jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ban Zhao | Xuechun Gao | Xiehua Ouyang | Jiakai Fang | Zihao Deng | Hao Wu | Yonghui Mao
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-09-14)
Field: Chemistry — Spectroscopy

Proteonano™: a novel deep proteomics platform with 1000-plex profiling capacity and picogram sensitivity and its application in diabetic kidney disease ABSTRACT The development of blood-based multi-biomarker panels for screening diabetic patients, and as an easy-to-access tool for identifying individuals at greatest risk of developing diabetic kidney disease (DKD) and its progression, is essential. However, conventional blood biomarker-based methodologies (e.g. clinical tests and ELISA) are unable to predict DKD progression with high sensitivity and specificity. To overcome these challenges, we developed a deep, untargeted plasma proteome profiling technology (Proteonano™ platform) to identify potential multiple protein biomarkers involved in DKD progression. The Proteonano™ technology is an affinity selective mass spectrometric platform that comprises nanoparticle-based affinity binders (nanobinders) for low abundant protein enrichment, automated workflow for parallel sample preparation, and machine learning empowered bioinformatic software for data analysis. Using the Proteonano™ platform, we performed untargeted proteomics on 75 subjects (DKD progressors, n = 30; DKD non-progressors, n = 45) and identified an average of 953 ± 80 (AVG ± SD) protein groups, with a wide dynamic range of 8 orders of magnitude (with the lowest concentration down to 3.00 pg/mL). Among these, 38 proteins were differentially expressed between DKD progressors relative to non-progressors, and the predictive power for these proteins were assessed. Further, we performed random forest and LASSO analyses for additional variable selection. Variables selected by these approaches were assessed by Akaike information criterion method followed by ROC analysis, which identified a combination of multiple proteins (including VWF, PTGDS, B2M, BT3A2, and LCAT) that showed excellent predictive power over current methods, with an area under the curve value up to 0.97. Some of these plasma proteins are not previously recognized in the context of DKD progression, suggesting they are novel biomarkers. Our studies pave the way to develop multi-biomarker panels for DKD progression management. This study suggests that the Proteonano™ technology platform reported here can be employed as an established workflow enabling untargeted deep proteomic analysis to identify highly discriminative biomarkers for precise medicine. Biomarker ; Receiver operating characteristic ; Medicine ; Proteomics ; Biomarker discovery ; Bioinformatics ; Computer science ; Computational biology ; Internal medicine ; Chemistry ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Erythropoietin and Anemia Treatment ; Chronic Kidney Disease and Diabetes
Authors: Walaa Assar
Source: SVU-International Journal of Engineering Sciences and Applications (article, 2023-11-12)
Field: Chemistry — Analytical Chemistry

Retrieval of Chlorophyll-a and vegetation indices Using Sentinel-2 MSI Imagery in El-Burullus Lake, Egypt Chlorophyll-a (Chl-a) is considered an important indicator for assessing many environmental issues. Monitoring Chl-a accurately through remote sensing technology is an efficient approach to observing water quality and providing early warnings of water eutrophication which overcomes the limitations in field campaigns as well as cost constraints. The Sentinel-2 Multi-Spectral Imager (S2-MSI) was used to analyse El-Burullus Lake, which is considered the second-largest natural lake in Egypt. The Case-2 Regional/Coast Colour (C2RCC) atmospheric correction processor was applied to retrieve different optically active water constituents such as Chl-a, the total suspended matter and coloured dissolved organic matter, as well as vegetation indicators. The spatial distribution marked the eutrophicated areas along El-Burullus Lake which result from the inflow from a large number of polluted agricultural drains. The results of the validation between the captured S2-MSI Chl-a and in situ measurements confirmed a clear underestimation in the level of the retrieved C2RCC parameters. The low R2 values mean the bio-optical model better fits the variations in the in situ Chl-a content, as the F-test showed a significant level for all the parameters. All the results, which included low values for the mean bias error (MBE), root mean square error and normalized root mean square error, demonstrated that the empirical model exhibits a good performance with the bio-optical model, and the positive MBE results indicated the underestimation in the in situ Chl-a data. Accordingly, the S2-MSI imageries proved their applicability for the efficient mapping, water quality assessment and sustainable protection of the lake ecosystem Vegetation Index ; Vegetation (pathology) ; Remote sensing ; Environmental science ; Geology ; Oceanography ; Normalized Difference Vegetation Index ; Medicine Spectroscopy and Chemometric Analyses
Authors: Ronald Wbeimar Pacheco Ortiz | Alline V. B. de Oliveira | Fabrício Venâncio | Giuseppe Sampaio Almeida Cardoso | Vinicius Ottonio O. Gonçalves | João Cajaiba | Vinícius Kartnaller
Source: Journal of Food Process Engineering (article, 2023-03-21)
Field: Chemistry — Analytical Chemistry

Novel apparatus for monitoring simultaneously color and °<scp>B</scp>rix of selected fruit juices through <scp>RGB</scp> image analysis Abstract This work introduces a novel apparatus for monitoring both color and °Brix of selected fruit juices. These parameters are essential for quality and processing control at concentration and dilution operations. The proposed apparatus performs the red, green, and blue (RGB) image analysis of the fruit juice as it flows through a transparent cell equipped with a webcam and a light source. The viewing cell is inside a box for controlling temperature and for ensuring homogeneous lighting. This apparatus not only measures the RGB color space of the fruit juice online but also allows relating it to the °Brix. The relationship between RGB and °Brix was obtained by measuring the RGB color space of various fruit juice samples, of which the °Brix were previously determined using a digital refractometer. RGB image analysis showed an excellent relationship with °Brix ( R 2 &gt; 0.99, using an exponential fit), allowing for monitoring reliably this parameter during continuous fruit juice concentration and dilution experiments. These results show the potential of this novel apparatus to substitute conventional instruments such as colorimeters and refractometers for online process monitoring. Furthermore, it has the advantage of not requiring sampling, which reduces measurement errors and saves time. Practical applications This work introduces a novel apparatus based on RGB image analysis for monitoring simultaneously the color and the °Brix of fruit juices. These parameters are important to control the production and quality of fruit juices, especially at concentration and dilution operations. The apparatus proposed here is a viewing cell where the RGB color space is quantified using the software RGBview® as the fruit juice flows through it. The °Brix are determined from the RGB color space using a suitable fit obtained from a previous calibration with juice samples with known °Brix. This is an alternative to conventional analytical instruments such as colorimeters and refractometers but with the advantage of performing an online measurement. This configuration allows for the reduction of analytical errors associated with sampling and saves time. Moreover, this apparatus has the potential to monitor other physicochemical properties related to the color of fruit juices. RGB color model ; Color space ; Brix ; Computer science ; Computer vision ; Artificial intelligence ; RGB color space ; Refractometer ; Mathematics ; Food science ; Image processing ; Chemistry ; Color image ; Image (mathematics) ; Optics ; Sugar ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: CARLOS IGNACIO JR. P. LUGAY | Dyanne Trixzel Suacoco | Mark Levinson Go | Kyle Matthew Espiritu | Marc Neilsen Elamparo
Source:  (article, 2023-03-09)
Field: Materials Science — Polymers and Plastics

Comparative Analysis of the Mechanical Properties of Coconut Fiber Cement Board and Concrete Hollow Blocks In order to determine if the CFB panel is superior to the CHB, this study, "Comparative Study of the Mechanical Properties of Coconut Fiber Cement Board and Concrete Hollow Blocks," compared the two products' prices, dead loads, and compressive strengths. Three samples of each of the two products were used in the experiment, which the researchers conducted. The tensile strength test, parametric test, and Universal Testing Machine (UTM) were used to gather the data for this study. The compressive strength test was performed at the TERMS Concrete and Materials Testing Laboratories Inc. in Tayuman, Manila. The study assumed that: CFBs are less expensive than CHBs; carry less weight than CHBs; and have greater structural strength. Results show that CFB has an average compressive strength of 1,004.167 PSI. In comparison to a CHB, the CFB has more compressive strength. The researchers also concluded that a CFB would carry less weight than a CHB at 5.4721 lb. In both panels' cost analyses, the price of CFB is deemed to be higher than the price of CHB. Fiber Cement Board (CFB) offers a substantially more cost-effective alternative. By enhancing the walling system for residential establishment, this study will benefit the Philippine construction industry. Cement ; Fiber ; Materials science ; Composite material Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Structural Behavior of Reinforced Concrete
Authors: Changchang Wang | Hang Su | Linna Duan | Hao Li
Source: Processes (article, 2023-11-07)
Field: Materials Science — Materials Chemistry

InterMat: A Blockchain-Based Materials Data Discovery and Sharing Infrastructure Material research and development driven by data analysis necessitates a substantial volume of data. However, conventional material data sharing platforms encounter challenges in sharing and integrating data across multiple platforms. This article proposes a blockchain-based materials data discovery and sharing infrastructure—InterMat, which is a material big data management and sharing framework model integrating cloud platforms and blockchain. It could support the full lifecycle of materials data sharing, including data generation, management, discovery, sharing, traceability, and valuation. The architecture of the InterMat, its unique method of constructing a consortium chain, and the protocol for data discovery are presented in this paper. Additionally, the method for materials data identifier and blockchain certification is established, which allows for a unified identifier on the blockchain and cloud-based data addressing from various organizations. InterMat has data discovery algorithms for various materials to achieve the discovery of similar materials data from different nodes. Furthermore, we have designed some blockchain smart contracts for InterMat to encourage data sharing across nodes. These contracts include a proof smart contract that records data sharing activities, ensuring transparency and traceability in the materials data flow. The other contract is a value-estimating contract to encourage high-quality data sharing. Finally, this article introduces the application case of InterMat, using steel materials as an example to demonstrate its applications in data management, data discovery, data valuation, etc. This study successfully addresses various challenges associated with the cross-platform sharing of materials data, such as issues related to data discovery, data rights and control, and willingness to share. InterMat can assist material researchers in discovering and accessing more data, which would create a new ecology for sharing data. Data sharing ; Traceability ; Computer science ; Blockchain ; Data management ; Cloud computing ; Data quality ; Identifier ; Smart contract ; Data science ; Data virtualization ; Data discovery ; Metadata ; Database ; Computer security ; World Wide Web ; Engineering ; Software engineering ; Computer network Machine Learning in Materials Science ; Recycling and Waste Management Techniques ; Blockchain Technology Applications and Security
Authors: Yu S Sidorova | N A Petrov | С Н Зорин | V K Mazo
Source: Problems of Nutrition (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Innovative methods for extracting biolactive сompounds from plant materials () , , . - . . PubMed, Scopus Web of Science Google Scholar. 15 .. , . Computer science ; Environmental science Adsorption, diffusion, and thermodynamic properties of materials
Authors: Zelal Ardic | Ayşe Burcu Aktaş
Source: Journal of Food Science and Technology (article, 2023-04-26)
Field: Chemistry — Organic Chemistry

Enrichment of green table olives by natural anthocyanins during fermentation Fermentation ; Food science ; Brine ; Anthocyanin ; Chemistry ; Factorial experiment ; Mathematics Edible Oils Quality and Analysis ; Antioxidant Activity and Oxidative Stress ; Phytochemicals and Antioxidant Activities
Authors: Xiangyin Tan | Barbara B. Gerbelli | Márcia Carvalho de Abreu Fantini | Cristiano L. P. Oliveira | Heloisa N. Bordallo | Pedro Leonidas Oseliero Filho
Source: Journal of Applied Crystallography (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Retrieving the size distribution of SBA-15 mesopores from small-angle X-ray scattering data using a Monte Carlo method A Monte Carlo (MC) method was introduced into a state-of-the-art model used to analyse small-angle X-ray scattering (SAXS) data of SBA-15, an ordered mesoporous material with many applications. With this new procedure, referred to herein as the SBA-15+MC model, it is possible to retrieve the size distribution of the mesopores, <i>D</i>(<i>r</i>), in a free modelling approach. To achieve this, two main points were addressed: (i) based on previous implementations, the method was adapted to work with long core-shell cylinders; (ii) since the MC model requires longer processing times, strategies to speed up the calculations were developed, which included a simplified version of the original model used to analyse SAXS data of SBA-15 (referred to as the SBA-15 model) as well as the determination of several structural features from the SAXS curve prior to the fit. The new model was validated with simulated data and later used to fit experimental SAXS curves of SBA-15. The obtained results show that the SBA-15 model only works well because the mesopore size distribution of SBA-15 is narrow, whereas the new approach can be successfully used in cases where <i>D</i>(<i>r</i>) is wider and/or has a more complex profile, such as SBA-15 with expanded mesopores. Even though a specific SAXS example was chosen to prove the model, the strategies presented herein are general and suitable for inclusion in other models aimed at the analysis of SBA-15 and similar ordered mesoporous materials. Small-angle X-ray scattering ; Mesoporous material ; Scattering ; Monte Carlo method ; Materials science ; Statistical physics ; Small-angle scattering ; Computer science ; Physics ; Chemistry ; Mathematics ; Optics ; Statistics Mesoporous Materials and Catalysis ; Supercapacitor Materials and Fabrication ; Zeolite Catalysis and Synthesis
Authors: R. N. M. P. Ranaweera | R. M. Kapila Tharanga Rathnayaka | L. L. Gihan Chathuranga
Source: KDU Journal of Multidisciplinary Studies (article, 2023-07-11)
Field: Materials Science — Polymers and Plastics

Optimal Cut Order Planning Solutions using Heuristic and Meta-Heuristic Algorithms: A Systematic Literature Review Cut order planning is a significant task in the apparel industry which determines the fabric spreading layout that can affect different aspects of the apparel industry including the cost of garments, the efficiency of the sewing line, etc. Due to the nature of the cut order problem, it is very hard to determine an optimal solution for the cut order plan although there are ready-made software and also industry experts working on this. Hence, various attempts have been made to optimize it by using machine learning algorithms in the modern world. This review aims at identifying how heuristic and meta-heuristic algorithms are used to optimize the cut-order planning solutions to obtain a near-optimal solution. Furthermore, the lack of research limits it down to 13 papers to be reviewed, and this paper discusses the methodologies and algorithms used, research parameters, issues, and future areas that need to be investigated for the cut order problem. The review shows that the genetic algorithm is widely used to optimize the cut-order plans by adopting the hybridization approaches along with some other meta-heuristic algorithms such as simulated annealing, tabu search, etc. Experimental results indicate that researchers were able to minimize fabric waste by optimizing the cut order plans. Simulated annealing ; Tabu search ; Computer science ; Heuristic ; Algorithm ; Meta heuristic ; Plan (archaeology) ; Order (exchange) ; Mathematical optimization ; Industrial engineering ; Artificial intelligence ; Engineering ; Mathematics Textile materials and evaluations ; Assembly Line Balancing Optimization ; Advanced Manufacturing and Logistics Optimization
Authors: Bart Vandecasteele | Chris Van Waes
Source: Frontiers in Plant Science (article, 2023-08-21)
Field: Chemistry — Analytical Chemistry

Fast screening of total nutrient contents in strawberry leaves and spent growing media using NIRS Use of NIRS on fresh samples would eliminate the need for drying and grinding the samples and would reduce screening time. The ammonium acetate extraction is a reliable alternative to NIRS for fast screening of the total P, K, Ca, and Mg contents in composts, plant fibers and spent growing media. Nutrient ; Horticulture ; Biology ; Agronomy ; Botany ; Ecology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI
Authors: Tianle Wang | Sudip K. Seal | Ramakrishnan Kannan | Cristina García–Cardona | Thomas Proffen | Shantenu Jha
Source:  (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

A Parallel Machine Learning Workflow for Neutron Scattering Data Analysis As part of a larger effort, this work-in-progress reports the possible advantages of modifying conventional workflows used to generate labelled training samples and train machine learning (ML) models on them. We compare results from three different workflows using neutron scattering data analysis as the motivating application and report about 20% improvement in speedup, with no appreciable loss of model accuracy, over a baseline workflow. Workflow ; Speedup ; Computer science ; Neutron scattering ; Artificial intelligence ; Work (physics) ; Machine learning ; Neutron ; Parallel computing ; Database ; Nuclear physics ; Engineering ; Physics X-ray Diffraction in Crystallography ; Nuclear Physics and Applications ; Machine Learning in Materials Science
Authors: Guilherme Corrêa Soares | A. Ruíz | Mikko Hokka
Source: Strain (article, 2023-07-30)
Field: Materials Science — Materials Chemistry

On the use of an induced temperature gradient and full‐field measurements to investigate and model the thermomechanical behaviour of an austenitic stainless steel 316 Abstract A temperature gradient was induced in 316 stable austenitic stainless‐steel tension specimens, and the strain and temperature evolution during tensile deformation was monitored using optical and infrared cameras. The combination of global load with full‐field strain and temperature provided local information on the thermomechanical state of the investigated material. The deformation did not fully concentrate on the hotter portion of the specimen, but instead, the hottest portion strain hardened enough so that the colder portions of the specimen also experienced plastic deformation. Evidently, heat release occurred with plastic deformation and altered the initial temperature gradient as deformation progressed. The Taylor–Quinney coefficient was computed in integral and differential forms, and both are presented as a function of temperature and strain. The Johnson–Cook plasticity model was calibrated through an inverse method procedure in which only five tests were used, and the obtained temperature and strain rate dependencies of the model were comparable to those found in the literature for the same material. A local analysis was done to quantify the impact of adiabatic heating on the mechanical behaviour of the material. Materials science ; Deformation (meteorology) ; Temperature gradient ; Austenite ; Plasticity ; Strain rate ; Composite material ; Tension (geology) ; Austenitic stainless steel ; Ultimate tensile strength ; Mechanics ; Microstructure ; Corrosion High-Velocity Impact and Material Behavior ; Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels
Authors: Shibang MA | Guo Ai-ling
Source: INMATEH Agricultural Engineering (article, 2023-08-31)
Field: Chemistry — Analytical Chemistry

NON-DESTRUCTIVE PREDICTION OF SOLUBLE SOLID CONTENT IN KIWIFRUIT BASED ON VIS/NIR HYPERSPECTRAL IMAGING Soluble solid content (SSC) is a major quality index of kiwifruits. Visible near-infrared (Vis/NIR) hyperspectral imaging with the genetic algorithm (GA) was adopted in this study to realize the non-destructive prediction of kiwifruit SSC. A laboratory Vis/NIR hyperspectral imaging system was established to collect the hyperspectral imaging of 120 kiwifruit samples at a range of 400–1100 nm. The average reflectance spectral data of the region of interest of the kiwifruit hyperspectral imaging were obtained after different preprocessing method, namely, Savitzky–Golay smoothing (SG), multiplicative scatter correction (MSC), and their combination method. The prediction models of partial least squares regression, multiple linear regression, and least squares support vector machine (LS-SVM) were built for determining kiwifruit SSC by using the average reflectance spectral data and effective feature wavelength variables selected by GA, respectively. The results show that SG+MSC is the best preprocessing method. The precisions of the prediction models built using the effective feature wavelength variables selected by GA are higher than that established using full average reflectance spectral data. The GA-LS-SVM prediction model has a best performance with correlation coefficient for prediction (R=0.932) and standard error of prediction (SEP=0.536° Bx) for predicting kiwifruit SSC. The prediction accuracy has been improved by 5.6% compared with that of the prediction models established by using the full-band reflectance spectral data. This study provides an effective method for non-destructive detection of kiwifruit SSC. Hyperspectral imaging ; Partial least squares regression ; Support vector machine ; Smoothing ; Mean squared error ; Mathematics ; Preprocessor ; Feature (linguistics) ; Correlation coefficient ; Artificial intelligence ; Pattern recognition (psychology) ; Remote sensing ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies