jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maxime Lorenzini | Sophie Burel | Adrien Lesage | Emily Wagner | Camille Charrière | Pierre‐Marie Chevillard | Evrard Berangere | Dan Maloney | Kiersten M. Ruff | Rohit V. Pappu | Stefan Wagner | Jeanne M. Nerbonne | Jonathan R. Silva | R. Reid Townsend | Lars S. Maier | Céline Marionneau
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-04-30)
Field: Chemistry — Spectroscopy

Proteomic and functional mapping of cardiac Na <sub>V</sub> 1.5 channel phosphorylation reveals multisite regulation of surface expression and gating Abstract Phosphorylation of Na V 1.5 channels regulates cardiac excitability, yet the phosphorylation sites regulating channel function and the underlying mechanisms remain largely unknown. Using a systematic quantitative phosphoproteomic approach, we analyzed Na V 1.5 channel complexes purified from non-failing and failing mouse left ventricles, and we identified 42 phosphorylation sites on Na V 1.5. Most sites are clustered, and three of these clusters are highly phosphorylated. Analyses of phosphosilent and phosphomimetic Na V 1.5 mutants revealed the roles of three phosphosites in regulating Na V 1.5 channel expression and gating. The phosphorylated serines-664 and -667 regulate the voltage-dependence of channel activation in a cumulative manner, whereas phosphorylation of the nearby serine-671, which is increased in failing hearts, decreases cell surface Na V 1.5 expression and peak Na + current. No additional roles could be assigned to the other clusters of phosphosites. Taken together, the results demonstrate that ventricular Na V 1.5 is highly phosphorylated, and that the phosphorylation-dependent regulation of Na V 1.5-encoded channels is highly complex, site-specific and dynamic. Abbreviations A, alanine; E, glutamate; HEK-293, Human Embryonic Kidney 293 cells; I Na , peak Na + current; I NaL , late Na + current; IP, immunoprecipitation; mαNa V PAN, anti-Na V channel subunit mouse monoclonal antibody; MS, Mass Spectrometry; MS1, mass spectrum of peptide precursors; MS2 or MS/MS, fragmentation mass spectrum of peptides selected in narrow mass range (2 Da) from MS1 scan; Na V , voltage-gated Na + channel; pS, phosphoserine; pT, phosphothreonine; S, serine; T, threonine; TAC, Transverse Aortic Constriction; TMT, Tandem Mass Tag. Phosphorylation ; Gating ; HEK 293 cells ; Serine ; Immunoprecipitation ; Protein subunit ; Chemistry ; Biophysics ; Molecular biology ; Cell biology ; Biology ; Biochemistry ; Gene Mass Spectrometry Techniques and Applications ; Ion channel regulation and function ; Advanced Proteomics Techniques and Applications
Authors: Akshay Subramanian | Utkarsh Saha | Tejasvini Sharma | Naveen Kumar Tailor | Soumitra Satapathi
Source: arXiv (Cornell University) (preprint, 2020-03-17)
Field: Materials Science — Materials Chemistry

Inverse Design of Potential Singlet Fission Molecules using a Transfer Learning Based Approach Singlet fission has emerged as one of the most exciting phenomena known to improve the efficiencies of different types of solar cells and has found uses in diverse optoelectronic applications. The range of available singlet fission molecules is, however, limited as to undergo singlet fission, molecules have to satisfy certain energy conditions. Recent advances in material search using inverse design has enabled the prediction of materials for a wide range of applications and has emerged as one of the most efficient methods in the discovery of suitable materials. It is particularly helpful in manipulating large datasets, uncovering hidden information from the molecular dataset and generating new structures. However, we seldom encounter large datasets in structure prediction problems in material science. In our work, we put forward inverse design of possible singlet fission molecules using a transfer learning based approach where we make use of a much larger ChEMBL dataset of structurally similar molecules to transfer the learned characteristics to the singlet fission dataset. chEMBL ; Singlet fission ; Inverse ; Singlet state ; Computer science ; Fission ; Molecule ; Range (aeronautics) ; Chemistry ; Materials science ; Physics ; Nuclear physics ; Drug discovery ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Amin Vahidi‐Moghaddam | Arman Rajaei | Moosa Ayati | Ramin Vatankhah | Mohammad Reza Hairi Yazdi
Source: IET Control Theory and Applications (preprint, 2020-12-01)
Field: Materials Science — Materials Chemistry

Adaptive prescribed‐time disturbance observer using nonsingular terminal sliding mode control: Extended Kalman filter and particle swarm optimization In this study, adaptive prescribed finite‐time stabilisation of uncertain single‐input and single‐output non‐linear systems is considered in the presence of unknown states, unknown parameters, external load disturbance, and non‐symmetric input saturation. A prescribed finite time disturbance observer is designed to approximate the unmeasured external disturbance. Also, a non‐singular prescribed finite time terminal sliding mode control is proposed for the closed‐loop control of the system with the non‐symmetric input saturation. Extended Kalman filter algorithm is employed for the real‐time estimations of the states and unknown parameters of the system. Moreover, a particle swarm optimisation algorithm is used to obtain the design parameters of the proposed disturbance observer and controller. To show the performance of designed control scheme, the proposed approach is employed to guarantee prescribed finite time stabilisation of non‐linear vibration of a non‐local strain gradient nanobeam. The Galerkin projection method is used to reduce the non‐dimensional form of the governing non‐linear partial differential equation of Euler–Bernoulli nanobeam to the ordinary differential equation. Finally, numerical simulations are performed to illustrate the effectiveness and performance of the developed adaptive control scheme for the vibration control of nanobeam in comparison with the conventional sliding mode control. Control theory (sociology) ; Particle swarm optimization ; Terminal sliding mode ; Nonlinear system ; Sliding mode control ; Controller (irrigation) ; Extended Kalman filter ; Mathematics ; Computer science ; Kalman filter ; Mathematical optimization ; Control (management) ; Physics Nonlocal and gradient elasticity in micro/nano structures ; Piezoelectric Actuators and Control ; Shape Memory Alloy Transformations
Authors: Andrew L. Ferguson | Johannes Hachmann | Thomas F. Miller | Jim Pfaendtner
Source: The Journal of Physical Chemistry A (article, 2020-11-05)
Field: Materials Science — Materials Chemistry

<i>The Journal of Physical Chemistry A</i>/<i>B</i>/<i>C</i> Virtual Special Issue on Machine Learning in Physical Chemistry Physical chemistry stands today at an exciting transition state where the integration of machine learning and data science tools into all corners of the field stands poised to do nothing short of revolutionizing the discipline. These powerful techniques—when appropriately combined with domain knowledge, tools, and expertise—have led to new physical insights, better understanding, accelerated discovery, rational design, and inverse engineering that transcend traditional approaches to materials, molecular, and chemical science and engineering. The primary driver of this trend has been the impressive advances enabled by machine learning, artificial intelligence, and data science tools, ranging from the discovery of novel electronic and optical materials by high-throughput virtual screening, to the massive acceleration of molecular simulations using learned classical force fields with quantum accuracy, to the powering of “self-driving laboratories” for automated chemical discovery. The 2011 White House Materials Genome Initiative (MGI), the 2017 NSF Data-Driven Discovery Science in Chemistry (D3SC) initiative, and the 2019 NSF Big Idea Harnessing the Data Revolution are some of the US federal programs that have provided incentive, attention, momentum, and support to power these advances and help drive the field forward. Necessity is also the mother of invention, and the prevalence of large data sets routinely generated by high-throughput virtual screening or automated experimentation have spurred the need for scalable data science and machine learning techniques to parse, explore, and harness the full power of these voluminous data streams. It bears remembering that physical chemistry is no stranger to machine learning, most visibly in the cheminformatics and quantitative structure property relation (QSPR) work that emerged in the 1980s. Some of the techniques being implemented today are, to some degree, reinventions of these ideas, but others are fundamentally new concepts that have been adopted and adapted from diverse fields including computer vision, manifold learning, and deep learning. This Virtual Special Issue on Machine Learning in Physical Chemistry covering all sections of The Journal of Physical Chemistry A/B/C pays tribute to this development, and the relevance and popularity of this topic is reflected in the depth and breadth of excellent articles in this exciting collection. Chemistry ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Vipul Mann | Venkat Venkatasubramanian
Source: ChemRxiv (preprint, 2020-09-22)
Field: Materials Science — Materials Chemistry

Predicting Chemical Reaction Outcomes: A Grammar Ontology-based Transformer Framework Discovering and designing novel materials is a challenging problem as it often requires searching a combinatorially large space of potential candidates. Evaluation of all candidates experimentally is typically infeasible as it requires great amounts of effort, time, expertise, and money. The ability to predict reaction outcomes without performing extensive experiments is, therefore, important. Towards that goal, we report an approach that uses context-free grammar (CFG) based representations of molecules in a neural machine translation framework. We formulate the reaction-prediction task as a machine translation problem that involves discovering the transformations from the source sequence (comprising the reactants and agents) to the target sequence (comprising the major product) in the reaction. The grammar ontology-based representation of molecules hierarchically incorporates rich molecular structure information that, in principle, should be valuable for modeling chemical reactions. We achieve an accuracy of 80.1% on a standard reaction dataset using a model characterized by only a fraction of the number of training parameters in other sequence-to-sequence models based works in this area. Moreover, 99% of the predictions made on the same reaction dataset were found to be syntactically valid. We conclude that CFGs-based ontological representations could be an efficient way of incorporating structural information, ensuring chemically valid predictions, and overcoming overfitting in complex machine learning architectures employed in reaction prediction tasks. Computer science ; Overfitting ; Artificial intelligence ; Sequence (biology) ; Machine learning ; Grammar ; Natural language processing ; Context-free grammar ; Representation (politics) ; Ontology ; Theoretical computer science ; Rule-based machine translation ; Chemistry ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Madhuri Suthar | Mansi Panchal | Disha Patel
Source: International Journal of Advanced Research (article, 2020-03-31)
Field: Chemistry — Organic Chemistry

SYNTHESIS OF ZNO NANOPARTICLES AND ITS APPLICATIONS Nowadays research on green chemistry in synthesis of nanoparticles with utilization of plants has engrossed a great attention. This study reports the exploit of aqueous leaf extract of Corriandrum Sativum Nanoparticle ; Nanotechnology ; Materials science ; Engineering Antimicrobial agents and applications ; Scientific and Engineering Research Topics
Authors: Olena Berdnikova | В.А. Костін | V.D. Poznyakov | O.A. Gaivoronskii | T.O. Alekseenko | I.I. Alekseenko
Source: The Paton Welding Journal (article, 2020-05-28)
Field: Materials Science — Materials Chemistry

Structure and crack resistance of special steels with 0.25−0.31 % carbon under the conditions of simulation of thermal cycles of welding The Paton Welding Journal, 2020, №05. International Scientific-Technical and Production Journal «The Paton Welding Journal» «The Paton Welding Journal» has been published monthly since 2000 in English, ISSN 0957-798X. «The Paton Welding Journal» is a cover-to-cover English translation of the «Avtomaticheskaya Svarka» (Automatic Welding) journal. The «Avtomaticheskaya Svarka» journal has been published monthly since 1948 in Russian, ISSN 005-111X. Materials science ; Welding ; Fracture toughness ; Carbon fibers ; Fracture (geology) ; Metallurgy ; Armour ; Carbon steel ; Composite material ; Toughness ; Corrosion Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Engineering and Environmental Studies
Authors: Kazi Md Hasanul Hoque | Afsana Sharmin
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-05-28)
Field: Materials Science — Polymers and Plastics

Recent Developments in Rapier Weaving Machines in Textiles <em>This</em> <em>paper</em> <em>reviews</em> <em>the</em> <em>recent</em> <em>developments</em> <em>in</em> <em>rapier</em> <em>weaving</em> <em>and</em> <em>addresses</em> <em>the</em> <em>challenges</em> <em>that</em> <em>face</em> <em>the</em> <em>weaving</em> <em>industry.</em> <em>The</em> <em>paper</em> <em>sheds</em> <em>the</em> <em>light</em> <em>on</em> <em>how</em> <em>the</em> <em>weaving</em> <em>machine</em> <em>manufacturers</em> <em>and</em> <em>woven</em> <em>fabric</em> <em>producers</em> <em>might</em> <em>strengthen</em> <em>the</em> <em>weaving</em> <em>industry</em> <em>by</em> <em>further</em> <em>advanced</em> <em>the</em> <em>rapier</em> <em>technology</em> <em>with</em> <em>the</em> <em>help</em> <em>of</em> <em>electro-mechanics,</em> <em>electronics,</em> <em>microprocessors,</em> <em>information</em> <em>technology</em> <em>and</em> <em>their</em> <em>application</em> <em>to</em> <em>the</em> <em>production</em> <em>of</em> <em>woven</em> <em>fabrics</em> Weaving ; Architectural engineering ; Engineering ; Mechanical engineering Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies
Authors: Gülden Koçak | Emine AKDEMİR
Source: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi (article, 2020-09-21)
Field: Chemistry — Organic Chemistry

Tiyosemikarbazit ve bis-1,2,4-triazol Türevlerinin Antikanser, Apoptotik ve Antibakteriyal Etkileri Bu çalışmada, çıkış maddesi [piridin-2,5-dikarbohidrazin (2)] ile tiyosemikarbazit türevleri [2,2’-(piridin-2,5-diildikarbonil-bis(N-etilhidrazinkarbotiyoamit) (3B) ve 2,2’-(piridin-2,5-diildikarbonil)-bis(N-(p-metoksifenil)hidrazinkarbotiyoamit (3C)] ve bu bileşiklerin halkalaşmasında oluşan bis-1,2,4-triazol türevlerinin [5,5-piridin-2,5-diilbis(4-etil-4H-1,2,4-triazol-3-tiyol) (4B) ve 5,5’-piridin-2,5diilbis[4-(4-metoksifenil)-4H-1,2,4-triazol-3-tiyol)] (4C)] in vitro ortamda prostat kanser hücre hattı (PC-3) üzerindeki antikanser ve apoptotik etkileri ile bazı gram pozitif ve gram negatif bakteriler üzerindeki antibakteriyel etkileri belirlenmiştir. Antikanser etki sonuçlarına göre tüm maddelerin artan konsantrasyonlara bağlı olarak canlı kanser hücre sayısında azalma tespit edilmiştir. Tüm uygulanan maddelerde en yüksek sitotoksik etkinin 500 μM konsantrasyonda olduğu belirlenmiştir. Tüm maddelerin apoptozu tetiklediği belirlenmiş olup apoptotik indeks oranlarına bakıldığında en düşük apoptotik indeks oranı 4B maddesi uygulandığında, en yüksek apoptotik indeks oranı ise 3C maddesi uygulandığında tespit edilmiştir. Antibakteriyel çalışmada tüm bileşiklerin gram pozitif bakteriler üzerinde gram negatiflere nazaran daha fazla etkili olduğu görülmüştür. Physics ; Molecular biology ; Gynecology ; Medicine ; Biology Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity ; Click Chemistry and Applications
Authors: Marko Morić | Igor Majnarić | Mario Barišić
Source: Cellulose Chemistry and Technology (article, 2020-02-20)
Field: Materials Science — Biomaterials

EFFECT OF CORONA POWER ON THE CMY REPRODUCTION QUALITY WITH ELECTROINK PRINTED ON FINE ART PAPER In order to achieve high-quality colour prints by using an electrophotography (EP) printing machine, it is necessary to prepare the printing substrate. Additional treatment may be needed to achieve better substrate surface tension for optimally attaching the liquid EP ink to the paper. To ensure good adhesion of the ink to the printing substrate, a corona treatment may be used. During the corona treatment, the surface of the paper is modified, and the ability of paper to accept the ink is improved. In this study, we will determine the possibility of realizing CMY prints and of achieving an increased range of tone value in EP digital printing. Based on the results, the new possible values of reproduced CMY tones will be determined, so that higher print quality can be achieved. The results show that the best colour reproduction is achieved without the use of corona power (0 W). The additional activation of 450 W corona will result in the greatest colour variation of E C = 0.60, E M = 0.60 and E Y = 0.48. Inkwell ; Substrate (aquarium) ; Corona (planetary geology) ; Surface tension ; Corona discharge ; Quality (philosophy) ; Chemistry ; Materials science ; Composite material ; Physics Advanced Cellulose Research Studies ; Electrowetting and Microfluidic Technologies ; Nanomaterials and Printing Technologies
Authors: Nitin Sharma | Aarti Verma | Rupali Sharma | Jaishiv Chauhan | Meenakshi Dahiya
Source: International Journal of Pharmaceutical Quality Assurance (article, 2020-09-25)
Field: Chemistry — Analytical Chemistry

Development and Validation of RP-HPLC Method using Photodiode Array or Diode-Array Detection Detector for simultaneous Estimation of the Amlodipine Besylate and Lisinopril in Fixed-Dose Formulation A basic and specific HPLC-PDA (high-performance liquid chromatography-photodiode array) detector showing approach was created for the simultaneous estimation of anti-hypertensive medications—amlodipine besylate (AMD) and lisinopril (LIS). A successful chromatography method was developed using the SunFire C8 column (4.6 × 150 mm, 5 μm) with gradient elution of the mobile phase composed of 85:25 potassium dihydrogen phosphate in water and methanol at a flow rate of 0.7 mL/min. The wavelength was set at 212 nm for the simultaneous estimation of AMD and LIS. The retention time obtained was 5.1 and 10.5 minutes for AMD and LIS, respectively. Descriptive performance of the proposed HPLC methodology was measurably approved as for framework accuracy, linearity, robustness, specificity, precision, and forced degradation for identification and evaluation limits. The linearity range for AMD and LIS correlation coefficient value obtained was R2 andgt; 0.999. The drugs were exposed to forced degradation conditions—acidic, alkali, oxidation, etc. The newly developed reverse phase high performance liquid chromatography (RP-HPLC) method was applied for the detection of the referred to anti-hypertensive medications in their combination pharmaceutical tablets. Amlodipine ; Chromatography ; High-performance liquid chromatography ; Photodiode ; Chromatography detector ; Lisinopril ; Chemistry ; Correlation coefficient ; Forced degradation ; Detector ; Elution ; Materials science ; Analytical Chemistry (journal) ; Ammonium acetate ; Computer science ; Medicine ; Optoelectronics Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Sensors ; Antibiotics Pharmacokinetics and Efficacy
Authors: N. S. Nesterov | A. I. Bykov
Source: MATEC Web of Conferences (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Locomotive Noise Reduction This paper presents the results of the investigation of the noise characteristics of the dual-voltage mainline electric locomotive. The study identified noise levels generated by the locomotive equipment that violate regulations. The analysis of points, where the noise is above regulations, indicated the primary source of the elevated noise is the operation of the traction transformer. We proposed installing a noise barrier below the locomotive body to reduce noise and bring the locomotive in compliance with the noise regulations. This solution allowed reducing the locomotive noise and ensure its compliance with regulations. Noise (video) ; Installation ; Noise reduction ; Automotive engineering ; Engineering ; Noise regulation ; Transformer ; Electrical engineering ; Traction (geology) ; Noise level ; Noise control ; Computer science ; Voltage ; Telecommunications ; Mechanical engineering ; Sound pressure Material Properties and Failure Mechanisms ; Railway Engineering and Dynamics ; Engineering Diagnostics and Reliability
Authors: Gabriel dos Passos Gomes | Robert Pollice | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2020-08-12)
Field: Materials Science — Materials Chemistry

Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning The ability to forge difficult chemical bonds through catalysis has transformed society on all fronts, from feeding our ever-growing populations to increasing our life-expectancies through the synthesis of new drugs. However, developing new chemical reactions and catalytic systems is a tedious task that requires tremendous discovery and optimization efforts. Over the past decade, advances in machine learning have revolutionized a whole new way to approach data- intensive problems, and many of these developments have started to enter chemistry. However, similar progress in the field of homogenous catalysis are only in their infancy. In this article, we want to outline our vision for the future of catalyst design and the role of machine learning to navigate this maze. Homogeneous ; Task (project management) ; Field (mathematics) ; Computer science ; Catalysis ; Artificial intelligence ; Data science ; Chemistry ; Engineering ; Systems engineering ; Organic chemistry ; Mathematics Machine Learning in Materials Science
Authors: Benjamin Rizkin | Albert Shkolnik | Neil Ferraro | Ryan L. Hartman
Source: ChemRxiv (preprint, 2020-01-08)
Field: Materials Science — Materials Chemistry

Combining Automated Microfluidic Experimentation with Machine Learning for Efficient Polymerization Design &lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;Understanding polymerization reactions has challenges relating to the complexity of the systems, hazards associated with the reagents, environmental footprint of the operations, and the highly non-linear topologies of reaction spaces. In this work, we aim to present a new methodology for studying such complex reactions using machine-learning-assisted automated microchemical reactors. A custom-designed rapidly prototyped microreactor is used in conjunction with in situ infrared thermography and efficient, high-speed experimentation to map the reaction space for a zirconocene polymerization catalyst. Chemical waste was decreased by two orders of magnitude and catalytic discovery was performed in one hour. Here we show that efficient microfluidic technology can be coupled with machine learning algorithms to obtain high-fidelity datasets on a complex chemical reaction.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Polymerization ; Microreactor ; Microfluidics ; Computer science ; Reagent ; Catalysis ; Fidelity ; Process engineering ; Nanotechnology ; Chemistry ; Materials science ; Polymer ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods