA). While PLS-DA provided clear varietal separation, minor overlaps were observed, which were effectively resolved by sPLS-DA through the identification of key spectral variables in carbohydrate, protein, and lipid regions. HCA further validated the classification, forming distinct clusters for each variety. This study highlights ATR-FTIR spectroscopy as a rapid, non-destructive, and reliable tool for rice variety discrimination, with promising applications for global rice quality control and authentication. Chemometrics ; Principal component analysis ; Spectroscopy ; Near-infrared spectroscopy ; Infrared spectroscopy ; Mid infrared ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Raghunandan Pratoori | Hamed Babaei | Valery I. Levitas
Source: ArXiv.org (preprint, 2025-05-14)
Field: Materials Science — Materials Chemistry

Analytical and Scale-Free Phase-Field Studies of $α$ to $ω$ Phase Transformation in Single Crystal Zirconium under Nonhydrostatic Loadings Zirconium (Zr) is an important engineering material with numerous practical applications. It undergoes martensitic $α$ to $ω$ phase transformation (PT) at pressures that vary from 0.67 GPa to 17 GPa under different loading conditions. Despite numerous experimental and theoretical studies, the effect of the nonhydrostatic stresses is not well understood. To separate the effect of nonhydrostatic stresses from the plastic deformation, a scale-free phase field approach (PFA) for multivariant $α$ to $ω$ PT in a single crystal Zr under general nonhydrostatic loadings is presented. Explicit conditions for the direct and reverse PTs between austenite and martensitic variants and between martensitic variants under general stress tensor are derived and analyzed. In particular, the effect of the deviatoric stresses on the PT pressures is elucidated. It is shown that their effect cannot explain much larger reduction in the transformation pressure observed during plastic flow, i.e., specific mechanisms of strain-induced phase transformations should be involved. Under assumption of the homogeneous fields in the sample, complete analytical solutions that include stress-strain curves during the PT, PT start and finish stresses (i.e., stress hysteresis), and volume fraction of the variants, are determined for different loadings. Finite element method (FEM) solutions are found for the phase field simulations of the microstructure evolution for the same loadings, as well as for two grains of the polycrystalline sample. Macroscopic averaged characteristics of the PFA solutions are well described by an analytical solution, which also simplifies their interpretations. Obtained results are in good qualitative agreement with existing experiments. In addition, some controversies of the previous approaches are analysed. Martensite ; Austenite ; Diffusionless transformation ; Zirconium ; Phase (matter) ; Stress (linguistics) ; Single crystal ; Volume fraction ; Microstructure Nuclear Materials and Properties ; X-ray Diffraction in Crystallography ; Intermetallics and Advanced Alloy Properties
Authors: D. Suma | V G Narendra | M. Darshan Holla | M. Raviraja Holla
Source: Discover Applied Sciences (article, 2025-10-09)
Field: Chemistry — Analytical Chemistry

Intelligent rice quality assessment using hybrid CNN-clustering approach While rice quality assessment can be performed through various approaches, this paper focuses specifically on broken rice percentage detection, which is critical for trade compliance and international export standards. Current methods face two primary limitations: reliance on fixed thresholds to classify broken versus head rice, which fails across different rice varieties, and fragment counting approaches that inflate broken rice percentages by multiple counting fragments originating from the same rice kernel. This paper presents a novel integrated approach combining unsupervised K-means clustering with supervised deep learning for enhanced rice grading accuracy. The system uses K-means clustering with morphological features to automatically separate head rice from fragments without arbitrary thresholds, addressing variability across rice varieties. A convolutional neural network (CNN) then classifies identified fragments into three morphological categories—Hooked Top (HT), Rectangular Midsection (RM), and Rounded Bottom (RB)—achieving 98.95% accuracy. The key innovation lies in a reconstruction algorithm that intelligently recombines complementary fragment pieces to determine true broken rice rate (BRR), unlike conventional methods that overestimate by counting fragments individually. Size proportions (1/3, 1/2, 2/3) are adaptively determined relative to each sample's head rice dimensions from clustering. The CNN architecture features three convolutional blocks with optimized regularization, trained on 300 original basmati rice images systematically augmented to 5,700 samples through rotation, scaling, and flipping techniques, with 75% allocated for training and 25% for testing. Experimental results demonstrate consistent performance and superior accuracy in automated quality assessment for precise rice grading. New method improves rice grading by reducing errors in detecting broken grains. Combines AI and clustering to adapt across different rice varieties. Achieves 98.9% accuracy, helping ensure fair trade and export quality. Convolutional neural network ; Cluster analysis ; Pattern recognition (psychology) ; Artificial neural network ; Grading (engineering) ; Quality assessment Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Usman Masud
Source: Optica Pura y Aplicada (article, 2025-12-31)
Field: Chemistry — Spectroscopy

Investigation of Thermal Variations on Side Mode Suppression Ratio in the Dual Mode Spectroscopic Sensors A dual-mode biomedical sensor is being developed based on the principle of intra-cavity absorption spectroscopy.As the foundation of this system lies in Fibre Bragg Gratings, it is essential to thoroughly evaluate their complete performance following intense hysteresis analysis.Since two wavelengths (modes) are present in the system, we use the Side Mode Suppression Ratio to check their existence in the presence of neighboring ones, starting with an investigation at the specified temperature and injection current.Both modes have shown intense competition with the side modes, technically validated by two equipment.This revealed a novel correlation between the dominant modes and the side modes, considering numerous factors in cavity optomechanics, such as red and blue shifts.A strong interplay between different types of semiconductors in a lasing system is explored and established, which helps in understanding the development of our dual-mode sensor and discovering strong clues for the competition between multiple modes. Thermal ; Mode (computer interface) ; Dual mode ; Dual (grammatical number) ; Temperature measurement Spectroscopy and Laser Applications ; Advanced Fiber Laser Technologies ; Mechanical and Optical Resonators
Authors: E López | Jorge Angel Velasco Espinal | Ana Paula Calderón Aguirre | S Martínez | Alexa Fernanda Uriostegui Navarro | Pablo Manuel Cervantes Barreto
Source: Educational Regent Multidisciplinary Journal (article, 2025-07-25)
Field: Materials Science — Biomaterials

Evaluación experimental de nanopartículas como vehículos de administración controlada de fármacos: resultados preliminares in vitro The development of nanotechnology-based drug delivery systems has revolutionized strategies for therapeutic targeting, enabling more precise and efficient pharmacological interventions. This study presents a comparative in vitro evaluation of three nanoparticle platforms—PLGA, lipid-based, and mesoporous silica nanoparticles—as vehicles for controlled drug delivery. The formulations were characterized in terms of particle size, zeta potential, drug release kinetics at physiological and acidic pH, thermal stability, and cellular uptake. PLGA nanoparticles demonstrated optimal release under acidic conditions, making them promising for tumor-targeted therapies. Lipid nanoparticles offered enhanced biocompatibility and uptake, while mesoporous silica nanoparticles stood out for their high thermal resistance and prolonged drug retention. The integration of artificial intelligence in modeling drug release patterns supported the predictive reliability of the experimental data. These findings highlight the potential of tailored nanocarrier systems in improving drug bioavailability, stability, and delivery precision, laying the groundwork for future in vivo and clinical research. Nanocarriers ; Drug delivery ; PLGA ; Mesoporous silica ; Nanoparticle ; Biocompatibility ; Zeta potential ; Nanotechnology ; Drug ; Chemistry ; Bioavailability ; In vivo ; Pharmacology ; Materials science ; Mesoporous material ; Medicine ; Biochemistry ; Biology ; Biotechnology Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Advancements in Transdermal Drug Delivery
Authors: B. Marjanović | Tea Sokač Cvetnić | Davor Valinger | Jasenka Gajdoš Kljusurić | Tamara Jurina | Maja Benković | Ana Jurinjak Tušek
Source: Foods (article, 2025-09-28)
Field: Chemistry — Analytical Chemistry

Ultrasound-Assisted Extraction of Bioactives from Spirulina platensis: Optimization and Prediction of Their Properties Using Near-Infrared Spectroscopy Coupled with Artificial Neural Network Modeling This study optimizes the ultrasound-assisted extraction (UAE) of bioactive compounds from <i>Spirulina platensis</i> and develops a rapid, non-destructive analytical method. A Box-Behnken design and desirability function were used to find the optimal extraction conditions to simultaneously maximize total polyphenols, proteins, C-phycocyanin, and antioxidant activity. The optimal conditions were a solid-to-liquid ratio of 35 g/L, a time of 20 min, a pH of 10, and a temperature of 45 °C. Independent validation confirmed the model's reliability, with experimental results closely matching predicted values. Furthermore, Near-Infrared (NIR) spectroscopy, combined with Artificial Neural Networks (ANNs), was explored as a predictive tool. The models, particularly those based on a semi-process NIR spectrometer, showed excellent predictive capabilities for key physicochemical properties, with an RPD of 3.9064 for <i>L</i>* and 2.8351 for TDS. This research establishes a highly reproducible and scalable extraction protocol, complemented by a fast and accurate analytical method, providing a significant advancement for the industrial application and quality control of <i>Spirulina platensis</i> extracts. Extraction (chemistry) ; Artificial neural network ; Spirulina (dietary supplement) ; Matching (statistics) ; Design of experiments ; Predictive modelling Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: J. Oldenburg | Marrit M. E. Tholen | Janne G. D. Donkers | Ana Ortiz‐Perez | Valentina Girola | Lorenzo Albertazzi
Source: Nanoscale Advances (article, 2025-11-17)
Field: Materials Science — Biomaterials

A high-throughput approach to optimize and understand nanoparticle protein degraders Targeted protein degradation strategies have emerged as a promising approach for addressing undruggable membrane proteins by redirecting them to the cell's degradation machinery. Traditionally, this has been achieved through antibody-based constructs that hijack the endocytic pathways. More recently, nanoparticles have been proposed as a modular platform for surface protein scavenging, offering tunable properties and multivalent targeting. However, the extent to which nanoparticle physicochemical features influence degradation efficacy remains to be elucidated. In this study, we set up a high-content imaging workflow to screen a diverse library of antibody-functionalized nanoparticles varying in material composition, size, and targeting modality. Our results reveal that specific combinations of nanoparticle properties induce efficient receptor scavenging, allowing us to understand structure-activity relations better. These findings highlight the complex interplay between nanoparticle design and biological response and demonstrate the value of our high-throughput platform for guiding the rational design of nanoparticle-based protein degraders. Nanoparticle ; Rational design ; Degradation (telecommunications) ; Modular design ; Workflow ; Endocytic cycle ; Protein degradation Nanoparticle-Based Drug Delivery ; Protein Degradation and Inhibitors ; Protein purification and stability
Authors: Thanh Luan Phan | Jialu Li | Swagata Acharya | Alice R. Giem | Md Azimul Haque | Srikrishna Sagar | Dimitar Pashov | Savio Laricchia | Elisa M. Miller | Michelle A. Smeaton | Katherine Jungjohann | Sarbajit Banerjee | Joseph M. Luther | Jinghua Guo | Andrew J. Ferguson | Jeffrey L. Blackburn | Lance M. Wheeler
Source: Advanced Functional Materials (article, 2025-11-20)
Field: Materials Science — Polymers and Plastics

Interlayer Exciton Polarons in Mesoscopic V <sub>2</sub> O <sub>5</sub> for Broadband Optoelectronic Synapses Abstract Persistent photoconductivity and optoelectronic synaptic behavior are demonstrated in solution‐processed mesoscopic α‐phase vanadium pentoxide (V 2 O 5 ) thin films. First‐principles simulations coupled with the two‐site Holstein polaron hopping model show that vacancies at the terminal oxygen position lead to long recombination times because photoexcited electrons and holes reside on different layers separated by the van der Waals gap, forming a weakly coupled interlayer exciton polaron. Mid‐gap polaronic states also significantly broaden the photoresponse of the films to span across visible and infrared wavelengths. By controlling the amplitude/intensity, duration, and/or number of optical pulses, tunable optoelectronic memory functions, such as short‐term and long‐term plasticity, are experimentally established in V 2 O 5 ‐based optoelectronic synapses. Device fabrication was extended to mechanically flexible ultrathin glass substrates. Flexible optoelectronic synapses maintained high performance after 150 bending cycles. Mesoscopic physics ; Exciton ; Polaron ; Fabrication ; van der Waals force ; Pentoxide ; Photoconductivity ; Electron Transition Metal Oxide Nanomaterials ; Catalysis and Oxidation Reactions ; Gas Sensing Nanomaterials and Sensors