jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhitong Li | Qing Gu
Source: Nanophotonics (review, 2024-02-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Topological hyperbolic metamaterials Hyperbolic metamaterial (HMM) is a unique type of anisotropic material that can exhibit metal and dielectric properties at the same time. This unique characteristic results in it having unbounded isofrequency surface contours, leading to exotic phenomena such as spontaneous emission enhancement and applications such as super-resolution imaging. However, at optical frequencies, HMM must be artificially engineered and always requires a metal constituent, whose intrinsic loss significantly limits the experimentally accessible wave vector values, thus negatively impacting the performance of these applications. The need to reduce loss in HMM stimulated the development of the second-generation HMM, termed active HMM, where gain materials are utilized to compensate for metal's intrinsic loss. With the advent of topological photonics that allows robust light transportation immune to disorders and defects, research on HMM also entered the topological regime. Tremendous efforts have been dedicated to exploring the topological transition from elliptical to hyperbolic dispersion and topologically protected edge states in HMM, which also prompted the invention of lossless HMM formed by all-dielectric material. Furthermore, emerging twistronics can also provide a route to manipulate topological transitions in HMMs. In this review, we survey recent progress in topological effects in HMMs and provide prospects on possible future research directions. Metamaterial ; Topology (electrical circuits) ; Physics ; Theoretical physics ; Mathematics ; Optics ; Combinatorics Metamaterials and Metasurfaces Applications ; Opportunistic and Delay-Tolerant Networks ; Chaos-based Image/Signal Encryption
Authors: Patrizia Vizza | Federica Aracri | Pietro Hiram Guzzi | Marco Gaspari | Pierangelo Veltri | Giuseppe Tradigo
Source: BMC Medical Informatics and Decision Making (article, 2024-04-08)
Field: Chemistry — Spectroscopy

Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case Proteomic-based analysis is used to identify biomarkers in blood samples and tissues. Data produced by devices such as mass spectrometry requires platforms to identify and quantify proteins (or peptides). Clinical information can be related to mass spectrometry data to identify diseases at an early stage. Machine learning techniques can be used to support physicians and biologists in studying and classifying pathologies. We present the application of machine learning techniques to define a pipeline aimed at studying and classifying proteomics data enriched using clinical information. The pipeline allows users to relate established blood biomarkers with clinical parameters and proteomics data. The proposed pipeline entails three main phases: (i) feature selection, (ii) models training, and (iii) models ensembling. We report the experience of applying such a pipeline to prostate-related diseases. Models have been trained on several biological datasets. We report experimental results about two datasets that result from the integration of clinical and mass spectrometry-based data in the contexts of serum and urine analysis. The pipeline receives input data from blood analytes, tissue samples, proteomic analysis, and urine biomarkers. It then trains different models for feature selection, classification and voting. The presented pipeline has been applied on two datasets obtained in a 2 years research project which aimed to extract hidden information from mass spectrometry, serum, and urine samples from hundreds of patients. We report results on analyzing prostate datasets serum with 143 samples, including 79 PCa and 84 BPH patients, and an urine dataset with 121 samples, including 67 PCa and 54 BPH patients. As results pipeline allowed to identify interesting peptides in the two datasets, 6 for the first one and 2 for the second one. The best model for both serum (AUC=0.87, Accuracy=0.83, F1=0.81, Sensitivity=0.84, Specificity=0.81) and urine (AUC=0.88, Accuracy=0.83, F1=0.83, Sensitivity=0.85, Specificity=0.80) datasets showed good predictive performances. We made the pipeline code available on GitHub and we are confident that it will be successfully adopted in similar clinical setups. Pipeline (software) ; Computer science ; Feature selection ; Proteomics ; Prostate cancer ; Artificial intelligence ; Machine learning ; Data mining ; Medicine ; Cancer ; Internal medicine ; Chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Claudia Ctortecka | Natalie M. Clark | Brian Boyle | Anjali Seth | D.R. Mani | Namrata D. Udeshi | Steven A. Carr
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-01-22)
Field: Chemistry — Spectroscopy

Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications Mass spectrometry (MS)-based single-cell proteomics (SCP) has gained massive attention as a viable complement to other single cell approaches. The rapid technological and computational advances in the field have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell at reasonable proteome depth to characterize biological phenomena remains a challenge. To address some of those limitations we present a combination of fully automated single cell sample preparation utilizing a dedicated chip within the picolitre dispensing robot, the cellenONE. The proteoCHIP EVO 96 can be directly interfaced with the Evosep One chromatographic system for in-line desalting and highly reproducible separation with a throughput of 80 samples per day. This, in combination with the Bruker timsTOF MS instruments, demonstrates double the identifications without manual sample handling. Moreover, relative to standard high-performance liquid chromatography, the Evosep One separation provides further 2-fold improvement in protein identifications. The implementation of the newest generation timsTOF Ultra with our proteoCHIP EVO 96-based sample preparation workflow reproducibly identifies up to 4,000 proteins per single HEK-293T without a carrier or match-between runs. Our current SCP depth spans over 4 orders of magnitude and identifies over 50 biologically relevant ubiquitin ligases. We complement our highly reproducible single-cell proteomics workflow to profile hundreds of lipopolysaccharide (LPS)-perturbed THP-1 cells and identified key regulatory proteins involved in interleukin and interferon signaling. This study demonstrates that the proteoCHIP EVO 96-based SCP sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications. Proteome ; Proteomics ; Workflow ; Throughput ; Quantitative proteomics ; Computational biology ; Mass spectrometry ; Sample preparation ; Sample (material) ; Computer science ; Chemistry ; Single-cell analysis ; Chromatography ; Cell ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Single-cell and spatial transcriptomics
Authors: Yalu Liu | Huan Liu | Dehua Luo | Jie Wang | Chao Deng | Mingjun Zhang | Chengyong Li | Dan Song | Faqi Li
Source: Ultrasonics Sonochemistry (article, 2024-05-23)
Field: Materials Science — Materials Chemistry

Cavitation erosion on different metallic materials under high hydrostatic pressure evaluated with the spatially confined sonoluminescence Cavitation erosion is a general phenomenon in the fields of aviation, navigation, hydraulic machinery, and so on, causing great damage to fluid machinery. With the vast requirements in deep ocean applications, it is urgent to study the mechanism of cavitation erosion and the cavitation erosion resistance of different materials under high hydrostatic pressure to predict and avoid the effect of cavitation erosion. In this work, the spatially confined cavitation bubble cloud associated with Gaussian-like intensity distribution sonoluminescence (SL) was produced by a spherically focused ultrasound transducer with two opening ends near metallic plates under different hydrostatic pressures (0.1, 3, 6, and 10 MPa). The cavitation erosion effects on copper, 17-4PH stainless steel and tungsten plates were studied. Through coupling analysis towards the SL intensity distribution, the macro/micro morphology of cavitation erosion, and the physical parameters of different metallic materials (hardness, yield strength, and melting point), it is found that with increasing hydrostatic pressure, the erosion effect is intensified, the depth of cavitation pits increases, the phenomenon of melting can be observed on materials with relatively low melting points, and the cavitation erosion experienced an evolution process from high-temperature creep to fracture. This work has also established a method for the evaluation of materials' cavitation erosion resistance with measurable SL intensity distribution, which is promising to promote the designing and selection of anti-cavitation materials in deep-sea applications. Sonoluminescence ; Cavitation ; Cavitation erosion ; Hydrostatic pressure ; High pressure ; Materials science ; Metal ; Erosion ; Hydrostatic equilibrium ; Composite material ; Mechanics ; Metallurgy ; Geology ; Physics Ultrasound and Cavitation Phenomena ; Cavitation Phenomena in Pumps ; Coal Combustion and Slurry Processing
Authors: Nidhi Dalal | R. Ofano | Luigi Ruggiero | Antonio Giandonato Caporale | Paola Adamo
Source: Current Research in Food Science (review, 2024-01-01)
Field: Chemistry — Analytical Chemistry

What the fish? Tracing the geographical origin of fish using NIR spectroscopy Food authentication is a growing concern with rising complexities of the food supply network, with fish being an easy target of food fraud. In this regard, NIR spectroscopy has been used as an efficient tool for food authentication. This article reviews the latest research advances on NIR based fish authentication. The process from sampling/sample preparation to data analysis has been covered. Special attention was given to NIR spectra pre-processing and its unsupervised and supervised analysis. Sampling is an important aspect of traceability study and samples chosen ought to be a true representative of the population. NIR spectra acquired is often laden with overlapping bands, scattering and highly multicollinear. It needs adequate pre-processing to remove all undesirable features. The pre-processing technique can make or break a model and thus need a trial-and-error approach to find the best fit. As for spectral analysis and modelling, multicollinear nature of NIR spectra demands unsupervised analysis (PCA) to compact the features before application of supervised multivariate techniques such as LDA, PLS-DA, QDA etc. Machine learning approach of modelling has shown promising result in food authentication modelling and negates the need for unsupervised analysis before modelling. Computer science ; Authentication (law) ; Tracing ; Artificial intelligence ; Sample (material) ; Population ; Pattern recognition (psychology) ; Traceability ; Sampling (signal processing) ; Data mining ; Machine learning ; Filter (signal processing) ; Chemistry Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Meat and Animal Product Quality
Authors: Mashiul Huq | Astrid Jasi | Philippe Poure | Slaviša Jovanović | I. Revol | B. Lenoir
Source: Renewable and Sustainable Energy Reviews (article, 2024-02-27)
Field: Materials Science — Materials Chemistry

Critical analysis of optimized energy harvesting at small-scale by thermally coupled photovoltaic-thermoelectric systems Photovoltaic system ; Thermoelectric generator ; Maximum power point tracking ; Thermoelectric effect ; Materials science ; Photovoltaics ; Rooftop photovoltaic power station ; Engineering physics ; Electrical engineering ; Computer science ; Automotive engineering ; Engineering ; Physics ; Voltage Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Innovative Energy Harvesting Technologies
Authors: Manuela Mancini | Veli‐Matti Taavitsainen | Åsmund Rinnan
Source: Waste Management (article, 2024-03-01)
Field: Chemistry — Analytical Chemistry

Comparison of classification methods performance for defining the best reuse of waste wood material using NIR spectroscopy Recycling of post-consumer waste wood material is becoming an increasingly appealing alternative to disposal. However, its huge heterogeneity is calling for an assessment of the material characteristics in order to define the best recycling option and intended reuse. In fact, waste wood comes into a variety of uses/types of wood, along with several levels of contamination, and it can be divided into different categories based on its composition and quality grade. This study provides the measurement of more than a hundred waste wood samples and their characterisation using a hand-held NIR spectrophotometer. Three classification methods, i.e. K-nearest Neighbours (KNN), Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA) and PCA-KNN, have been compared to develop models for the sorting of waste wood in quality categories according to the best-suited reuse. In addition, the classification performance has been investigated as a function of the number of the spectral measurements of the sample and as the average of the spectral measurements. The results showed that PCA-KNN performs better than the other classification methods, especially when the material is ground to 5 cm of particle size and the spectral measurements are averaged across replicates (classification accuracy: 90.9 %). NIR spectroscopy, coupled with chemometrics, turned out to be a promising tool for the real-time sorting of waste wood material, ensuring a more accurate and sustainable waste wood management. Obtaining real-time information about the quality and characteristics of waste wood material translates into a decision of the best recycling option, increasing its recycling potential. Reuse ; Waste management ; Environmental science ; Spectroscopy ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Wood Treatment and Properties
Authors: Rui Zhu | Xiaohong Wu | Bin Wu | Jiaxing Gao
Source: Current Research in Food Science (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

High-accuracy classification and origin traceability of peanut kernels based on near-infrared (NIR) spectroscopy using Adaboost - Maximum uncertainty linear discriminant analysis Peanut kernels, known for their high nutritional value and palatability, are classified as nut food. In this study, peanut kernel samples from six distinct cities in Shandong Province, China, were examined to categorize and trace their origins. Near-infrared (NIR) spectra of samples were captured using a portable NIR-M-R2 spectrometer. After the application of Savitzky-Golay (SG) filtering, the classification was attempted using principal component analysis (PCA) plus linear discrimination analysis (LDA). Additionally, maximum uncertainty linear discriminant analysis (MLDA) was applied for comparison. A specific number of eigenvectors could respectively maximize the classification accuracies, 81.48% for PCA + LDA and 76.54% for MLDA. In order to further improve the classification accuracies, Adaboost-MLDA was proposed to develop a stronger classifier. This method, after 18 iterations, achieved remarkable effects, achieving a high accuracy of 95.06%. In a similar vein, the enhancement with preprocessing techniques multiplicative scatter correction (MSC) + SG and standard normal variate (SNV) + SG raised accuracies to 98.77% and 97.53%, respectively. The results of classifying first-order and second-order derivative spectra using Adaboost-MLDA were also described, achieving accuracies near 100%. The experiment demonstrates that integrating Adaboost with NIR spectroscopy offers a highly accurate method for peanut kernel classification, promising for practical applications in food quality control. Linear discriminant analysis ; AdaBoost ; Pattern recognition (psychology) ; Traceability ; Near-infrared spectroscopy ; Artificial intelligence ; Mathematics ; Optimal discriminant analysis ; Spectroscopy ; Discriminant ; Analytical Chemistry (journal) ; Computer science ; Statistics ; Chromatography ; Chemistry ; Support vector machine ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Charles A Bowesman | Qianwei Qu | L. K. McKemmish | S. N. Yurchenko | Jonathan Tennyson
Source: Monthly Notices of the Royal Astronomical Society (article, 2024-02-19)
Field: Materials Science — Materials Chemistry

ExoMol line lists – LV: hyperfine-resolved molecular line list for vanadium monoxide (51V16O) ABSTRACT The new hyperfine-resolved HyVO line lists for vanadium monoxide (51V16O) are presented. The new line list considers 15 electronic states and comprises almost 59 billion transitions between 3.4 million energy levels, up to 45 000 cm−1. Empirical energy levels obtained through the Marvel procedure are used to update calculated values to experimental accuracy, making the line list well suited for high-resolution experiments. Machine learning is utilized to refine the energies of levels in important bands, where empirical values are not known. These updates allow for the accurate prediction of line positions in regions of astrophysical interest, particularly in strong rovibronic bands in the infrared and visible. Previously published ab initio transition dipole moment curves are adjusted based on experimental lifetime measurements, resulting in increased intensities in key regions. The line list covers levels up to F = 150 and is complete up to 5400 K, making it well suited for studies of ultra-hot Jupiter and M-type stellar atmospheres where VO is believed to be a strong source of opacities. The full line list is made available via www.exomol.com. Physics ; Hyperfine structure ; Vanadium ; Line (geometry) ; Monoxide ; Astrophysics ; Carbon monoxide ; Atomic physics ; Inorganic chemistry ; Geometry Porphyrin and Phthalocyanine Chemistry ; Catalysis and Oxidation Reactions ; Vanadium and Halogenation Chemistry
Authors: Eman Abdelhakeem | Asmaa Ashraf Nemr | Hassan M. Rashed | Adli A. Selim | Basma M. Essa | Doaa Hegazy
Source: Journal of Drug Delivery Science and Technology (article, 2024-11-29)
Field: Materials Science — Biomaterials

Revitalizing Itraconazole: Unleashing its Anticancer Potential through Oral Nanosystems for Liver Targeting and Biodistribution Profiling in an Animal Model using Radiolabeling Technique Hepatic cancer presents a global health challenge requiring innovative therapeutic approaches. In this study, we explore the need for novel drug delivery systems in hepatocellular carcinoma (HCC) treatment, focusing on targeted itraconazole delivery via bilosomes. Itraconazole, an antifungal with promising anticancer activity, necessitates systems enhancing efficacy and selectivity. In this study itraconazole-loaded bilosomes were optimized as a targeted drug delivery system. Entrapment efficiency, particle size, polydispersity index, and zeta potential were measured. By employing statistical analysis and numerical optimization, the optimized itraconazole-loaded bilosomes formulation (OIB) was identified. OIB was evaluated through in-vitro drug release kinetics, and morphological examination. OIB's targeting capability was investigated through radiolabeling biodistribution technique, and its anticancer activity is examined against HCC using the HepG cell line. OIB showed an entrapment efficiency of 77.5 % ± 0.1, a particle size of 366.2 nm ± 6.6, acceptable PDI, and a zeta potential of 57.3 mV ± 0.9. The optimized formulation exhibited sustained release with 61.9 % ± 0.9 % itraconazole released after 24 h, following the Higuchi diffusion model. Morphological examination revealed spherical morphology. Biodistribution studies using radiolabeled itraconazole confirmed targeted accumulation in hepatic cells, highlighting OIB's potential as a selective therapeutic agent. OIB demonstrated potent anticancer activity against HCC in the HepG cell line, validating its efficacy as a targeted treatment. Itraconazole-loaded bilosomes offer hope in combating hepatocellular carcinoma. Besides, 131 I-itraconazole loaded bilosomes can combine the radiotherapeutic effect of 131 I with the hopeful anticancer effect of itraconazole. This study supports bilosomes as a promising strategy against this deadly disease. • Itraconazole is a broad-spectrum FDA-approved triazole antifungal drug with reported anticancer activity. • Optimized Itraconazole bilosomes demonstrated a high EE, a suitable PS, and an excellent ZP. • Radiolabeling and biodistribution studies confirmed the accumulation of optimized Itraconazole bilosomes in hepatic cells. • Optimized Itraconazole bilosomes showed 53-fold enhancement in the potency of Itraconazole in HepG2 cell line. Biodistribution ; Itraconazole ; Pharmacology ; Animal model ; Nanotechnology ; Medicine ; Chemistry ; Materials science ; Internal medicine ; Antifungal ; Biochemistry ; In vitro Nanoparticle-Based Drug Delivery ; Drug Transport and Resistance Mechanisms ; Drug Solubulity and Delivery Systems
Authors: Wang Guang | Hongzhao Fan | Jiawang Li | Zhigang Li | Yanguang Zhou
Source: Nature Communications (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

Direct observation of tunable thermal conductance at solid/porous crystalline solid interfaces induced by water adsorbates Improving interfacial thermal transport is crucial for heat dissipation in devices with interfaces, such as electronics, buildings, and solar panels. Here, we design a strategy by utilizing the water adsorption-desorption process in porous metal-organic frameworks (MOFs) to tune the interfacial heat transfer, which could benefit their potential in cooling or heat dissipation applications. We observe a changeable thermal conductance across the solid/porous MOF interfaces owing to the dense water channel formed by the adsorbed water molecules in MOFs. Our experimental and/or modeling results show that the interfacial thermal conductance of Au/Cu<sub>3</sub>(BTC)<sub>2</sub>, Au/Zr<sub>6</sub>O<sub>4</sub>(OH)<sub>4</sub>(BDC)<sub>6</sub> and Au/MOF-505 heterointerfaces is increased up to 7.1, 1.7 and 3.1 folds by this strategy, respectively, where Cu<sub>3</sub>(BTC)<sub>2</sub> is referred to as HKUST-1 and Zr<sub>6</sub>O<sub>4</sub>(OH)<sub>4</sub>(BDC)<sub>6</sub> is referred to as UiO-66. Our molecular dynamics simulations further show that the surface tension of Au layer will cause the adsorbed water molecules in MOFs to gather at the interfacial region. The dense water channel formed at the interfacial region can activate the high-frequency lattice vibrations and act as an additional thermal pathway, and then enhance heat transfer across the interfaces significantly. Our findings revealed the underlying mechanisms for tailoring thermal transport at the solid/porous MOF heterointerfaces by water adsorbates, which could motivate and benefit the new cooling system design based on MOFs. Materials science ; Adsorption ; Chemical engineering ; Porosity ; Thermal ; Heat transfer ; Thermal conductivity ; Chemical physics ; Porous medium ; Conductance ; Molecule ; Nanotechnology ; Thermodynamics ; Composite material ; Physical chemistry ; Chemistry ; Organic chemistry Thermal properties of materials ; Phase Change Materials Research ; Composite Material Mechanics