jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mehmet Umut Caglar | Adam J. Hockenberry | Claus O. Wilke
Source: PLoS ONE (article, 2018-11-02)
Field: Chemistry — Spectroscopy

Predicting bacterial growth conditions from mRNA and protein abundances Cells respond to changing nutrient availability and external stresses by altering the expression of individual genes. Condition-specific gene expression patterns may thus provide a promising and low-cost route to quantifying the presence of various small molecules, toxins, or species-interactions in natural environments. However, whether gene expression signatures alone can predict individual environmental growth conditions remains an open question. Here, we used machine learning to predict 16 closely-related growth conditions using 155 datasets of E. coli transcript and protein abundances. We show that models are able to discriminate between different environmental features with a relatively high degree of accuracy. We observed a small but significant increase in model accuracy by combining transcriptome and proteome-level data, and we show that measurements from stationary phase cells typically provide less useful information for discriminating between conditions as compared to exponentially growing populations. Nevertheless, with sufficient training data, gene expression measurements from a single species are capable of distinguishing between environmental conditions that are separated by a single environmental variable. Transcriptome ; Gene expression ; Proteome ; Biology ; Gene ; Computational biology ; Exponential growth ; Gene expression profiling ; Regulation of gene expression ; Abundance (ecology) ; Biological system ; Bioinformatics ; Genetics ; Ecology ; Physics Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Bacterial Genetics and Biotechnology
Authors: Naren Vikram Raj Masna | Fengchao Zhang | Cheng Chen | Soumyajit Mandal | Swarup Bhunia
Source: International Journal of Food Science & Technology (article, 2018-07-24)
Field: Chemistry — Analytical Chemistry

Authentication of dietary supplements through Nuclear Quadrupole Resonance (<scp>NQR</scp>) spectroscopy Summary As the industry grows, adulteration of many products by mislabelling, re‐branding and false advertising is becoming prevalent practice. Existing solutions for analysis often require extensive sample preparation or are limited in terms of detecting different types of integrity issues. We describe a novel authentication method based on Nuclear Quadrupole Resonance ( NQR ) spectroscopy which is quantitative, non‐invasive and non‐destructive. It is sensitive to small deviation in the solid‐state chemical structure of a product, which changes the NQR signal properties. These characteristics are unique for different manufacturers, resulting in manufacturer‐specific watermarks. We show that nominally identical dietary supplements from different manufacturers can be accurately classified based on features from NQR spectra. Specifically, we use a machine learning‐based classification called support vector machines ( SVM s) to verify the authenticity of products under test. This approach has been evaluated on three products using semi‐custom hardware and shows promising results, with typical classification accuracy of over 95%. Nuclear quadrupole resonance ; Authentication (law) ; Support vector machine ; Computer science ; Product (mathematics) ; Sample (material) ; Spectroscopy ; Pattern recognition (psychology) ; Quadrupole ; Artificial intelligence ; Biological system ; Data mining ; Nuclear magnetic resonance ; Chemistry ; Mathematics ; Physics ; Computer security ; Biology ; Chromatography Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Metabolomics and Mass Spectrometry Studies
Authors: Hakim Sadou | Tarik Hacib | Yann Le Bihan | Olivier Meyer | Hulusi Açikgöz
Source: Physica B Condensed Matter (article, 2018-09-07)
Field: Chemistry — Analytical Chemistry

Predictors Generation by Partial Least Square Regression for microwave characterization of dielectric materials Admittance parameters ; Inverse ; Dielectric ; Microwave ; Partial least squares regression ; Curse of dimensionality ; Regression analysis ; Mathematics ; Coaxial ; Inversion (geology) ; Linear regression ; Computational physics ; Mathematical analysis ; Materials science ; Physics ; Statistics ; Computer science ; Telecommunications ; Geometry ; Optoelectronics ; Quantum mechanics ; Geology Spectroscopy and Chemometric Analyses ; Microwave and Dielectric Measurement Techniques ; Ultrasonics and Acoustic Wave Propagation
Authors: Neil L. Allan | Thomas Lee | Judy N. Hart | Colin L. Freeman | Chris E. Mohn
Source: Physics and Chemistry of Minerals (article, 2018-08-28)
Field: Materials Science — Biomaterials

Calcite–magnesite solid solutions: using genetic algorithms to understand non-ideality Calcite ; Magnesite ; Chemistry ; Solid solution ; Dolomite ; Supercell ; Mixing (physics) ; Maxima and minima ; Crystallography ; Mineralogy ; Thermodynamics ; Physics Calcium Carbonate Crystallization and Inhibition ; Spectroscopy and Quantum Chemical Studies ; High-pressure geophysics and materials
Authors: Mehmet Acı | Çiğdem İnan Acı | Mutlu Avcı
Source: Turkish Journal of Engineering (article, 2018-05-12)
Field: Materials Science — Materials Chemistry

PERFORMANCE COMPARISON OF ANFIS, ANN, SVR, CART AND MLR TECHNIQUES FOR GEOMETRY OPTIMIZATION OF CARBON NANOTUBES USING CASTEP Density Functional Theory (DFT) calculations used in the Carbon Nanotubes (CNT) design take a very long time even in the simulation environment as it is well known in literature. In this study, calculation time of DFT for geometry optimization of CNT is reduced from days to minutes using seven artificial intelligence-based and one statistical-based methods and the results are compared. The best results are achieved from ANFIS and ANN based models and these models can be used instead of CNT simulation software with high accuracy. CASTEP ; Adaptive neuro fuzzy inference system ; Carbon nanotube ; MATLAB ; Density functional theory ; Computer science ; Materials science ; Software ; Geometry ; Nanotechnology ; Artificial intelligence ; Mathematics ; Computational chemistry ; Chemistry Carbon Nanotubes in Composites ; Chemical and Physical Properties of Materials ; Graphene research and applications
Authors: Ciro Aprea | Adriana Greco | Angelo Maiorino | Claudia Masselli
Source: International Journal of Heat and Technology (article, 2018-09-30)
Field: Materials Science — Materials Chemistry

A comparison between different materials with mechanocaloric effect Caloric refrigeration can be a viable alternative to the traditional vapour compression technology since a caloric solid refrigerant has zero vapour pressure and therefore is ecological with no direct Ozone Depletion Potential and zero direct Global Warming Potential. Caloric refrigeration embraces four main cooling techniques, each one based on a different caloric effect. This paper is focused upon materials that display mechanocaloric properties, with a transition induced through the application of variable pressure (barocaloric cooling) or uniaxial stress (elastocaloric cooling). Materials that display solidstate caloric effects driven by applied pressure/stress could lead to more accessible and economic technological solutions. By means of a two-dimensional mathematical model an energy analysis is performed with the most performing elastocaloric and barocaloric materials to explore the potential of mechanocaloric cooling. Temperature span, cooling power and coefficient of performance have bene evaluated. Results demonstrate best mechanocaloric materials, like Cu68.13Zn15.74Al16.14 and (NH4)2MoO2F4, provide energy performances better than those of traditional vapour compression plants. Materials science ; Composite material Shape Memory Alloy Transformations
Authors: Linda Grabill | Robert Berger
Source: Scientific Reports (article, 2018-07-06)
Field: Materials Science — Surfaces, Coatings and Films

Calibrating the Extended Hückel Method to Quantitatively Screen the Electronic Properties of Materials The extended Hückel (eH) tight-binding method has historically been prized for its computational ease and intuitive chemical clarity. However, its lack of quantitative predictiveness has prevented the eH method from being used as a tool for rapidly screening materials for desired electronic properties. In this work, we demonstrate that when eH input parameters are calibrated using density functional theory (DFT) calculations of carefully chosen sets of simple crystals, the eH parameters retain most of their quantitative accuracy when transferred to more complex, structurally related phases. Using solar-energy-relevant semiconductors and insulators in the Sr-Ti-O family as a case study, we show that calibrated eH parameters can match the features of DFT band structures within about two tenths of an eV, at a tiny fraction of the computational cost of DFT. Computer science ; Extended Hückel method ; Hückel method ; Information retrieval ; Physics ; Quantum mechanics ; Molecule Electron and X-Ray Spectroscopy Techniques ; Surface and Thin Film Phenomena ; Electronic and Structural Properties of Oxides
Authors: Nathan O. Hodas | Panos Stinis
Source: Frontiers in Psychology (preprint, 2018-07-12)
Field: Materials Science — Materials Chemistry

Doing the Impossible: Why Neural Networks Can Be Trained at All As deep neural networks grow in size, from thousands to millions to billions of weights, the performance of those networks becomes limited by our ability to accurately train them. A common naive question arises: if we have a system with billions of degrees of freedom, don't we also need billions of samples to train it? Of course, the success of deep learning indicates that reliable models can be learned with reasonable amounts of data. Similar questions arise in protein folding, spin glasses and biological neural networks. With effectively infinite potential folding/spin/wiring configurations, how does the system find the precise arrangement that leads to useful and robust results? Simple sampling of the possible configurations until an optimal one is reached is not a viable option even if one waited for the age of the universe. On the contrary, there appears to be a mechanism in the above phenomena that forces them to achieve configurations that live on a low-dimensional manifold, avoiding the curse of dimensionality. In the current work we use the concept of mutual information between successive layers of a deep neural network to elucidate this mechanism and suggest possible ways of exploiting it to accelerate training. We show that adding structure to the neural network that enforces higher mutual information between layers speeds training and leads to more accurate results. High mutual information between layers implies that the effective number of free parameters is exponentially smaller than the raw number of tunable weights. Curse of dimensionality ; Artificial neural network ; Computer science ; Folding (DSP implementation) ; Mechanism (biology) ; Artificial intelligence ; Deep neural networks ; Deep learning ; Physics ; Engineering Machine Learning in Materials Science ; Neural Networks and Applications ; Computational Physics and Python Applications
Authors: Florent Langenfeld | A. Axenopoulos | Anargyros Chatzitofis | Daniela Crăciun | Petros Daras | Bowen Du | Andrea Giachetti | Yu‐Kun Lai | Haisheng Li | Yingbin Li | Majid Masoumi | Yuxu Peng | Paul L. Rosin | Jeremy Sirugue | Li Sun | Spyridon Thermos | Matthew Toews | Yang Wei | Yujuan Wu | Yujia Zhai | Tianyu Zhao | Yanping Zheng | Matthieu Montès
Source: Eurographics (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Protein Shape Retrieval Proteins are macromolecules central to biological processes that display a dynamic and complex surface. They display multiple conformations differing by local (residue side-chain) or global (loop or domain) structural changes which can impact drastically their global and local shape. Since the structure of proteins is linked to their function and the disruption of their interactions can lead to a disease state, it is of major importance to characterize their shape. In the present work, we report the performance in enrichment of six shape-retrieval methods (3D-FusionNet, GSGW, HAPT, DEM, SIWKS and WKS) on a 2 267 protein structures dataset generated for this protein shape retrieval track of SHREC'18. Computer science ; Protein structure ; Protein function ; Biological system ; Domain (mathematical analysis) ; Function (biology) ; Computational biology ; Chemistry ; Biology ; Mathematics ; Cell biology ; Biochemistry Enzyme Structure and Function ; Image Processing and 3D Reconstruction ; Protein Structure and Dynamics
Authors: Norazlin Abdullah | Nazmi Mat Nawi | Phebe Ding | Mohd Shahir Kasim | Siti Saripa Rabiah Mat Lazim
Source: Food Research (article, 2018-11-06)
Field: Chemistry — Analytical Chemistry

Preliminary study to predict moisture content of jackfruit skin using shortwave near infrared spectroscopy Moisture content of a jackfruit is one of the main attributes used by farmers to determine the maturity level of the fruit. The objective of this preliminary research was to explore the potential application of low-cost shortwave near infrared (VSWNIR) spectroscopy to nondestructively predict moisture content of jackfruit from their outer skin. A total of 870 skin portions collected from twenty-nine jackfruit samples were used in this study. After the spectral measurement, the skin portions were dried in the oven in order to measure their moisture content (%, wet basis, w.b.). Partial least square (PLS) method was used to develop both calibration and prediction models for calibrating the spectral data with the moisture content. This study found that the value of coefficient of determination (R 2 ) and root means square error of calibration (RMSEC) were 0.65 and 2.17, respectively. For the prediction model, the value of R 2 and root mean square error of prediction (RMSEP) were 0.64 and 2.81, respectively. These results indicated the VSWNIR spectrometer is a promising technology for non-destructively predicting moisture content of jackfruits. Shortwave ; Water content ; Near-infrared spectroscopy ; Moisture ; Spectroscopy ; Environmental science ; Materials science ; Physics ; Optics ; Geology ; Composite material ; Radiative transfer Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Dimitar Nikolov | Diana Tsankova
Source: Food Science and Applied Biotechnology (article, 2018-10-10)
Field: Chemistry — Analytical Chemistry

Features Extraction for Pollen Recognition Using Gabor Filters The aim of the article is to investigate the features extraction from microscope images of pollens for a classification of honey on the base of its botanical origin. A filter-bank of Gabor filters (as a biologically inspired recognition system) is used to obtain features, which are then post-processed using normalization, down-sampling (by bicubic interpolation), and principal components analysis (PCA). PCA is used for reducing the features size and a proper visualization of the features extraction results. Microscope images from the European pollen database, including pollen images of linden, acacia, lavender, rapeseed, and thistle, are used to illustrate capabilities of the proposed features extraction approach. The performance of the proposed algorithm is evaluated by simulations in MATLAB environment. Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Gabor filter ; Normalization (sociology) ; Visualization ; Principal component analysis ; Pollen ; Computer vision ; Feature extraction ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Bee Products Chemical Analysis ; Advanced Chemical Sensor Technologies
Authors: Barbara Sturm | Roberto Moscetti | Stuart Crichton | Sharvari Raut | Michael Bantle | Riccardo Massantini
Source:  (article, 2018-09-11)
Field: Chemistry — Analytical Chemistry

Feasibility of Vis/NIR spectroscopy and image analysis as basis of the development of smart-drying technologies Drying is a complex, dynamic, unsteady and nonlinear process that, when not optimized on a system level, may be responsible for (1) significant quality degradation and (2) energy wastage. Consequently, new drying technologies must be designed combining non-invasive at-/on-/in-line advanced measurement and control systems with models cross-linking all relevant aspects of product quality changes and heat and mass transfer phenomena. This paper presents preliminary results on the use of RGB imaging, NIR spectroscopy and Vis-NIR hyperspectral imaging for real-time monitoring of physicochemical changes of apples and carrots during drying. Keywords: chemometrics, artificial intelligence, deep learning Hyperspectral imaging ; Chemometrics ; Process engineering ; Process analytical technology ; RGB color model ; Computer science ; Spectroscopy ; Imaging spectroscopy ; Degradation (telecommunications) ; Mass transfer ; Process (computing) ; Quality (philosophy) ; Near-infrared spectroscopy ; Environmental science ; Artificial intelligence ; Biological system ; Machine learning ; Chemistry ; Engineering ; Physics ; Optics ; Chemical engineering ; Chromatography ; Bioprocess ; Telecommunications Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Advanced Chemical Sensor Technologies
Authors: Dalila Bensaddek | Armel Nicolas | Angus I. Lamond
Source: Current Opinion in Chemical Biology (review, 2018-12-11)
Field: Chemistry — Spectroscopy

Signal enhanced proteomics: a biological perspective on dissecting the functional organisation of cell proteomes Proteome ; Proteomics ; Computational biology ; Biology ; Cell biology ; Protein subcellular localization prediction ; Cell fractionation ; Gene isoform ; Quantitative proteomics ; Bioinformatics ; Biochemistry ; Gene ; Enzyme Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Biotin and Related Studies
Authors: Hua Zhu | Jun Yue | Zhenbo Li | Zhiwang Zhang
Source: International journal of agricultural and biological engineering (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Multi-kernel dictionary learning for maize varieties classification The automatic classification and identification of maize varieties is one of the important research contents in agriculture. A multi-kernel maize varieties classification approach was proposed in this paper in order to improve the recognition rate of maize varieties. In this approach, four kinds of maize varieties were selected, in each variety 200 grains were selected randomly as the samples, and in each sample 160 grains were taken as the training samples randomly; the characteristics of maize grain were extracted as the typical characteristics to distinguish maize varieties, by which the dictionary required by K-SVD was constructed; for the test samples, the feature-matrixes were extracted by dimension reduction method which were mapped to the high-dimension space by muti-kernel function mapping. The high-dimension characteristic matrixes were trained by K-SVD method and the corresponding feature dictionary was obtained respectively. Finally, the test samples representing were trained and classified by l2,1 minimization sparse coefficient. The experiment results showed that recognition rate was improved obviously through this approach, and the poor-effect to maize variety identification from partial occlusion can be eliminated effectively. Keywords: multi-kernel, sparse representation, dictionary learning, maize classification DOI: 10.25165/j.ijabe.20181103.3091 Citation: Zhu H, Yue J, Li Z B, Zhang Z W. Multi-kernel dictionary learning for classifying maize varieties. Int J Agric & Biol Eng, 2018; 11(3): 183–189. Mathematics ; Kernel (algebra) ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Combinatorics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Abhijith | Pai Srinivasa | Grynal D’Mello | Hebbar Gautama
Source: MATEC Web of Conferences (article, 2018-01-01)
Field: Materials Science — Biomaterials

Surface roughness optimization in machining of AZ31 magnesium alloy using ABC algorithm Magnesium alloys serve as excellent substitutes for materials traditionally used for engine block heads in automobiles and gear housings in aircraft industries. AZ31 is a magnesium alloy finds its applications in orthopedic implants and cardiovascular stents. Surface roughness is an important parameter in the present manufacturing sector. In this work optimization techniques namely firefly algorithm (FA), particle swarm optimization (PSO) and artificial bee colony algorithm (ABC) which are based on swarm intelligence techniques, have been implemented to optimize the machining parameters namely cutting speed, feed rate and depth of cut in order to achieve minimum surface roughness. The parameter Ra has been considered for evaluating the surface roughness. Comparing the performance of ABC algorithm with FA and PSO algorithm, which is a widely used optimization algorithm in machining studies, the results conclude that ABC produces better optimization when compared to FA and PSO for optimizing surface roughness of AZ 31. Particle swarm optimization ; Firefly algorithm ; Machining ; Surface roughness ; Magnesium alloy ; Artificial bee colony algorithm ; Materials science ; Computer science ; Surface finish ; Mechanical engineering ; Mathematical optimization ; Algorithm ; Engineering ; Alloy ; Metallurgy ; Mathematics ; Composite material ; Artificial intelligence Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics
Authors: Vadim Demichev | Christoph B. Messner | Kathryn S. Lilley | Markus Ralser
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-03-15)
Field: Chemistry — Spectroscopy

DIA-NN: Deep neural networks substantially improve the identification performance of Data-independent acquisition (DIA) in proteomics Abstract Data-independent acquisition (DIA-MS) strategies, like SWATH-MS, have been developed to increase consistency, quantification precision and proteomic depth in label-free proteomic experiments. They aim to overcome stochasticity in the selection of precursor ions by utilising (mass-) windowed acquisition that is followed by computational reconstruction of the chromatograms. While DIA methods increasingly outperform typical data-dependent methods in identification consistency and precision specifically on large sample series, possibilities remain for further improvements. At present, only a fraction of the information recorded in the complex DIA spectra is extracted by the software analysis pipelines. Here we present a software tool (DIA-NN) that introduces artificial neural nets and a new quantification strategy to enhance signal processing in DIA-data. DIA-NN greatly improves identification of precursor ions and, as a consequence, protein quantification accuracy. The performance of DIA-NN demonstrates that deep learning provides opportunities to boost the analysis of data-independent acquisition workflows in proteomics. Computer science ; Workflow ; Artificial neural network ; Consistency (knowledge bases) ; Identification (biology) ; Software ; Data acquisition ; Proteomics ; Artificial intelligence ; Data mining ; Quantitative proteomics ; Pattern recognition (psychology) ; Machine learning ; Chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies