jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Meftah Salem M. Alfatni | Siti Khairunniza Bejo | Mohammad Hamiruce Marhaban | Osama M. Ben Saaed | Aouache Mustapha | Abdul Rashid Mohamed Shariff
Source: Agriculture (article, 2022-09-14)
Field: Chemistry — Analytical Chemistry

Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis Remote sensing sensors-based image processing techniques have been widely applied in non-destructive quality inspection systems of agricultural crops. Image processing and analysis were performed with computer vision and external grading systems by general and standard steps, such as image acquisition, pre-processing and segmentation, extraction and classification of image characteristics. This paper describes the design and implementation of a real-time fresh fruit bunch (FFB) maturity classification system for palm oil based on unrestricted remote sensing (CCD camera sensor) and image processing techniques using five multivariate techniques (statistics, histograms, Gabor wavelets, GLCM and BGLAM) to extract fruit image characteristics and incorporate information on palm oil species classification FFB and maturity testing. To optimize the proposed solution in terms of performance reporting and processing time, supervised classifiers, such as support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN), were performed and evaluated via ROC and AUC measurements. The experimental results showed that the FFB classification system of non-destructive palm oil maturation in real time provided a significant result. Although the SVM classifier is generally a robust classifier, ANN has better performance due to the natural noise of the data. The highest precision was obtained on the basis of the ANN and BGLAM algorithms applied to the texture of the fruit. In particular, the robust image processing algorithm based on BGLAM feature extraction technology and the ANN classifier largely provided a high AUC test accuracy of over 93% and an image-processing time of 0,44 (s) for the detection of FFB palm oil species. Artificial intelligence ; Support vector machine ; Image processing ; Pattern recognition (psychology) ; Computer science ; Feature extraction ; Classifier (UML) ; Artificial neural network ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Manuel Matzinger | Adrian Vasiu | Mathias Madalinski | Fränze Müller | Florian Stanek | Karl Mechtler
Source: Nature Communications (article, 2022-07-08)
Field: Chemistry — Spectroscopy

Mimicked synthetic ribosomal protein complex for benchmarking crosslinking mass spectrometry workflows Cross-linking mass spectrometry has matured to a frequently used tool for the investigation of protein structures as well as interactome studies up to a system-wide level. The growing community generated a broad spectrum of applications, linker types, acquisition strategies and specialized data analysis tools, which makes it challenging to decide for an appropriate analysis workflow. Here, we report a large and flexible synthetic peptide library as reliable instrument to benchmark crosslink workflows. Additionally, we provide a tool, IMP-X-FDR, that calculates the real, experimentally validated, FDR, compares results across search engine platforms and analyses crosslink properties in an automated manner. We apply the library with 6 commonly used linker reagents and analyse the data with 6 established search engines. We thereby show that the correct algorithm and search setting choice is highly important to improve identification rate and reliability. We reach identification rates of up to ~70 % of the theoretical maximum (i.e. 700 unique lysine-lysine cross-links) while maintaining a real false-discovery-rate of <3 % at cross-link level with high reproducibility, representatively showing that our test system delivers valuable and statistically solid results. Benchmarking ; Mass spectrometry ; Workflow ; Computational biology ; Ribosomal RNA ; Ribosomal protein ; Proteomics ; Chemistry ; Computer science ; Ribosome ; Biochemistry ; Biology ; Chromatography ; RNA ; Database ; Business Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: José Luis P. Calle | Marta Barea-Sepúlveda | Ana Ruiz-Rodríguez | José A. Álvarez | Marta Ferreiro‐González | Miguel Palma
Source: Sensors (article, 2022-05-19)
Field: Chemistry — Analytical Chemistry

Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data Fruit juice production is one of the most important sectors in the beverage industry, and its adulteration by adding cheaper juices is very common. This study presents a methodology based on the combination of machine learning models and near-infrared spectroscopy for the detection and quantification of juice-to-juice adulteration. We evaluated 100% squeezed apple, pineapple, and orange juices, which were adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The spectroscopic data have been combined with different machine learning tools to develop predictive models for the control of the juice quality. The use of non-supervised techniques, specifically model-based clustering, revealed a grouping trend of the samples depending on the type of juice. The use of supervised techniques such as random forest and linear discriminant analysis models has allowed for the detection of the adulterated samples with an accuracy of 98% in the test set. In addition, a Boruta algorithm was applied which selected 89 variables as significant for adulterant quantification, and support vector regression achieved a regression coefficient of 0.989 and a root mean squared error of 1.683 in the test set. These results show the suitability of the machine learning tools combined with spectroscopic data as a screening method for the quality control of fruit juices. In addition, a prototype application has been developed to share the models with other users and facilitate the detection and quantification of adulteration in juices. Adulterant ; Orange juice ; Artificial intelligence ; Machine learning ; Linear discriminant analysis ; Support vector machine ; Mathematics ; Test set ; Partial least squares regression ; Pattern recognition (psychology) ; Computer science ; Food science ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Jin Liu | Shanshan Qiu | Zhenbo Wei
Source: Chemosensors (article, 2022-09-20)
Field: Chemistry — Analytical Chemistry

Real-Time Measurement of Moisture Content of Paddy Rice Based on Microstrip Microwave Sensor Assisted by Machine Learning Strategies Moisture content is extremely imoprtant to the processes of storage, packaging, and transportation of grains. In this study, a portable moisture measuring device was developed based on microwave microstrip sensors. The device is composed of three parts: a microwave circuit module, a real-time measurement module, and software to display the results. This work proposes an improvement measure by optimizing the thickness of paddy rice samples (8–13 cm) and adding the ambient temperatures and the moisture contents (13.66–27.02% w.b.) at a 3.00 GHz frequency. A random forest, decision tree, k-nearest neighbor, and support vector machine were applied to predict the moisture content in the paddy rice. Microwave characteristics, phase shift, and temperature compensation were selected as the input variables to the prediction models, which have achieved high accuracy. Among those prediction models, the random forest model yielded the best performance with highest accuracy and stability (R2 = 0.99, RMSE = 0.28, MAE = 0.26). The device showed a relatively stable performance (the maximum average absolute error was 0.55%, the minimum absolute error was 0.17%, the mean standard deviation was 0.18%, the maximum standard deviation was 0.41%, and the minimum standard deviation was 0.08%) within the moisture content range of 13–30%. The instrument has the advantages of real-time, simple structure, convenient operation, low cost, and portability. This work is expected to provide an important reference for the real-time in situ measurement of agricultural products, and to be of great significance for the development of intelligent agricultural equipment. Water content ; Standard deviation ; Mean squared error ; Microstrip ; Microwave ; Software portability ; Moisture ; Approximation error ; Computer science ; Environmental science ; Simulation ; Materials science ; Soil science ; Mathematics ; Statistics ; Algorithm ; Electronic engineering ; Composite material ; Engineering ; Telecommunications Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Laura Fancello | Thomas Bürger
Source: Genome biology (article, 2022-06-20)
Field: Chemistry — Spectroscopy

An analysis of proteogenomics and how and when transcriptome-informed reduction of protein databases can enhance eukaryotic proteomics In summary, using transcriptome information is an interesting strategy that has not been promoted for the right reasons. While the increase in peptide identifications from searching reduced transcriptome-informed databases is an artifact caused by the use of an FDR control method unsuitable to excessively small databases, transcriptome information can reduce the ambiguity of protein identifications. Proteogenomics ; Transcriptome ; Proteomics ; Biology ; Identification (biology) ; Computational biology ; Robustness (evolution) ; Protein isoform ; Database ; Computer science ; Genetics ; Gene ; Gene expression Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Mass Spectrometry Techniques and Applications
Authors: K. S. ARAVIND | Ananta Vashisth | P. Krishanan | Bappa Das
Source: Journal of Agrometeorology (article, 2022-01-06)
Field: Chemistry — Analytical Chemistry

Wheat yield prediction based on weather parameters using multiple linear, neural network and penalised regression models Wheat yield production is largely attributed by weather parameters. Model developed by multiple linear, neural network and penalised regression techniques using weather data have the potential to provide reliable, timely and cost-effective prediction of wheat yield. Wheat yield data and weather parameter during crop growing period (46th to 15th SMW) for more than 30 years were collected for study area and model was developed using stepwise multiple linear regression (SMLR), principal component analysis (PCA) in combination with SMLR, artificial neural network (ANN) alone and in combination with PCA, least absolute shrinkage and selection operator (LASSO) and elastic net (ENET) techniques. Analysis was carried out by fixing 70% of the data for calibration and remaining dataset for validation. On examining these models, LASSO and elastic net are performing excellent having nRMSE value less than 10 % for four out of five location and good for one location, because of prevention in over fitting and reducing regression coefficient by penalization. Lasso (programming language) ; Linear regression ; Principal component analysis ; Artificial neural network ; Elastic net regularization ; Regression analysis ; Regression ; Principal component regression ; Predictive modelling ; Calibration ; Statistics ; Linear model ; Mathematics ; Yield (engineering) ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Genetics and Plant Breeding ; Greenhouse Technology and Climate Control
Authors: Sheng Cheng | Yang Jiao | Yi Ren
Source: Acta Materialia (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

Data-driven learning of 3-point correlation functions as microstructure representations Generative grammar ; Representation (politics) ; Microstructure ; Computer science ; Completeness (order theory) ; Set (abstract data type) ; Point (geometry) ; Bayesian probability ; Generative model ; Correlation ; Function (biology) ; Variety (cybernetics) ; Artificial intelligence ; Theoretical computer science ; Algorithm ; Materials science ; Mathematics ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Composite Material Mechanics ; Block Copolymer Self-Assembly
Authors: David Reishofer | Roland Resel | Jürgen Sattelkow | Wolfgang Johann Fischer | Katrin Niegelhell | Tamilselvan Mohan | Karin Stana Kleinschek | Heinz Amenitsch | Harald Plank | Tekla Tammelin | Eero Kontturi | Stefan Spirk
Source: Biomacromolecules (article, 2022-02-28)
Field: Materials Science — Biomaterials

Humidity Response of Cellulose Thin Films Cellulose-water interactions are crucial to understand biological processes as well as to develop tailor made cellulose-based products. However, the main challenge to study these interactions is the diversity of natural cellulose fibers and alterations in their supramolecular structure. Here, we study the humidity response of different, well-defined, ultrathin cellulose films as a function of industrially relevant treatments using different techniques. As treatments, drying at elevated temperature, swelling, and swelling followed by drying at elevated temperatures were chosen. The cellulose films were prepared by spin coating a soluble cellulose derivative, trimethylsilyl cellulose, onto solid substrates followed by conversion to cellulose by HCl vapor. For the highest investigated humidity levels (97%), the layer thickness increased by ca. 40% corresponding to the incorporation of 3.6 molecules of water per anhydroglucose unit (AGU), independent of the cellulose source used. The aforementioned treatments affected this ratio significantly with drying being the most notable procedure (2.0 and 2.6 molecules per AGU). The alterations were investigated in real time with X-ray reflectivity and quartz crystal microbalance with dissipation, equipped with a humidity module to obtain information about changes in the thickness, roughness, and electron density of the films and qualitatively confirmed using grazing incidence small angle X-ray scattering measurements using synchrotron irradiation. Cellulose ; Swelling ; Chemical engineering ; Humidity ; Materials science ; Evaporation ; Relative humidity ; Hydroxypropyl cellulose ; Polymer chemistry ; Chemistry ; Organic chemistry ; Composite material ; Polymer Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; Aerogels and thermal insulation
Authors: Nóra Igaz | Péter Bélteky | Dávid Kovács | Csaba Papp | Andrea Rónavári | Diána Szabó | Attila Gácser | Zoltán Kónya | Mónika Kiricsi
Source: International Journal of Nanomedicine (article, 2022-07-01)
Field: Materials Science — Biomaterials

Functionalized Mesoporous Silica Nanoparticles for Drug-Delivery to Multidrug-Resistant Cancer Cells The efficient delivery of Rho123 and MMC to multidrug-resistant cells via MSNs, the amplified and presumably prolonged intracellular drug concentration, and the consequently enhanced cytotoxic effects envision the enormous potential of MSNs to defeat multidrug-resistant cancer. RHOB ; Multiple drug resistance ; Drug delivery ; Mesoporous silica ; Nanocarriers ; Materials science ; Pharmacology ; Cancer research ; Drug resistance ; Chemistry ; Nanotechnology ; Medicine ; Microbiology ; Biochemistry ; Mesoporous material ; Biology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Drug Transport and Resistance Mechanisms
Authors: Bingcai Luo | Yubao Zhang | Ying Chen | Jianqiang Huo
Source: Materials Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Photocatalytic conversion of arylboronic acids to phenols by a new 2D donor–acceptor covalent organic framework 3D perspective view of BTT-BTDDA-COF and illustration of arylboronic acid hydroxylation under blue light. Covalent bond ; Photocatalysis ; Acceptor ; Phenols ; Chemistry ; Photochemistry ; Organic chemistry ; Catalysis Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Luminescence and Fluorescent Materials
Authors: Shammya Afroze | Md Sumon Reza | Md. Ruhul Amin | Juntakan Taweekun | Абул Калам Азад
Source: International Journal of Hydrogen Energy (review, 2022-12-21)
Field: Materials Science — Materials Chemistry

Progress in nanomaterials fabrication and their prospects in artificial intelligence towards solid oxide fuel cells: A review Anode ; Materials science ; Oxide ; Cathode ; Nanomaterials ; Durability ; Supercapacitor ; Nanotechnology ; Solid oxide fuel cell ; Electrochemical energy conversion ; Operating temperature ; Energy storage ; Process engineering ; Energy transformation ; Electrode ; Electrochemistry ; Electrical engineering ; Engineering ; Chemistry ; Composite material ; Metallurgy ; Power (physics) Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Gas Sensing Nanomaterials and Sensors
Authors: Peter Cats | Ranisha S. Sitlapersad | Wouter K. den Otter | Anthony R. Thornton | René van Roij
Source: Utrecht University Repository (Utrecht University) (article, 2022-03-01)
Field: Chemistry — Electrochemistry

We present a study of the structure and differential capacitance of electric double layers of aqueous electrolytes. We consider electric double layer capacitors (EDLC) composed of spherical cations and anions in a dielectric continuum confined between a planar cathode and anode. The model system includes steric as well as Coulombic ion-ion and ion-electrode interactions. We compare results of computationally expensive, but “exact” , Brownian Dynamics (BD) simulations with approximate, but cheap, calculations based on classical Density Functional Theory (DFT). Excellent overall agreement is found for a large set of system parameters, including variations in concentration, ionic size- and valency-asymmetries, applied voltages and electrode separation, provided the differences between the canonical ensemble of the BD simulations and the grand-canonical ensemble of DFT are properly taken into account. In particular, a careful distinction is made between the differential capacitance CN at fixed number of ions and Cμ at fixed ionic chemical potential. Furthermore, we derive and exploit their thermodynamic relations. In the future these relations will also be useful for comparing and contrasting experimental data with theories for supercapactitors and other systems. The quantitative agreement between simulation and theory indicates that the presented DFT is capable of accounting accurately for coupled Coulombic and packing effects. Hence it is a promising candidate to cheaply study room temperature ionic liquids at much lower dielectric constants than that of water. Density functional theory ; Chemistry ; Ion ; Capacitance ; Ionic bonding ; Molecular dynamics ; Statistical physics ; Thermodynamics ; Physics ; Electrode ; Computational chemistry ; Physical chemistry Electrochemical Analysis and Applications ; Electrostatics and Colloid Interactions ; Membrane-based Ion Separation Techniques
Authors: Subhajit Ghosh | Fariborz Kargar | Nicholas Sesing | Zahra Barani | Tina T. Salguero | Yan Dong | Sergey Rumyantsev | Alexander A. Balandin
Source: Advanced Electronic Materials (article, 2022-11-20)
Field: Materials Science — Materials Chemistry

Low‐Frequency Current Fluctuations in Quasi‐1D (TaSe<sub>4</sub>)<sub>2</sub>I Weyl Semimetal Nanoribbons Abstract Low‐frequency current fluctuations, i.e., electronic noise, in quasi‐1D (TaSe 4 ) 2 I Weyl semimetal nanoribbons are discussed. It is found that the noise spectral density is of the 1/ f type and scales with the square of the current, S I ~ I 2 ( f is the frequency). The noise spectral density increases by almost an order of magnitude and develops Lorentzian features near the temperature T ≈ 225 K. These spectral changes are attributed to the charge‐density‐wave phase transition even though the temperature of the noise maximum deviates from the reported Peierls transition temperature in bulk (TaSe 4 ) 2 I crystals. The noise level, normalized by the channel area, in these Weyl semimetal nanoribbons is surprisingly low, ≈10 −9 µm 2 Hz −1 at f = 10 Hz, when measured below and above the Peierls transition temperature. The obtained results shed light on the specifics of electron transport in quasi‐1D topological Weyl semimetals and can be important for their proposed applications as downscaled interconnects. Weyl semimetal ; Condensed matter physics ; Semimetal ; Charge density wave ; Noise (video) ; Physics ; Current density ; Phase transition ; Materials science ; Band gap ; Quantum mechanics Graphene research and applications ; Topological Materials and Phenomena ; 2D Materials and Applications
Authors: Vahid Sheikhhassani | Barbara Scalvini | J. Ng | Laurens W.H.J. Heling | Yosri Ayache | Tom M. J. Evers | Eva Estébanez‐Perpiñá | Iain J. McEwan | Alireza Mashaghi
Source: Protein Science (article, 2022-05-26)
Field: Chemistry — Spectroscopy

Topological dynamics of an intrinsically disordered N‐terminal domain of the human androgen receptor Human androgen receptor contains a large N-terminal domain (AR-NTD) that is highly dynamic and this poses a major challenge for experimental and computational analysis to decipher its conformation. Misfolding of the AR-NTD is implicated in prostate cancer and Kennedy's disease, yet our knowledge of its structure is limited to primary sequence information of the chain and a few functionally important secondary structure motifs. Here, we employed an innovative combination of molecular dynamics simulations and circuit topology (CT) analysis to identify the tertiary structure of AR-NTD. We found that the AR-NTD adopts highly dynamic loopy conformations with two identifiable regions with distinct topological make-up and dynamics. This consists of a N-terminal region (NR, residues 1-224) and a C-terminal region (CR, residues 225-538), which carries a dense core. Topological mapping of the dynamics reveals a traceable time-scale dependent topological evolution. NR adopts different positioning with respect to the CR and forms a cleft that can partly enclose the hormone-bound ligand-binding domain (LBD) of the androgen receptor. Furthermore, our data suggest a model in which dynamic NR and CR compete for binding to the DNA-binding domain of the receptor, thereby regulating the accessibility of its DNA-binding site. Our approach allowed for the identification of a previously unknown regulatory binding site within the CR core, revealing the structural mechanisms of action of AR inhibitor EPI-001, and paving the way for other drug discovery applications. Androgen receptor ; Topology (electrical circuits) ; Computational biology ; Binding site ; DNA-binding domain ; Molecular dynamics ; Chemistry ; Protein structure ; Biology ; Biophysics ; Genetics ; Biochemistry ; Gene ; Prostate cancer ; Transcription factor ; Computational chemistry ; Mathematics Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Estrogen and related hormone effects