jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jianquan Ouyang | Ningqiao Huang | Yunqi Jiang
Source: BMC Bioinformatics (article, 2020-04-25)
Field: Materials Science — Materials Chemistry

A single-model quality assessment method for poor quality protein structure According to results of poor protein structure assessment based on six features, contact prediction and relying on fewer prediction features can improve selection accuracy. Quality (philosophy) ; Computer science ; Computational biology ; DNA microarray ; Quality assessment ; Data mining ; Biology ; Reliability engineering ; Evaluation methods ; Engineering ; Genetics ; Gene Enzyme Structure and Function ; Protein Structure and Dynamics ; Microbial Metabolic Engineering and Bioproduction
Authors: Tess Smidt
Source: ChemRxiv (preprint, 2020-09-10)
Field: Materials Science — Materials Chemistry

Euclidean Symmetry and Equivariance in Machine Learning Understanding symmetry’s role in the physical sciences is critical for choosing an appropriate machine learning method. While invariant models are the most prevalent symmetry-aware models, equivariant models can more faithfully represent physical interactions. Until recently, equivariant models had been absent in the literature due to their technical complexity. Now, after two years of active development, fully-equivariant Euclidean neural net- works are ready to take on challenges across the physical sciences. Equivariant map ; Euclidean geometry ; Symmetry (geometry) ; Invariant (physics) ; Computer science ; Algebra over a field ; Pure mathematics ; Theoretical computer science ; Mathematics ; Artificial intelligence ; Geometry ; Mathematical physics Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Lisa Lancellotti | Alessandro Ulrici | Simona Sighinolfi | Andrea Marchetti
Source: Journal of Food Composition and Analysis (article, 2020-08-20)
Field: Chemistry — Analytical Chemistry

Chemical Characterization Of Commercial Balsamic Vinegar Glaze Chemistry ; Titratable acid ; Raw material ; Food science ; Glaze ; Organic chemistry Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Fault Detection and Control Systems
Authors: Ali Dabaghi | Mohammad Hadi Khoshtaghaza | Mohamad Reza Alizadeh | Hemad Zareiforoush
Source: International Journal of Engineering & Technology (article, 2020-02-07)
Field: Chemistry — Analytical Chemistry

Identification the appearance quality of rice kernels by vision technology and neural network classifier In this study, the appearance quality of Hashemi variety of rice grains was evaluated using image processing and artificial neural network (ANN) classifier. Non-touching kernel images of different classes in a Hashemi rice sample were acquired using a flatbed scanner. Then preprocessing, segmentation, feature extraction and effective feature selection process were done on each objects of image. To categorized grains, various structures of ANN consisting network with one and two hidden layer with different hidden nodes, different training and transfer functions were considered. Results of validation stage showed ANN with 13-18-18-5 topology and LM training and tansig transfer functions had highest mean of classification accuracy (97.33%) and the lowest value of RMSE (0.08361). Itâ€™s concluded that the suggested method uses low cost equipment to identify quality of rice with acceptable accuracy. Results of this research can be used for fast and accurate grading and developing an efficient rice sorting system.Â Â Artificial intelligence ; Artificial neural network ; Computer science ; Identification (biology) ; Classifier (UML) ; Pattern recognition (psychology) ; Computer vision ; Biology ; Botany Spectroscopy and Chemometric Analyses
Authors: Nasser Jalili‐Jahani | Azadeh Fatehi | Ehsan Zeraatkar
Source: RSC Advances (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

PLS and N-PLS based MIA-QSPR modeling of the photodegradation half-lives for polychlorinated biphenyl congeners Multivariate image analysis applied to quantitative structure-property relationships (MIA-QSPR) has been used to predict photodegradation half-lives of polychlorinated biphenyls in <i>n</i>-hexane solution under UV irradiation. Owing to the high cost and laboriousness in experimental tests, developing a simple method to assess the photostability of the compounds is important in environmental risk assessment. The predictor block was built by superposition of the chemical structures (2D images), which was unfolded to a matrix, suitable for multilinear and classical partial least squares, N-PLS and PLS, respectively, as regression methods, demonstrating different predictive capability to each other. Model performance was improved after removing an outlier, and the results were in general more accurate than the ones previously obtained through quantum chemical descriptors analysis. Model validation and <i>Y</i>-randomization test proved that the developed model has goodness-of-fit, predictive power, and robustness. Additionally, the applicability domain of the developed model was visualized by Williams plot. This study showed that a simple procedure is able to give highly predictive models, useful in ecotoxicology, independent of the regression method used for this class of compounds. Quantitative structure–activity relationship ; Polychlorinated biphenyl ; Photodegradation ; Biphenyl ; Environmental chemistry ; Chemistry ; Organic chemistry ; Stereochemistry Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Remote-Sensing Image Classification
Authors: Berit Zeller‐Plumhoff | Anna-Lena Robisch | Daniele Pelliccia | Elena Longo | Hanna Słomińska | Alexander Hermann | Martin Krenkel | Malte Storm | Yuri Estrin | Regine Willumeit‐Römer | Tim Salditt | Dmytro Orlov
Source: Scientific Reports (article, 2020-09-30)
Field: Materials Science — Biomaterials

Nanotomographic evaluation of precipitate structure evolution in a Mg–Zn–Zr alloy during plastic deformation Magnesium and its alloys attract increasingly wide attention in various fields, ranging from transport to medical solutions, due to their outstanding structural and degradation properties. These properties can be tailored through alloying and thermo-mechanical processing, which is often complex and multi-step, thus requiring in-depth analysis. In this work, we demonstrate the capability of synchrotron-based nanotomographic X-ray imaging methods, namely holotomography and transmission X-ray microscopy, for the quantitative 3D analysis of the evolution of intermetallic precipitate (particle) morphology and distribution in magnesium alloy Mg-5.78Zn-0.44Zr subjected to a complex multi-step processing. A rich history of variation of the intermetallic particle structure in the processed alloy provided a testbed for challenging the analytical capabilities of the imaging modalities studied. The main features of the evolving precipitate structure revealed earlier by traditional light and electron microscopy methods were confirmed by the 3D techniques of synchrotron-based X-ray imaging. We further demonstrated that synchrotron-based X-ray imaging enabled uncovering finer details of the variation of particle morphology and number density at various stages of processing-above and beyond the information provided by visible light and electron microscopy. Intermetallic ; Synchrotron ; Materials science ; Alloy ; Transmission electron microscopy ; Synchrotron radiation ; Microscopy ; Particle (ecology) ; Deformation (meteorology) ; Metallurgy ; Nanotechnology ; Optics ; Composite material ; Physics Magnesium Alloys: Properties and Applications ; Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties
Authors: C. Johan van der Westhuizen | Divan G. van Greunen | Werner Cordier | Margo Nell | Vanessa Steenkamp | André Stander | Jenny‐Lee Panayides | Darren L. Riley
Source: ARKIVOC (article, 2020-10-17)
Field: Chemistry — Organic Chemistry

Binding pose analysis of hydroxyethylamine based β-secretase inhibitors and application thereof to the design and synthesis of novel indeno[1,2-b]indole based inhibitors -Secretase (BACE1) is recognised as a target for the treatment of Alzheimer's disease, and transition-state isosteres such as hydroxyethylamines have shown promise when incorporated into BACE1 inhibitors. A computational investigation of previously reported carbazole-based hydroxylethylamines with contradictory binding poses was undertaken using molecular dynamic simulations to rationalise the ligands preferred binding preference. Visual inspection of the confirmed binding pocket showed unoccupied space surrounding the carbazole moiety which was probed through the synthesis of seventeen ligands wherein the carbazole ring system was replaced with an indeno[1,2-b]indole ring system. The most active compound, rac-1-[benzyl(methyl)amino]-3-(indeno[1,2-b]indol-5(10H)-yl)propan-2-ol, indicated an inhibition of 91% at 10 M against -secretase with a cytotoxicity IC 50 value of 10.51 1.11 M against the SH-SY5Y cell line. Chemistry ; Indole test ; Combinatorial chemistry ; Stereochemistry Carbohydrate Chemistry and Synthesis ; Chemical Synthesis and Analysis ; Microbial Natural Products and Biosynthesis
Authors: Martin Reinhardt | Helmut Grubmüller
Source: Physical review. E (article, 2020-10-13)
Field: Materials Science — Materials Chemistry

Variationally derived intermediates for correlated free-energy estimates between intermediate states Free-energy difference calculations based on atomistic simulations generally improve in accuracy when sampling from a sequence of intermediate equilibrium thermodynamic states that bridge the configuration space between two states of interest. For reasons of efficiency, usually the same samples are used to calculate the stepwise difference of such an intermediate to both adjacent intermediates. However, this procedure violates the assumption of uncorrelated estimates that is necessary to derive both the optimal sequence of intermediate states and the widely used Bennett acceptance ratio estimator. In this work, via a variational approach, we derive the sequence of intermediate states and the corresponding estimator with minimal mean-squared error that account for these correlations and assess its accuracy. Estimator ; Sequence (biology) ; Work (physics) ; Statistical physics ; Uncorrelated ; Mathematics ; Configuration space ; Space (punctuation) ; Energy (signal processing) ; Sampling (signal processing) ; Physics ; Statistics ; Thermodynamics ; Chemistry ; Quantum mechanics ; Computer science Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics ; Advanced Chemical Physics Studies
Authors: Mubarak Almutairi
Source: IEEE Access (review, 2020-12-22)
Field: Chemistry — Analytical Chemistry

A Comprehensive Review of Soft Computing Models for Permeability Prediction Crude oil is a vital and valuable commodity in the energy industry. In order to maintain continuous, stable, and reasonably priced supplies, oil producers need cheaper exploration and extraction techniques. Permeability is one of the formation parameters that is a key interest to petroleum engineers in determining the economic worth and yield of crude deposits, yet permeability prediction remains a difficult problem. Many approaches have been applied to solving this important issue. Soft computing has been deployed to predict permeability. In this paper, we present an extensive review of the existing research that has been conducted on applications of soft computing for permeability prediction. This paper finds out that traditional approaches for permeability prediction are still relevant in the oil and gas industry. Soft computing methods in particular are worthy of addition to this interesting area. This extensive review is intended to be an entry point for further exploration of other approaches that have received little or no attention from researchers. Soft computing ; Computer science ; Petroleum industry ; Permeability (electromagnetism) ; Petroleum ; Crude oil ; Petroleum engineering ; Fossil fuel ; Commodity ; Artificial intelligence ; Environmental science ; Geology ; Engineering ; Business ; Artificial neural network Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Ahmed Ashraf Soliman | Attia Attia | Muhamed Abdelwahab
Source: Journal of Petroleum and Mining Engineering (article, 2020-09-01)
Field: Chemistry — Analytical Chemistry

Development of new models for predicting crude oil bubble point pressure, oil formation volume factor, and solution gas-oil ratio using genetic algorithm Bubble point pressure (Pb), oil formation volume factor (BO), and solution gas-oil ratio (Rs) are considered the key parameters required to describe and characterize the crude oil. Accurate determination for crude oil properties are necessary for multi-operation in reservoir evaluation, such as reserve estimation, enhanced oil recovery, oil reservoir performance prediction, designing pipelines and production equipment, and reservoir simulation. Traditional techniques used to calculate PVT data are usually expensive or unavailable, so there are a huge number of empirical correlations developed to estimate PVT properties as a function of production data. But when we used these correlations to predict crude oil properties, big errors are attained. The main target of this study is to find a better and accurate approach for predicting the properties of crude oil. This paper developed new empirical correlations for predicting the properties of reservoir oil as a function of PVT properties such as (P, T, Bo, Rs) using genetic algorithm technique. The simulation model is built using MATLAB software which contains the optimization tool that includes a genetic algorithm tool in it. To validate these correlations, 130 data sets of different crude oils were used. The results obtained showed that the developed empirical correlations from the genetic algorithm model appeared excellent accuracy of predicting crude oil properties compared to their relevant published correlations. The average absolute error for all correlations that the genetic algorithm applied to them is decreased. This technique can be applied to predict crude oil properties with a high level of accuracy. Gas oil ratio ; Bubble point ; Bubble ; Petroleum engineering ; Volume (thermodynamics) ; Crude oil ; Gas bubble ; Genetic algorithm ; Environmental science ; Chemistry ; Thermodynamics ; Materials science ; Mechanics ; Mathematics ; Engineering ; Mathematical optimization ; Physics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Yusuf Sıyambaş | Yakup Turgut
Source: Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji (article, 2020-06-21)
Field: Materials Science — Materials Chemistry

HSLA Çeliğinin Delinmesinde Kesme Parametrelerinin Çap Değişimi ve Çapak Yüksekliği Üzerindeki Etkilerinin Gri İlişkisel Analiz Yöntemiyle Araştırılması Yüksek dayanımlı düşük alaşımlı çelikler (HSLA) yüksek mukavemet, hafiflik ve işlenebilirlik gibi mühendislik özelliklerinden dolayı otomotiv sanayisinde tercih edilmektedir. Yapılan deneysel çalışmada, TiAlN kaplamalı ve kaplamasız HSS matkaplar ile üç adet kesme hızı (18, 26, 34 m/dak) ve üç adet ilerleme hızı (0,05, 0,075, 0,1 mm/dev) değeri kullanılarak kesme parametrelerinin çap değişimi ve çapak yüksekliği üzerindeki etkileri araştırılmıştır. Delik delme işlemleri ıslak şartlarda ve boydan boya gerçekleştirilmiştir. Deneyler Taguchi L18 ortogonal deney tasarımına göre yapılmıştır. Elde edilen deney sonuçları gri ilişkisel analiz yöntemi yardımıyla optimize edilmiştir. İşlem üzerindeki en etkili parametrenin belirlenmesi için anova metodu kullanılmıştır. Gri ilişki derecesi değerine göre, çap değişimi ve çapak yüksekliği değerlerinde, en uygun parametrelerin kaplamasız matkaplar, 0,05 mm/dev ilerleme hızı ve 34 m/dak kesme hızı olduğu tespit edilmiştir. Mathematics ; Physics ; Animal science ; Biology Metal Alloys Wear and Properties ; Advanced machining processes and optimization ; Advanced Machining and Optimization Techniques
Authors: William E. Fondrie | William Stafford Noble
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-12-02)
Field: Chemistry — Spectroscopy

mokapot: Fast and flexible semi-supervised learning for peptide detection Abstract Proteomics studies rely on the accurate assignment of peptides to the acquired tandem mass spectra—a task where machine learning algorithms have proven invaluable. We describe mokapot, which provides a flexible semi-supervised learning algorithm that allows for highly customized analyses. We demonstrate some of the unique features of mokapot by improving the detection of RNA-cross-linked peptides from an analysis of RNA-binding proteins and increasing the consistency of peptide detection in a single-cell proteomics study. Proteomics ; Computer science ; Task (project management) ; Consistency (knowledge bases) ; Peptide ; Artificial intelligence ; Machine learning ; Computational biology ; Chemistry ; Biology ; Engineering ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Ayman Ibrahim | Nazeer El-Bialee | Mohsen M. Saad | Elio Romano
Source: International Journal on Advanced Science Engineering and Information Technology (article, 2020-12-21)
Field: Chemistry — Analytical Chemistry

Non-Destructive Quality Inspection of Potato Tubers Using Automated Vision System In this investigation, an automated vision system "AVS" for non-destructive quality inspection of potato tubers "PT" was developed. Color, size, mass, firmness, and the texture homogeneity of the "PT" surface, various sensitive features were studied, and extracted from the digital image by using the R program. Otsu threshold method, RGB, Lu*v*, CIE LChuv color models, and texture analysis by using the package Gray-Level Co-Occurrence Matrices (GLCMs) were applied. The results showed a great correlation between the tuber pixel area percentages (DIM=dimension as a percentage of total pixels), and both mass and geometric mean diameter (GMD) of all "PT" varieties. The color results demonstrated that the hue angle (huv) ranged from 68.92 to 96.61°, and the "PT" color was classified into deep and light color intensity. The "AVS" could predict the mass and size, and gave statistical data at the mass production level, in terms of the inspecting samples No., mass, and grades based on size, color, and free from injuries through the texture homogeneity of tuber surface. A predictive model hypothesized based on the tuber's surface texture characteristics for predicting the tubers firmness was statistically significant. This "AVS" can be applied as a non-destructive, precise, and symmetric technique in-line inspection, the quality of "PT", also helping decision-makers in the agricultural field and stakeholders to improve the horticulture sector through the statistical data issued by this system. Quality (philosophy) ; Computer vision ; Machine vision ; Artificial intelligence ; Agricultural engineering ; Computer science ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Majid Abdolrazzagh-Nezhad | Shokooh Pour Mahyabadi | Ali Ebrahimpoor
Source: Data Science Journal of Computing and Applied Informatics (article, 2020-02-01)
Field: Chemistry — Analytical Chemistry

Improving KNN by Gases Brownian Motion Optimization Algorithm to Breast Cancer Detection In the last decade, the application of information technology and artificial intelligence algorithms are widely developed in collecting information of cancer patients and detecting them based on proposing various detection algorithms. The K-Nearest-Neighbor classification algorithm (KNN) is one of the most popular of detection algorithms, which has two challenges in determining the value of k and the volume of computations proportional to the size of the data and sample selected for training. In this paper, the Gaussian Brownian Motion Optimization (GBMO) algorithm is utilized for improving the KNN performance to breast cancer detection. To achieve to this aim, each gas molecule contains the information such as a selected subset of features to apply the KNN and k value. The GBMO has lower time-complexity order than other algorithms and has also been observed to perform better than other optimization algorithms in other applications. The algorithm and three well-known meta-heuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) have been implemented on five benchmark functions and compared the obtained results. The GBMO+KNN performed on three benchmark datasets of breast cancer from UCI and the obtained results are compared with other existing cancer detection algorithms. These comparisons show significantly improves this classification accuracy with the proposed detection algorithm. Algorithm ; Benchmark (surveying) ; Computer science ; Particle swarm optimization ; Artificial intelligence ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Gene expression and cancer classification ; AI in cancer detection
Authors: Patrick J. Willems | Ursula Fels | An Staes | Kris Gevaert | Petra Van Damme
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-05-24)
Field: Chemistry — Spectroscopy

The use of hybrid data-dependent and -independent acquisition spectral libraries empower dual-proteome profiling ABSTRACT In the context of bacterial infections, it is imperative that physiological responses can be studied in an integrated manner, meaning a simultaneous analysis of both the host and the pathogen responses. To improve the sensitivity of detection, data-independent acquisition (DIA) based proteomics was found to outperform data-dependent acquisition (DDA) workflows in identifying and quantifying low abundant proteins. Here, by making use of representative bacterial pathogen/host proteome samples, we report an optimized hybrid library generation workflow for data-independent acquisition mass spectrometry relying on the use of data-dependent and in silico predicted spectral libraries. When compared to searching DDA experiment-specific libraries only, the use of hybrid libraries significantly improved peptide detection to an extent suggesting that infection relevant host-pathogen conditions could be profiled in sufficient depth without the need of a priori bacterial pathogen enrichment when studying the bacterial proteome. GRAPHICAL ABSTRACT Proteome ; Workflow ; Computational biology ; Profiling (computer programming) ; In silico ; Pathogen ; Context (archaeology) ; Proteomics ; Computer science ; Biology ; Bioinformatics ; Microbiology ; Genetics Advanced Proteomics Techniques and Applications ; Biosensors and Analytical Detection ; Bacterial Identification and Susceptibility Testing