jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alireza Barazesh | Mahdi Navidbakhsh | Ali Abouei Mehrizi | Mojtaba Koosha | Sajad Razavi Bazaz | Tianduo Li
Source: Frontiers in Bioengineering and Biotechnology (article, 2021-09-13)
Field: Materials Science — Biomaterials

Simultaneous Modeling of Young’s Modulus, Yield Stress, and Rupture Strain of Gelatin/Cellulose Acetate Microfibrous/Nanofibrous Scaffolds Using RSM Electrospinning is a promising method to fabricate bioengineered scaffolds, thanks to utilizing various types of biopolymers, flexible structures, and also the diversity of output properties. Mechanical properties are one of the major components of scaffold design to fabricate an efficacious artificial substitute for the natural extracellular matrix. Additionally, fiber orientations, as one of the scaffold structural parameters, could play a crucial role in the application of fabricated fibrous scaffolds. In this study, gelatin was used as a highly biocompatible polymer in blend with cellulose acetate (CA), a polysaccharide, to enhance the achievable range of mechanical characteristics to fabricated fibrous electrospun scaffolds. By altering input variables, such as polymers concentration, weight ratio, and mandrel rotation speed, scaffolds with various mechanical and morphological properties could be achieved. As expected, the electrospun scaffold with a higher mandrel rotation speed shows higher fiber alignment. A wide range of mechanical properties were gained through different values of polymer ratio and total concentration. A general improvement in mechanical strength was observed by increasing the concentration and CA content in the solution, but contradictory effects, such as high viscosity in more concentrated solutions, influenced the mechanical characteristics as well. A response surface method was applied on experimental results in order to describe a continuous variation of Young's modulus, yield stress, and strain at rupture. A full quadratic version of equations with the 95% confidence level was applied for the response modeling. This model would be an aid for engineers to adjust mandrel rotation speed, solution concentration, and gelatin/CA ratio to achieve desired mechanical and structural properties. Materials science ; Gelatin ; Composite material ; Cellulose acetate ; Scaffold ; Electrospinning ; Modulus ; Mandrel ; Polymer ; Vinyl alcohol ; Nanofiber ; Cellulose ; Biomedical engineering ; Chemical engineering ; Chemistry Electrospun Nanofibers in Biomedical Applications ; Additive Manufacturing and 3D Printing Technologies ; Bone Tissue Engineering Materials
Authors: Lucas Janoni dos Santos | Érica Regina Filletti | Fabíola Manhas Verbi Pereira
Source: Eclética Química (article, 2021-07-01)
Field: Chemistry — Analytical Chemistry

Artificial intelligence method developed for classifying raw sugarcane in the presence of the solid impurity An investigation dedicated to evaluating a big issue in biorefineries, solid impurity in raw sugarcane, is presented. This relevant industrial sector requests a high-frequency, low-cost, and noninvasive method. Then, the developed method uses the averaged color values of ten color-scale descriptors: R (red), G (green), B (blue), their relative colors (r, g, and b), H (hue), S (saturation), V (value) and L (luminosity) from digital images acquired from 146 solid mixtures among sugarcane stalks and solid impurity — vegetal parts (green and dry leaves) and soil. The solid mixture of samples was prepared considering desirable and undesirable scenarios for the solid impurity amounts. The outstanding result was revealed by an artificial neural network (ANN), achieving 100% of accurate classifications for two ranges of raw sugarcane in the samples: from 90 to 100 wt% and from 41 to 87 wt%. Low-computational cost and a simple setup for image acquisition method could screen solid impurity in sugarcane shipments as a promising application. Hue ; Impurity ; Raw material ; Artificial neural network ; Materials science ; Mathematics ; Artificial intelligence ; Environmental science ; Biological system ; Pulp and paper industry ; Computer science ; Chemistry ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Natural Products and Biological Research ; Sugarcane Cultivation and Processing
Authors: Fengping Guo
Source: Pakistan Veterinary Journal (article, 2021-03-01)
Field: Chemistry — Physical and Theoretical Chemistry

Bioinformatics Analysis of JAZF1 Gene in Broilers with Ascites Syndrome Pulmonary vascular remodeling (PVR) is the main characteristic lesion of ascites syndrome (AS) in broilers. JAZF1 plays an important role in PVR, but there is no study on its protein function and structure. In this study, the physical and chemical properties, hydrophilicity/hydrophobicity and transmembrane domain, phosphorylation site and glycosylation site, subcellular localization and signal peptide, secondary and tertiary structure, antigen peptide and conserved domain and phylogenetic relationship of JAZF1 protein were predicted online by bioinformatics tools. The results showed that the number of amino acids of JAZF1 was 243aa, the theoretical isoelectric point was 8.63, the instability index was 58.1, and the average coefficient of hydrophilicity was -0.674. It was found to be a hydrophilic protein having 35 phosphorylation sites and a N-glycosylation site with no transmembrane domain. The protein is expressed in the nucleus, there is no signal peptide distribution in the whole sequence and the secondary structure is mainly composed of random coil and α- helix. There were 7 B cell epitopes, 7 conserved domains and compared with other birds, JAZF1 is 95.61% similar. In summary, from the analysis we came to conclude that the amino acid sequence 64-80aa, 91-108aa, 136-151aa and 179-187aa can be selected as antigen sites and among which 136-151aa may be the best. This study lays a good foundation for follow-up experiments, which then provides powerful conditions for pathological detection of pulmonary vascular remodeling and gene drug therapy of ascites syndrome in broilers. Transmembrane domain ; Signal peptide ; Peptide ; Peptide sequence ; Amino acid ; Protein secondary structure ; Transmembrane protein ; Glycosylation ; Biology ; Epitope ; Random coil ; Biochemistry ; Chemistry ; Gene ; Antigen ; Receptor ; Genetics thermodynamics and calorimetric analyses ; Physiological and biochemical adaptations
Authors: Patrícia Ferreira Schopf | Ivana Zanella | M. Natália D. S. Cordeiro | Juan M. Ruso | Michael González‐Durruthy | Mirkos Ortiz Martins
Source: Biophysica (article, 2021-03-24)
Field: Chemistry — Organic Chemistry

Nanomarker for Early Detection of Alzheimer’s Disease Combining Ab initio DFT Simulations and Molecular Docking Approach The tau protein is considered an important qualitative and quantitative biomarker for Alzheimer’s disease in its asymptomatic phase. In 2011, biomarkers were suggested by the National Institute on Aging-Azheimer’s Association as a new criterion for the early diagnosis of Alzheimer’s disease. Thus, highlighting the non-existence of theoretical research on the subject, we investigated the binding interaction properties between phosphorylated tau protein and a theoretically modeled ligands constituted by the fullerol functionalized with radiopharmaceuticals from an in silico approach via molecular docking and density functional theory (DFT) ab initio computational simulation. The results demonstrated that the ligand with the greatest affinity-based binding energy to the protein was fullerol + F-THK5105. However, all systems were considered promising for the development of a potential diagnostic nanomarker. These theoretical results could efficiently contribute to reduce the time and the cost for future experimental preclinical studies and open new opportunities toward molecular recognition in nanomedicine. Nanomedicine ; Docking (animal) ; In silico ; Density functional theory ; Ab initio ; Computational biology ; Computational chemistry ; Binding energy ; Molecular dynamics ; Chemistry ; Disease ; Bioinformatics ; Nanotechnology ; Materials science ; Medicine ; Biology ; Physics ; Biochemistry ; Nanoparticle ; Gene ; Quantum mechanics ; Pathology Fullerene Chemistry and Applications ; Cholinesterase and Neurodegenerative Diseases ; Computational Drug Discovery Methods
Authors: Tatiana Nazarenko | Harry J. Whitwell | Oleg Blyuss | Alexey Zaikin
Source: Frontiers in Genetics (article, 2021-10-20)
Field: Chemistry — Spectroscopy

Parenclitic and Synolytic Networks Revisited Parenclitic networks provide a powerful and relatively new way to coerce multidimensional data into a graph form, enabling the application of graph theory to evaluate features. Different algorithms have been published for constructing parenclitic networks, leading to the question-which algorithm should be chosen? Initially, it was suggested to calculate the weight of an edge between two nodes of the network as a deviation from a linear regression, calculated for a dependence of one of these features on the other. This method works well, but not when features do not have a linear relationship. To overcome this, it was suggested to calculate edge weights as the distance from the area of most probable values by using a kernel density estimation. In these two approaches only one class (typically controls or healthy population) is used to construct a model. To take account of a second class, we have introduced synolytic networks, using a boundary between two classes on the feature-feature plane to estimate the weight of the edge between these features. Common to all these approaches is that topological indices can be used to evaluate the structure represented by the graphs. To compare these network approaches alongside more traditional machine-learning algorithms, we performed a substantial analysis using both synthetic data with <i>a priori</i> known structure and publicly available datasets used for the benchmarking of ML-algorithms. Such a comparison has shown that the main advantage of parenclitic and synolytic networks is their resistance to over-fitting (occurring when the number of features is greater than the number of subjects) compared to other ML approaches. Secondly, the capability to visualise data in a structured form, even when this structure is not <i>a priori</i> available allows for visual inspection and the application of well-established graph theory to their interpretation/application, eliminating the "black-box" nature of other ML approaches. Computer science ; Computer network Molecular spectroscopy and chirality ; Complex Network Analysis Techniques ; Computational Drug Discovery Methods
Authors: Jiyao Wang | Philippe Youkharibache | Aron Marchler‐Bauer | Christopher J. Lanczycki | Dachuan Zhang | Shennan Lu | Thomas Madej | Gabriele H. Marchler | Tiejun Cheng | Li Chuin Chong | Sarah Zhao | Kevin Yang | Jack J. Lin | Zhiyu Cheng | Rachel Dunn | Sridhar Acharya Malkaram | Chin‐Hsien Tai | David Enoma | Ben Busby | Nicholas L. Johnson | Francesco Tabaro | Guangfeng Song | Yuchen Ge
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-09-11)
Field: Materials Science — Materials Chemistry

iCn3D: From Web-based 3D Viewer to Structural Analysis Tool in Batch Mode Abstract iCn3D was initially developed as a web-based 3D molecular viewer. It then evolved from visualization into a full-featured interactive structural analysis software. It became a collaborative research instrument through the sharing of permanent, shortened URLs that encapsulate not only annotated visual molecular scenes, but also all underlying data and analysis scripts in a FAIR manner. More recently, with the growth of structural databases, the need to analyze large structural datasets systematically led us to use Python scripts and convert the code to be used in Node.js scripts. We showed a few examples of Python scripts at https://github.com/ncbi/icn3d/tree/master/icn3dpython to export secondary structures or PNG images from iCn3D. Users just need to replace the URL in the Python scripts to export other annotations from iCn3D. Furthermore, any interactive iCn3D feature can be converted into a Node.js script to be run in batch mode, enabling an interactive analysis performed on one or a handful of protein complexes to be scaled up to analysis features of large ensembles of structures. Currently available Node.js analysis scripts examples are available at https://github.com/ncbi/icn3d/tree/master/icn3dnode . This development will enable ensemble analyses on growing structural databases such as AlphaFold or RoseTTAFold on one hand and Electron Microscopy on the other. In this paper, we also review new features such as DelPhi electrostatic potential, 3D view of mutations, alignment of multiple chains, assembly of multiple structures by realignment, dynamic symmetry calculation, 2D cartoons at different levels, interactive contact maps, and use of iCn3D in Jupyter Notebook as described at https://pypi.org/project/icn3dpy . Python (programming language) ; Scripting language ; Computer science ; Visualization ; Software ; Web application ; Interactive visualization ; World Wide Web ; Computer graphics (images) ; Data mining ; Programming language Enzyme Structure and Function ; Protein Structure and Dynamics ; Genomics and Phylogenetic Studies
Authors: Ondřej Kozák | Josef Pihera
Source: Energies (article, 2021-04-29)
Field: Materials Science — Materials Chemistry

Partial Discharge Analysis and Simulation Using the Consecutive Pulses Correlation Method The behaviour of partial discharge as consequences of an alternating current (AC) is already well defined. AC partial discharges have completely different behaviour, background physics and parameters than partial discharges (PD) under direct current (DC) stress. This paper focuses on the most used and promising evaluation method of the PD DC stress—pulse sequence analysis (PSA). The first step is understanding and verifying the mechanisms and principles of this method. It is provided by well-known fundamentals of AC PD and by comparison with the other diagnostic and fault-locating methods such as phase-resolved partial discharge (PRPD) and pulse diagrams. The paper shows the PSA simulations and PD analyses performed at AC and partly at DC test conditions on typical PD test arrangements such as corona, surface and internal discharges. It is shown that the simulations performed, compared and validated with data obtained from measurements on different PD arrangements are a good match. This fact opens the way for the PD source recognition in DC, especially the time-resolved pulse sequence analysis described in detail in the paper. Partial discharge ; Sequence (biology) ; Pulse (music) ; Pulse sequence ; Stress (linguistics) ; Direct current ; Alternating current ; Current (fluid) ; Computational physics ; Materials science ; Physics ; Voltage ; Computer science ; Algorithm ; Electronic engineering ; Nuclear magnetic resonance ; Chemistry ; Electrical engineering ; Engineering ; Thermodynamics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Christopher J. Simpson | David Knowles | Mahmoud Mostafavi
Source: Metals (article, 2021-05-09)
Field: Materials Science — Materials Chemistry

Influence of Microstructure on Synchrotron X-ray Diffraction Lattice Strain Measurement Uncertainty Accurate residual lattice strain measurements are highly dependent upon the precision of the diffraction peak location and the underlying microstructure suitability. The suitability of the microstructure is related to the requirement for valid powder diffraction sampling statistics and the associated number of appropriately orientated illuminated. In this work, these two sources of uncertainty are separated, and a method given for both the quantification of errors associated with insufficient grain sampling statistics and minimization of the total lattice strain measurement uncertainty. It is possible to reduce the total lattice strain measurement uncertainty by leveraging diffraction peak measurements made at multiple azimuthal angles. Lattice strain measurement data acquired during eight synchrotron X-ray diffraction experiments, monochromatic and energy dispersive, has been assessed as per this approach, with microstructural suitability being seen to dominate total measurement uncertainty when the number of illuminated grains was &lt;106. More than half of the studied experimental data fell into this category, with a severe underestimation of total strain measurement uncertainty being possible when microstructural suitability is not considered. To achieve a strain measurement uncertainty under 10−4, approximately 3×105 grains must be within the sampled gauge volume, with this value varying with the multiplicity of the family of lattice planes under study. Where additional azimuthally arrayed data are available an in-plane lattice strain tensor can be extracted. This improves overall strain measurement accuracy and an uncertainty under 10−4 can then be achieved with just 4×104 grains. Diffraction ; Lattice constant ; Lattice (music) ; Materials science ; Microstructure ; Synchrotron radiation ; Measurement uncertainty ; Synchrotron ; X-ray crystallography ; Monochromatic color ; Infinitesimal strain theory ; Optics ; Computational physics ; Physics ; Statistics ; Mathematics ; Composite material ; Thermodynamics X-ray Diffraction in Crystallography ; Powder Metallurgy Techniques and Materials ; Microstructure and Mechanical Properties of Steels
Authors: Edurne Gaston | Jesús M. Frías | Patrick J. Cullen | Colm P. O’Donnell | Aoife Gowen
Source: Arrow - TU Dublin (Technological University Dublin) (article, 2021-03-03)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging for the Detection of Microbial Spoilage of Mushrooms Brown blotch, caused by pathogenic Pseudomonas tolaasii, is the most problematic bacterial disease in Agaricus bisporus mushrooms; it reduces their consumer appeal in the market place, thus generating important economical losses worldwide. The mushroom industry is in need of fast and accurate evaluation methodologies to ensure that only high quality produce reaches the market. Hyperspectral imaging (HSI) is a non-destructive technique that combines imaging and spectroscopy to obtain spatial and spectral information from an object. The aim of this study was to investigate the potential of Vis-NIR HSI to identify microbiological damage in mushrooms and to discriminate it from mechanical damage. Hyperspectral images of mushrooms subjected to i) no treatment, ii) microbiological spoilage and iii) mechanical damage were taken during storage and spectra representing each of the classes were selected. Partial least squares- discriminant analysis (PLS-DA) was carried out in two steps: i) discrimination between undamaged and damaged mushrooms and ii) discrimination between damage sources (i.e. microbiological or mechanical). The models were applied at a pixel level and a decision tree was used to classify mushrooms into one of the aforementioned classes. A correct classification of &gt;95% was achieved. This was the first reported study to employ HSI for the detection of damage of bacterial origin in horticultural products. The industry could incorporate the knowledge gained in this study towards the development of a HSI sensor to detect and classify mushroom damage of microbial and mechanical origin, enabling the rapid and automated identification of mushrooms of reduced marketability. Hyperspectral imaging ; Agaricus bisporus ; Mushroom ; Food spoilage ; Artificial intelligence ; Environmental science ; Remote sensing ; Computer science ; Pattern recognition (psychology) ; Food science ; Biology ; Geography ; Bacteria Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Bee Products Chemical Analysis
Authors: Marco Tognetti | Kamil Skłodowski | Sebastian Müller | Dominique Kamber | Jan Muntel | Roland Bruderer | Lukas Reiter
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-10-06)
Field: Chemistry — Spectroscopy

Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area Abstract The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancer. Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma. In the cohort, we identified 2,732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the disease state. For pancreatic cancer, a separation by stage was achieved. Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling. Biomarker ; Biomarker discovery ; Pancreatic cancer ; Profiling (computer programming) ; Colorectal cancer ; Breast cancer ; Prostate cancer ; Proteome ; Cohort ; Oncology ; Medicine ; Computational biology ; Cancer research ; Bioinformatics ; Internal medicine ; Cancer ; Biology ; Proteomics ; Computer science ; Gene ; Genetics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications
Authors: Claudio Fredes | Constantino Valero Ubierna | Belén Diezma Iglesias | Marco Mora | José Naranjo-Torres | Manuel Wilson | Gabriel Delgadillo
Source: Foods (article, 2021-03-13)
Field: Chemistry — Analytical Chemistry

A Model Based on Clusters of Similar Color and NIR to Estimate Oil Content of Single Olives Lipid extraction using the traditional, destructive Soxhlet method is not able to measure oil content (OC) on a single olive. As the color and near infrared spectrum are key parameters to build an oil estimation model (EM), this study grouped olives with similar color and NIR for building EM of oil content obtained by Soxhlet from a cluster of similar olives. The objective was to estimate OC of individual olives, based on clusters of similar color and NIR in two seasons. This study was performed with Arbequina olives in 2016 and 2017. The descriptor of the cluster consisted of the three color channels of c1c2c3 color model plus 11 reflectance points between 1710 and 1735 nm of each olive, normalized with the Z-score index. Clusters of similar color and NIR spectrum were formed with the k-means++ algorithm, leaving a sufficient number of olives to perform the Soxhlet analysis of OC, as reference value of EM. The training of EM was based on Support Vector Machine. The test was performed with Leave One-Out Cross Validation in different training-testing combinations. The best EM predicted the OC with 6 and 13% deviation with respect to the real value when one season was tested with itself and with another season, respectively. The use of clustering in EM is discussed. Mathematics ; Olive oil ; Cluster analysis ; Reflectivity ; Near-infrared spectroscopy ; Pattern recognition (psychology) ; Statistics ; Artificial intelligence ; Chemistry ; Food science ; Computer science ; Physics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies