jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Miguel-Jorge Reig-Valor | Javier Rozas-Martínez | Alexis López-Borrell | Jaime Lora-García | F. López
Source: Membranes (article, 2024-03-30)
Field: Materials Science — Biomaterials

Experimental Study of a Sequential Membrane Process of Ultrafiltration and Nanofiltration for Efficient Polyphenol Extraction from Wine Lees The wine industry is a sector of great importance in the Spanish economy, contributing substantial annual revenues. However, one challenge facing the industry is the amount of waste generated, reaching millions of tons annually. These residues consist of organic matter of industrial interest, such as polyphenols. These substances are characterised by their excellent antioxidant properties, making them ideal for use in the food, cosmetic, and pharmaceutical industries. Modern techniques, such as membrane technology, are explored for their extraction based on separating compounds according to size. This work studies a sequential filtration process using ultrafiltration (UF) and nanofiltration (NF) membranes at different operating conditions (2 bar and 9.5 bar for UF and NF, respectively, at 20 °C) to extract polyphenols from wine lees. The results show a total polyphenols rejection rate for each process of 54% for UF and 90% for NF. Pore blocking models have been studied for the UF process and an intermediate pore blocking of the membrane upon wine lees filtration has been identified. A mathematical model that justifies the behavior of a polymeric NF membrane with the filtration of pre-treated vinasse residues has been validated. This study shows a viable process for extracting polyphenols from wine lees with sequential membrane technology. Lees ; Nanofiltration ; Ultrafiltration (renal) ; Wine ; Polyphenol ; Membrane ; Chemistry ; Pulp and paper industry ; Winemaking ; Filtration (mathematics) ; Diafiltration ; Chromatography ; Membrane technology ; Microfiltration ; Food science ; Mathematics ; Engineering ; Organic chemistry ; Antioxidant ; Biochemistry Electrospun Nanofibers in Biomedical Applications ; Membrane Separation Technologies ; Advanced Cellulose Research Studies
Authors: Qingxue Zhang | Junguo Cui | Wensheng Xiao | Lianpeng Mei | Xiaolong Yu
Source: Electronics (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Demagnetization Fault Diagnosis of a PMSM for Electric Drilling Tools Using GAF and CNN Permanent magnets (PMs) provide high efficiency for synchronous motors used for driving drilling tools. Demagnetization is a special fault that reduces the efficiency of the permanent magnet synchronous motor (PMSM) and thus affects the performance of the drilling tools. Therefore, early detection of demagnetization is important for safe and efficient operation. However, it is difficult to detect multiple demagnetization types at the same time using traditional fault diagnosis methods, and the recognition accuracy cannot be guaranteed. To solve the above problem, this article proposes a method combining Gramian angular field (GAF) transform and convolutional neural network (CNN) to recognize and classify different types of demagnetization faults based on output torque signal. Firstly, the thermal demagnetization model of PM was obtained by experiments, and the finite element model (FEM) of PMSM for electric drilling tools was established to analyze the torque, back electromotive force (BEMF), and air gap flux density under different demagnetization faults. Then, the acquired one-dimensional torque signals were transformed into two-dimensional gray images based on the GAF method to enhance the fault features. To improve the generalization ability of the CNN, these gray images were augmented through increasing noise. Finally, the CNN structure was designed and trained with a training accuracy of 98.33%, and the effectiveness of the method was verified by the demagnetization fault experiment. The results show that the testing accuracy of the CNN model was 97.41%, indicating the proposed method can diagnose various demagnetization faults effectively, and that it is immune to loads. Armature (electrical engineering) ; Demagnetizing field ; Convolutional neural network ; Torque ; Fault (geology) ; Computer science ; Torque density ; Engineering ; Magnet ; Control theory (sociology) ; Artificial intelligence ; Magnetic field ; Mechanical engineering ; Geology Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Machine Fault Diagnosis Techniques
Authors: Xinglong Wang | Shuyao Yu | Ruoxi Sun | Kangjie Xu | Kun Wang | Wang Rui-yan | Junli Zhang | Wenwen Tao | Shangyang Yu | Kai Linghu | Xinyi Zhao | Jingwen Zhou
Source: Synthetic and Systems Biotechnology (article, 2024-06-06)
Field: Materials Science — Biomaterials

Identification of a human type XVII collagen fragment with high capacity for maintaining skin health Collagen XVII (COL17) is a transmembrane protein that mediates skin homeostasis. Due to expression of full length collagen was hard to achieve in microorganisms, arising the needs for selection of collagen fragments with desired functions for microbial biosynthesis. Here, COL17 fragments (27-33 amino acids) were extracted and replicated 16 times for recombinant expression in <i>Escherichia coli</i>. Five variants were soluble expressed, with the highest yield of 223 mg/L. The fusion tag was removed for biochemical and biophysical characterization. Circular dichroism results suggested one variant (sample-1707) with a triple-helix structure at >37 °C. Sample-1707 can assemble into nanofiber (width, 5.6 nm) and form hydrogel at 3 mg/mL. Sample-1707 was shown to induce blood clotting and promote osteoblast differentiation. Furthermore, sample-1707 exhibited high capacity to induce mouse hair follicle stem cells differentiation and osteoblast migration, demonstrating a high capacity to induce skin cell regeneration and promote wound healing. A strong hydrogel was prepared from a chitosan and sample-1707 complex with a swelling rate of >30 % higher than simply using chitosan. Fed-batch fermentation of sample-1707 with a 5-L bioreactor obtained a yield of 600 mg/L. These results support the large-scale production of sample-1707 as a biomaterial for use in the skin care industry. Identification (biology) ; Fragment (logic) ; Type (biology) ; Computational biology ; Computer science ; Biology ; Algorithm ; Ecology Collagen: Extraction and Characterization ; Cell Adhesion Molecules Research ; Protease and Inhibitor Mechanisms
Authors: Urvashi Agrawal | Narendra Bawane | Najah Alsubaie | Mohammed S. Alqahtani | Mohamed Abbas | Ben Othman Soufiene
Source: Scientific Reports (article, 2024-10-25)
Field: Chemistry — Analytical Chemistry

Design &amp; development of adulteration detection system by fumigation method &amp; machine learning techniques A novel method for discovery of adulteration in edible oil is proposed based on concept of refractive index and electronic sensors. The research work focusses on two distinct methodologies like employing datasets and implementing a fumigation technique that integrates real-time hardware for testing Edible oil Impurities. In the first method, the dataset taken into consideration contains spectral data collected using Advanced ATR-MIR Spectroscopy for pure oil and various levels of adulteration with Vegetable oil. Each and every edible oil has a certain value of refractive index. When such oils are contemned in a change adding adulterants, the value of its refractive indices also changes. This value of refractive index serves as a feature for testing the oil and helps us in detecting the adulteration. If Oil is adulterated with vegetable oils, the refractive index will be lower and with animal fats, the refractive index will be higher than that of pure Oil. While in Fumigation Method a hardware module is develop in which adulterated & pure oil samples are heated at 40-50 °C for 4.66 min and the volatiles that are generated by varying gas concentrations are forcefully passed through to the MEMS Gas Sensor-MISC-2714 and Multichannel Gas sensor. The conductance of the sensors changes according to the gases sensed by the sensors contributes to features extraction. The conductance value serves as a feature for the classifier to determine whether the sample is highly, moderately, or lowly contaminated. Thus, in proposed methods we use different algorithms based on machine learning like KNN, Random Forest, CATBOOST and XGBOOST to accurately reveal the adulteration. Amongst all the applied algorithm Random Forest (RF) Classifier & XGBOOST algorithm outperform well and gives 100% accuracy. The proposed work is used for identifying food adulteration in edible food products which helps us to feed Society with high-quality food. Fumigation ; Computer science ; Computational biology ; Biology ; Agronomy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fire Detection and Safety Systems
Authors: Noor Abu Jarad | Akansha Prasad | Sara Rahmani | Fereshteh Bayat | Mathura Thirugnanasampanthar | Zeinab Hosseinidoust | Leyla Soleymani | Tohid F. Didar
Source: Advanced Functional Materials (article, 2024-07-30)
Field: Materials Science — Materials Chemistry

Smart Fabrics with Integrated Pathogen Detection, Repellency, and Antimicrobial Properties for Healthcare Applications Abstract Healthcare textiles serve as key reservoirs for pathogen proliferation, demanding an urgent call for innovative interventions. Here, a new class of Smart Fabrics (SF) is introduced with integrated “Repel, Kill, and Detect” functionalities, achieved through a blend of hierarchically structured microparticles, modified nanoparticles, and an acidity‐responsive sensor. SF exhibit remarkable resilience against aerosol and droplet‐based pathogen transmission, showcasing a reduction exceeding 99.90% compared to uncoated fabrics across various drug‐resistant bacteria, Candida albicans , and Phi6 virus. Experiments involving bodily fluids from healthy and infected individuals reveal a significant reduction of 99.88% and 99.79% in clinical urine and feces samples, respectively, compared to uncoated fabrics. The SF's colorimetric detection capability coupled with machine learning (96.67% accuracy) ensures reliable pathogen identification, facilitating accurate differentiation between healthy and infected urine and fecal contaminated samples. SF holds promise for revolutionizing infection prevention and control in healthcare facilities, providing protection through early contamination detection. Materials science ; Antimicrobial ; Nanotechnology ; Pathogen ; Microbiology ; Biology Nanoparticles: synthesis and applications ; Infection Control and Ventilation ; Microplastics and Plastic Pollution
Authors: Jiaqi He | Leen Van Doorselaer | Astrid Tempelaere | Janne Vignero | Wouter Saeys | Hilde Bosmans | Pieter Verboven | Bart Nicolaı̈
Source: Postharvest Biology and Technology (article, 2024-05-03)
Field: Chemistry — Analytical Chemistry

Nondestructive internal disorders detection of ‘Braeburn’ apple fruit by X-ray dark-field imaging and machine learning Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Advanced X-ray Imaging Techniques
Authors: Elisa Roldán | Neil D. Reeves | Glen Cooper | Kirstie Andrews
Source: Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials (article, 2024-06-17)
Field: Materials Science — Biomaterials

Machine learning to mechanically assess 2D and 3D biomimetic electrospun scaffolds for tissue engineering applications: Between the predictability and the interpretability Currently, the use of autografts is the gold standard for the replacement of many damaged biological tissues. However, this practice presents disadvantages that can be mitigated through tissue-engineered implants. The aim of this study is to explore how machine learning can mechanically evaluate 2D and 3D polyvinyl alcohol (PVA) electrospun scaffolds (one twisted filament, 3 twisted filament and 3 twisted/braided filament scaffolds) for their use in different tissue engineering applications. Crosslinked and non-crosslinked scaffolds were fabricated and mechanically characterised, in dry/wet conditions and under longitudinal/transverse loading, using tensile testing. 28 machine learning models (ML) were used to predict the mechanical properties of the scaffolds. 4 exogenous variables (structure, environmental condition, crosslinking and direction of the load) were used to predict 2 endogenous variables (Young's modulus and ultimate tensile strength). ML models were able to identify 6 structures and testing conditions with comparable Young's modulus and ultimate tensile strength to ligamentous tissue, skin tissue, oral and nasal tissue, and renal tissue. This novel study proved that Classification and Regression Trees (CART) models were an innovative and easy to interpret tool to identify biomimetic electrospun structures; however, Cubist and Support Vector Machine (SVM) models were the most accurate, with R<sup>2</sup> of 0.93 and 0.8, to predict the ultimate tensile strength and Young's modulus, respectively. This approach can be implemented to optimise the manufacturing process in different applications. Interpretability ; Predictability ; Tissue engineering ; Computer science ; Artificial intelligence ; Nanotechnology ; Machine learning ; Materials science ; Biomedical engineering ; Engineering ; Mathematics Electrospun Nanofibers in Biomedical Applications ; 3D Printing in Biomedical Research ; Artificial Intelligence in Healthcare and Education
Authors: T Lucas | A. M. Saunders | Christopher A. Schuh
Source: Acta Materialia (article, 2024-08-23)
Field: Materials Science — Materials Chemistry

Microstructure effects on high velocity microparticle impacts of copper Materials science ; Microstructure ; Copper ; Microparticle ; Metallurgy ; Composite material ; Chemical engineering High-Velocity Impact and Material Behavior ; Ion-surface interactions and analysis ; Nuclear Materials and Properties
Authors: Lifei Hu | Feng Zhu | Yifan Wang | Tao Wu | Xinyu Wu | Zhian Huang | Daihua Sun | Mingxing Liu
Source: Food Chemistry X (article, 2024-10-18)
Field: Chemistry — Analytical Chemistry

Comparison and chemometrics analysis of phenolic compounds and mineral elements in Artemisia Argyi Folium from different geographical origins The quality of Artemisia Argyi Folium (AAF), a traditional Chinese food ingredient, is intrinsically linked to its geographical origin, which this study explores through phenolic compounds and mineral elements. The contents of 17 phenols and 18 minerals differed significantly between geographically distinct samples according to UHPLC and ICP-MS, respectively. Chemometrics indicated that a supervised model, orthogonal partial least squares discriminant analysis (OPLS-DA), outperformed unsupervised methods at classifying AAF samples by their origins. Phenols were more effective at distinguishing samples from seven provinces, while minerals were adept at differentiating samples from the Dabie Mountain region (three provinces) and those from four other provinces. Six phenols and 10 minerals were important variables for discrimination. Complex correlations were observed between the contents of various phenols and minerals in AAF, with minerals possibly affecting the accumulation of phenols. This study provides an approach for distinguishing geographically distinct AAF samples and determining their geographical origins. Folium of Descartes ; Chemometrics ; Mineral ; Traditional medicine ; Chemistry ; Chromatography ; Medicine Heavy Metals in Plants ; Essential Oils and Antimicrobial Activity ; Phytochemicals and Antioxidant Activities
Authors: Gábor Nagy | Søren Vrønning Hoffmann | Nykola C. Jones | Helmut Grubmüller
Source: Applied Spectroscopy (article, 2024-04-22)
Field: Chemistry — Spectroscopy

Reference Data Set for Circular Dichroism Spectroscopy Comprised of Validated Intrinsically Disordered Protein Models Circular dichroism (CD) spectroscopy is an analytical technique that measures the wavelength-dependent differential absorbance of circularly polarized light and is applicable to most biologically important macromolecules, such as proteins, nucleic acids, and carbohydrates. It serves to characterize the secondary structure composition of proteins, including intrinsically disordered proteins, by analyzing their recorded spectra. Several computational tools have been developed to interpret protein CD spectra. These methods have been calibrated and tested mostly on globular proteins with well-defined structures, mainly due to the lack of reliable reference structures for disordered proteins. It is therefore still largely unclear how accurately these computational methods can determine the secondary structure composition of disordered proteins. Here, we provide such a required reference data set consisting of model structural ensembles and matching CD spectra for eight intrinsically disordered proteins. Using this set of data, we have assessed the accuracy of several published CD prediction and secondary structure estimation tools, including our own CD analysis package, SESCA. Our results show that for most of the tested methods, their accuracy for disordered proteins is generally lower than for globular proteins. In contrast, SESCA, which was developed using globular reference proteins, but was designed to be applicable to disordered proteins as well, performs similarly well for both classes of proteins. The new reference data set for disordered proteins should allow for further improvement of all published methods. Globular protein ; Circular dichroism ; Intrinsically disordered proteins ; Protein secondary structure ; Chemistry ; Macromolecule ; Protein structure ; Absorbance ; Reference data ; Spectroscopy ; Biological system ; Crystallography ; Physics ; Computer science ; Biochemistry ; Biology ; Chromatography ; Data mining Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Sherif Hamdy | Aurélie Charrier | Laurence Le Corre | Pejman Rasti | David Rousseau
Source: Plant Methods (article, 2024-05-06)
Field: Chemistry — Analytical Chemistry

Toward robust and high-throughput detection of seed defects in X-ray images via deep learning This work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging. The method is illustrated on sugar beet and faba bean and could be efficiently extended to other species via the proposed generic X-ray data processing approach (X-Robustifier). Beyond a simple proof of feasibility, this constitutes important results toward the effective use in the routine of deep learning-based automatic detection of seed defects. Robustness (evolution) ; Artificial intelligence ; Deep learning ; Computer science ; Convolutional neural network ; Pattern recognition (psychology) ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Sugarcane Cultivation and Processing
Authors: Franziska Sendker | Tabea Schlotthauer | Christopher-Nils Mais | Yat Kei Lo | Mathias Girbig | Stefan Bohn | Thomas Heimerl | Daniel Schindler | Arielle Weinstein | Brian P. H. Metzger | Joseph W. Thornton | Arvind Pillai | Gert Bange | Jan M. Schuller | Georg Hochberg
Source: Nature Communications (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Frequent transitions in self-assembly across the evolution of a central metabolic enzyme Many enzymes assemble into homomeric protein complexes comprising multiple copies of one protein. Because structural form is usually assumed to follow function in biochemistry, these assemblies are thought to evolve because they provide some functional advantage. In many cases, however, no specific advantage is known and, in some cases, quaternary structure varies among orthologs. This has led to the proposition that self-assembly may instead vary neutrally within protein families. The extent of such variation has been difficult to ascertain because quaternary structure has until recently been difficult to measure on large scales. Here, we employ mass photometry, phylogenetics, and structural biology to interrogate the evolution of homo-oligomeric assembly across the entire phylogeny of prokaryotic citrate synthases - an enzyme with a highly conserved function. We discover a menagerie of different assembly types that come and go over the course of evolution, including cases of parallel evolution and reversions from complex to simple assemblies. Functional experiments in vitro and in vivo indicate that evolutionary transitions between different assemblies do not strongly influence enzyme catalysis. Our work suggests that enzymes can wander relatively freely through a large space of possible assembly states and demonstrates the power of characterizing structure-function relationships across entire phylogenies. Protein quaternary structure ; Phylogenetics ; Evolutionary biology ; Biology ; Homomeric ; Function (biology) ; Protein structure ; Computational biology ; Genetics ; Biochemistry ; Gene ; Protein subunit Enzyme Structure and Function ; Biochemical and Molecular Research ; Genomics and Phylogenetic Studies
Authors: Junping Zhu | Jiaming Wei | Ye Lin | Yuanyuan Tang | Zhaoli Su | Li-qing Li | Bin Liu | Xiong Cai
Source: BMC Complementary Medicine and Therapies (article, 2024-02-27)
Field: Chemistry — Organic Chemistry

Inhibition of IL-17 signaling in macrophages underlies the anti-arthritic effects of halofuginone hydrobromide: Network pharmacology, molecular docking, and experimental validation In summary, these findings suggest that HF has the potential to inhibit the activation of inflammatory macrophages through its regulation of the IL-17 signaling pathway, underscoring its potential in the suppression of immune-mediated inflammation in RA. Docking (animal) ; Pharmacology ; Chemistry ; Medicine Quinazolinone synthesis and applications ; Synthesis and biological activity ; Synthesis and Biological Evaluation
Authors: Harry Wilders | George S. Biggs | Sam M. Rowe | Emma E. Cawood | Ioannis G. Riziotis | Alan R. Rendina | Emma K. Grant | Jonathan Pettinger | David J. Fallon | Mark Skehel | David House | Nicholas C. O. Tomkinson | Jacob T. Bush
Source: Angewandte Chemie International Edition (article, 2024-12-04)
Field: Chemistry — Organic Chemistry

Expedited SARS‐CoV‐2 Main Protease Inhibitor Discovery through Modular ‘Direct‐to‐Biology’ Screening Reactive fragment (RF) screening has emerged as an efficient method for ligand discovery across the proteome, irrespective of a target's perceived tractability. To date, however, the efficiency of subsequent optimisation campaigns has largely been low-throughput, constrained by the need for synthesis and purification of target compounds. We report an efficient platform for 'direct-to-biology' (D2B) screening of cysteine-targeting chloroacetamide RFs, wherein synthesis is performed in 384-well plates allowing direct assessment in downstream biological assays without purification. Here, the developed platform was used to optimise inhibitors of SARS-CoV-2 main protease (M<sup>Pro</sup>), an established drug target for the treatment of COVID-19. An initial RF hit was developed into a series of potent inhibitors, and further exploration using D2B screening enabled a 'switch' to a reversible inhibitor series. This example of ligand discovery for M<sup>Pro</sup> illustrates the acceleration that D2B chemistry can offer for optimising RFs towards covalent inhibitor candidates, as well as providing future impetus to explore the evolution of RFs into non-covalent ligands. Drug discovery ; Computational biology ; High-throughput screening ; Chemical biology ; Protease inhibitor (pharmacology) ; Ligand efficiency ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Proteome ; Drug target ; Ligand (biochemistry) ; Combinatorial chemistry ; Chemistry ; Coronavirus disease 2019 (COVID-19) ; Nanotechnology ; Biology ; Biochemistry ; Virology ; Medicine ; Virus ; Receptor ; Materials science Click Chemistry and Applications ; Computational Drug Discovery Methods ; Peptidase Inhibition and Analysis