jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Geetika Kaur | Shivantika Bisen | Nikhlesh K. Singh
Source: Biophysics Reviews (review, 2024-11-05)
Field: Materials Science — Materials Chemistry

Nanotechnology in retinal diseases: From disease diagnosis to therapeutic applications Nanotechnology has demonstrated tremendous promise in the realm of ocular illnesses, with applications for disease detection and therapeutic interventions. The nanoscale features of nanoparticles enable their precise interactions with retinal tissues, allowing for more efficient and effective treatments. Because biological organs are compatible with diverse nanomaterials, such as nanoparticles, nanowires, nanoscaffolds, and hybrid nanostructures, their usage in biomedical applications, particularly in retinal illnesses, has increased. The use of nanotechnology in medicine is advancing rapidly, and recent advances in nanomedicine-based diagnosis and therapy techniques may provide considerable benefits in addressing the primary causes of blindness related to retinal illnesses. The current state, prospects, and challenges of nanotechnology in monitoring nanostructures or cells in the eye and their application to regenerative ophthalmology have been discussed and thoroughly reviewed. In this review, we build on our previously published review article in 2021, where we discussed the impact of nano-biomaterials in retinal regeneration. However, in this review, we extended our focus to incorporate and discuss the application of nano-biomaterials on all retinal diseases, with a highlight on nanomedicine-based diagnostic and therapeutic research studies. Nanomedicine ; Nanotechnology ; Blindness ; Regenerative medicine ; Medicine ; Materials science ; Nanoparticle ; Optometry ; Biology ; Stem cell Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Retinal and Optic Conditions
Authors: Usman Usman
Source: Journal of Applied Data Sciences (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Improving Classification Accuracy of Local Coconut Fruits with Image Augmentation and Deep Learning Algorithm Convolutional Neural Networks (CNN) Local coconut varieties must be classified to maintain the quality and genetic diversity of coconuts as the main commodity in Indonesia's largest coconut-producing region. This study introduces a deep learning module for improved classification of coconuts, using color jitter as part of a data augmentation strategy to supplement the existing dataset and utilizing well-known CNN-based models like VGG16 for image analysis, with a focus on the needs of future research. The goal is to improve the classification accuracy of local coconut varieties through deep learning. We investigate both data augmentations and EDA, and we use VGG-16-based CNN models to enhance the classification performance. We used a confusion matrix for the model evaluation, containing metrics like accuracy, precision, recall, and f1-score. Results reveal that a color jitter augmentation model attained a training accuracy of 99.12%, testing accuracy of 97.33%, and validation accuracy of 97.33%. Model exploration using VGG16, on the other hand, improved all three: training accuracy—99.87%, testing accuracy—98.77%, and validation accuracy—98.97% average F1-score: 99%. Our research contributes massively to providing the best automatic classification method that will benefit and help farmers shorten their jobs while promoting economic growth in trading effectively across Indonesian regions. Its novelty is in combining image augmentation and CNNs, concerning the VGG16 model, showing better. Convolutional neural network ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Image (mathematics) ; Deep learning ; Contextual image classification ; Algorithm ; Machine learning Coconut Research and Applications ; Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Verena Bitto | Pia Hönscheid | María José Besso | Christian Sperling | Ina Kurth | Michaël Baumann | Benedikt Brors
Source: npj Systems Biology and Applications (article, 2024-05-27)
Field: Chemistry — Spectroscopy

Enhancing mass spectrometry imaging accessibility using convolutional autoencoders for deriving hypoxia-associated peptides from tumors Mass spectrometry imaging (MSI) allows to study cancer's intratumoral heterogeneity through spatially-resolved peptides, metabolites and lipids. Yet, in biomedical research MSI is rarely used for biomarker discovery. Besides its high dimensionality and multicollinearity, mass spectrometry (MS) technologies typically output mass-to-charge ratio values but not the biochemical compounds of interest. Our framework makes particularly low-abundant signals in MSI more accessible. We utilized convolutional autoencoders to aggregate features associated with tumor hypoxia, a parameter with significant spatial heterogeneity, in cancer xenograft models. We highlight that MSI captures these low-abundant signals and that autoencoders can preserve them in their latent space. The relevance of individual hyperparameters is demonstrated through ablation experiments, and the contribution from original features to latent features is unraveled. Complementing MSI with tandem MS from the same tumor model, multiple hypoxia-associated peptide candidates were derived. Compared to random forests alone, our autoencoder approach yielded more biologically relevant insights for biomarker discovery. Biomarker discovery ; Mass spectrometry imaging ; Mass spectrometry ; Hyperparameter ; Autoencoder ; Tandem mass tag ; Computational biology ; Computer science ; Random forest ; Artificial intelligence ; Pattern recognition (psychology) ; Chemistry ; Proteomics ; Biology ; Deep learning ; Quantitative proteomics ; Chromatography ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Jacob Stamm | Sung Hyun Kwon | Shawn Sandhu | Jesse Sandhu | Benjamin G. Levine | Marcos Dantus
Source: Communications Chemistry (article, 2024-05-09)
Field: Chemistry — Spectroscopy

Coherence mapping to identify the intermediates of multi-channel dissociative ionization Identifying the short-lived intermediates and reaction mechanisms of multi-channel radical cation fragmentation processes remains a current and important challenge to understanding and predicting mass spectra. We find that coherent oscillations in the femtosecond time-dependent yields of several product ions following ultrafast strong-field ionization represent spectroscopic signatures that elucidate their mechanism of formation and identify the intermediate(s) they originate from. Experiments on endo-dicyclopentadiene show that vibrational frequencies from various intermediates are mapped onto their resulting products. Aided by ab initio methods, we identify the vibrational modes of both the cleaved and intact molecular ion intermediates. These results confirm stepwise and concerted fragmentation pathways of the dicyclopentadiene ion. This study highlights the power of tracking the femtosecond dynamics of all product ions simultaneously and sheds further light onto one of the fundamental reaction mechanisms in mass spectrometry, the retro-Diels Alder reaction. Fragmentation (computing) ; Chemistry ; Ion ; Femtosecond ; Ionization ; Photochemistry ; Dicyclopentadiene ; Coherence (philosophical gambling strategy) ; Mass spectrometry ; Polyatomic ion ; Reaction intermediate ; Chemical physics ; Computational chemistry ; Laser ; Physics ; Organic chemistry Mass Spectrometry Techniques and Applications ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Samfford S. Cabaluna | Maria Fe P. Bahinting | Leah A. Alindayo
Source: International Journal of Advanced Computer Science and Applications (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Computer-Vision-Based Detection and Monitoring System for Mature Coconut Fruits with a Web Dashboard Visualization Platform The Philippines is the second largest producer of coconut products in the world with 347 million trees planted in 3.6 million hectares of land across the country. Traditionally, harvesting coconuts is a labor-intensive process in the Philippines that involves manual climbing and chopping fruits, which carries a high risk of harm or even death. Hence, the number of expert coconut climbers has decreased as a result. In response, current research has concentrated on creating robot harvesters. However, classifying the mature coconut fruit is a major problem in the harvesting process that calls for a great deal of experience, patience, and work. Studies employing Convolutional Neural Networks (CNNs) have shown great accuracy in detecting coconut ripeness, although these efforts have been limited to detection without practical integration with harvesting equipment. Moreover, the present research lacks a comprehensive solution that allows real-time data display and monitoring, such as the maturation stage of coconuts, via a web-based dashboard. This discrepancy emphasizes the requirement for systems that can not only identify the age of coconuts but also work with harvesting technologies and provide intuitive user interfaces for data display and decision-making. In order to fill these gaps, this study presents a computer-vision-based system that monitors and detects coconut fruit maturity, with an emphasis on mature coconuts, by utilizing the YOLOv8 model. With a Mean Average Precision (mAP50) of 99.5%, mAP50-95 of 89.5%, precision of 99.5%, and recall of 99.9%, the system demonstrated great accuracy. A web-based dashboard is also integrated into the system to provide monitoring and visualization of detected coconut fruits, along with notifications for fully ripe fruits. Computer science ; Dashboard ; Visualization ; Web application ; Computer graphics (images) ; Computer vision ; Human–computer interaction ; World Wide Web ; Artificial intelligence ; Data science Coconut Research and Applications ; Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Mladenka Jurin | Ana Čikoš | Višnja Stepanić | Marcin Górecki | Gennaro Pescitelli | Darko Kontrec | Andreja Jakas | Tonko Dražić | Marin Roje
Source: Pharmaceuticals (article, 2024-09-24)
Field: Chemistry — Organic Chemistry

Synthesis, Absolute Configuration, Biological Profile and Antiproliferative Activity of New 3,5-Disubstituted Hydantoins Hydantoins, a class of five-membered heterocyclic compounds, exhibit diverse biological activities. The aim of this study was to synthesize and characterize a series of novel 3,5-disubstituted hydantoins and to investigate their antiproliferative activity against human cancer cell lines. The new hydantoin derivatives <b>5a</b>-<b>i</b> were prepared as racemic mixtures of <i>syn</i>- and <i>anti</i>-isomers via a base-assisted intramolecular amidolysis of C-3 functionalized β-lactams. The enantiomers of <i>syn</i>-<b>5a</b> and <i>anti</i>-hydantoins <b>5b</b> were separated by preparative high-performance liquid chromatography (HPLC) using <i>n</i>-hexane/2-propanol (90/10, <i>v</i>/<i>v</i>) as the mobile phase. The absolute configuration of the four allyl hydantoin enantiomers <b>5a</b> was assigned based on a comparison of the experimental electronic circular dichroism (ECD) and vibrational circular dichroism (VCD) spectra with those calculated using density functional theory (DFT). The antiproliferative activity evaluated <i>in vitro</i> against three different human cancer cell lines: HepG2 (liver hepatocellular carcinoma), A2780 (ovarian carcinoma), and MCF7 (breast adenocarcinoma), and on the non-tumor cell line HFF1 (normal human foreskin fibroblasts) using the MTT cell proliferation assay. In silico drug-like properties and ADMET profiles were estimated using the ADMET Predictor ver. 9.5 and the online server admetSAR. Eighteen new 3,5-disubstituted hydantoins were synthesized and characterized. The compound <i>anti</i>-<b>5c</b> showed potent cytotoxic activity against the human tumor cell line MCF7 (IC<sub>50</sub> = 4.5 µmol/L) and the non-tumor cell line HFF1 (IC<sub>50</sub> = 12.0 µmol/L). In silico analyzes revealed that the compounds exhibited moderate water solubility and membrane permeability and are likely substrates for CYP3A4 and P-glycoprotein and have a high probability of antiarthritic activity. Absolute configuration ; Absolute (philosophy) ; Computational biology ; Pharmacology ; Stereochemistry ; Chemistry ; Medicine ; Biology Synthesis and biological activity ; Synthesis of heterocyclic compounds ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Chollette C. Olisah | Ben Trewhella | Bo Li | Melvyn Smith | Benjamin Winstone | E. Charles Whitfield | Felicidad Fernández Fernández | Harriet Duncalfe
Source: arXiv (Cornell University) (preprint, 2024-01-09)
Field: Chemistry — Analytical Chemistry

Convolutional Neural Network Ensemble Learning for Hyperspectral Imaging-based Blackberry Fruit Ripeness Detection in Uncontrolled Farm Environment Fruit ripeness estimation models have for decades depended on spectral index features or colour-based features, such as mean, standard deviation, skewness, colour moments, and/or histograms for learning traits of fruit ripeness. Recently, few studies have explored the use of deep learning techniques to extract features from images of fruits with visible ripeness cues. However, the blackberry (Rubus fruticosus) fruit does not show obvious and reliable visible traits of ripeness when mature and therefore poses great difficulty to fruit pickers. The mature blackberry, to the human eye, is black before, during, and post-ripening. To address this engineering application challenge, this paper proposes a novel multi-input convolutional neural network (CNN) ensemble classifier for detecting subtle traits of ripeness in blackberry fruits. The multi-input CNN was created from a pre-trained visual geometry group 16-layer deep convolutional network (VGG16) model trained on the ImageNet dataset. The fully connected layers were optimized for learning traits of ripeness of mature blackberry fruits. The resulting model served as the base for building homogeneous ensemble learners that were ensemble using the stack generalization ensemble (SGE) framework. The input to the network is images acquired with a stereo sensor using visible and near-infrared (VIS-NIR) spectral filters at wavelengths of 700 nm and 770 nm. Through experiments, the proposed model achieved 95.1% accuracy on unseen sets and 90.2% accuracy with in-field conditions. Further experiments reveal that machine sensory is highly and positively correlated to human sensory over blackberry fruit skin texture. Ripeness ; Artificial intelligence ; Convolutional neural network ; Hyperspectral imaging ; Computer science ; Pattern recognition (psychology) ; Ensemble learning ; Deep learning ; Classifier (UML) ; Ripening ; Horticulture Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Horticultural and Viticultural Research
Authors: A. D’Elia | Vincent Polewczyk | Alexander Yu. Petrov | Giovanni Vinai | Liang Li | Chongwen Zou | S.J. Rezvani | A. Marcelli
Source: NPG Asia Materials (article, 2024-11-22)
Field: Materials Science — Polymers and Plastics

Band anisotropy and effective mass renormalization in strained metallic VO2 (101) thin films Abstract We explore how strain impacts the band structure of metallic-phase VO 2 thin films deposited on TiO 2 (101) substrates. Employing a combination of X-ray absorption linear dichroism and valence band measurements, we demonstrate that strain can alter the intrinsic band structure anisotropy of metallic VO 2 . Our findings reveal that reducing the thickness of VO 2 films leads to a more isotropic band structure. This observation is further supported by an analysis of the electronic population redistribution in the $${d}_{{||}}{-}{\pi }^{* }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>d</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>||</mml:mi> </mml:mrow> </mml:msub> <mml:mi>−</mml:mi> <mml:msup> <mml:mrow> <mml:mi>π</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>*</mml:mo> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> bands, which affects the screening length and induces effective mass renormalization. Overall, our results underscore the potential of strain manipulation in tailoring the electronic structure uniformity of thin films, thereby expanding the scope for engineering VO 2 functionalities. Materials science ; Anisotropy ; Condensed matter physics ; Thin film ; Effective mass (spring–mass system) ; Renormalization ; Metal ; Optoelectronics ; Nanotechnology ; Metallurgy ; Optics ; Physics ; Quantum mechanics Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; Ga2O3 and related materials
Authors: Masoud Rahbar Niazi | W.A. Curtin
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-06-04)
Field: Materials Science — Biomaterials

Strengthening of edge prism dislocations in Mg–Zn by cross-core diffusion Abstract The activation of prismatic slip in Mg and its alloys can be beneficial for deformation and forming. Experiments show that addition of Zn and Al solutes have a softening effect at/below room temperature, attributed to solutes facilitating basal-prism-basal cross-slip of prismatic screw dislocations, but a strengthening effect with increasing temperature. Here, the dynamic strain aging mechanism of cross-core diffusion within the prismatic edge dislocation is investigated as a possible mechanism for the strengthening at higher temperatures. First-principles calculations provide the required information on solute/dislocation interaction energies and vacancy-mediated solute migration barriers for Zn solutes around the dislocation core. Results for Mg–0.0045Zn show that cross-core diffusion notably increases the stress for prismatic edge dislocation glide but that the strengthening remains roughly 30% of the experimental strength. Other possible strengthening mechanisms of (i) solute drag of the prism edge dislocation and (ii) solute interactions and/or diffusion within the prismatic screw core, are then briefly discussed with some quantitative assessments pointing toward areas for future study. Materials science ; Prism ; Enhanced Data Rates for GSM Evolution ; Core (optical fiber) ; Diffusion ; Composite material ; Optics ; Thermodynamics Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Metal and Thin Film Mechanics
Authors: Changming Li | Yong Tan | Chunyu Liu | Xun Gao | Zhong Lv | Wenjing Guo
Source: Preprints.org (preprint, 2024-03-15)
Field: Chemistry — Analytical Chemistry

Spectral Fusion Based on Hyperspectral Imaging Technology for Discrimination of Rice Varieties To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties. Hyperspectral imaging ; Fusion ; Artificial intelligence ; Computer science ; Remote sensing ; Geography ; Linguistics Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use
Authors: Gabriel Spartacus | J. Malaplate | Frédéric De Geuser | Isabelle Mouton | Denis Sornin | Raphaëlle Guillou | A. Deschamps
Source: Acta Materialia (article, 2024-08-23)
Field: Materials Science — Materials Chemistry

Precipitation kinetics of ferritic / martensitic oxide dispersion strengthened steels: Influence of the matrix phase transformation Materials science ; Precipitation ; Kinetics ; Phase (matter) ; Oxide ; Metallurgy ; Diffusionless transformation ; Dispersion (optics) ; Transformation (genetics) ; Martensite ; Matrix (chemical analysis) ; Microstructure ; Thermodynamics ; Composite material Fusion materials and technologies ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Tuomas Jalonen | Mohammad Al-Sa’d | Roope Mellanen | Serkan Kıranyaz | Moncef Gabbouj
Source: IEEE Sensors Journal (article, 2024-12-30)
Field: Materials Science — Polymers and Plastics

Real-Time Damage Detection in Fiber Lifting Ropes Using Lightweight Convolutional Neural Networks The health and safety hazards posed by worn crane lifting ropes mandate periodic inspection for damage. This task is time-consuming, prone to human error, halts operation, and may result in the premature disposal of ropes. Therefore, we propose using efficient deep learning and computer vision methods to automate the process of detecting damaged ropes. Specifically, we present a vision-based system for detecting damage in synthetic fiber rope images using lightweight convolutional neural networks (CNNs). We develop a camera-based apparatus to photograph the lifting rope’s surface, while in operation, and capture the progressive wear-and-tear as well as the more significant degradation in the rope’s health state. Experts from Konecranes annotate the collected images in accordance with the rope’s condition; normal or damaged. Then, we preprocess the images, systematically design a deep learning model, evaluate its detection and prediction performance, analyze its computational complexity, and compare it with various other models. Experimental results show the proposed model outperforms other similar techniques with 96.5% accuracy, 94.8% precision, 98.3% recall, 96.5% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}1$ </tex-math></inline-formula>-score, and 99.3% AUC. Besides, they demonstrate the model’s real-time operation, low memory footprint, robustness to various environmental and operational conditions, and adequacy for deployment in industrial applications such as lifting, mooring, towing, climbing, and sailing. Convolutional neural network ; Computer science ; Fiber ; Real-time computing ; Embedded system ; Artificial intelligence ; Materials science ; Composite material Textile materials and evaluations ; Vibration and Dynamic Analysis ; Material Properties and Processing