jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fangyu Zhang | Zhengfeng Zhang | Enci Wang | Chengqi Wang | Benze Xiao
Source: Computational Biology and Bioinformatics (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

The Importance of Stiff Change of U(T) Content Around Splicing Sites in Efficient Plant Intron Splicing -- A Case Study in Rice Pre-mRNAs splicing is one of the fundamental process which generates multiple transcripts from a single gene, contributing to transcriptome and proteome diversity. AS is regulated by the cooperation of trans-factors and cis-elements. In plants, extensive alternative splicing occurs not only in tissue-specific manner but also in response to stress conditions. Intron retention is the most predominant splicing type. However, the cis-elements regulating intron retention are still ambiguous in plants, especially under environmental stresses. This study aimed to elucidate the cis-elements underlying intron retention in plants under adverse enrironments. Using RNA-seq data of rice cultivars IRAT109 and ZS97 under drought environments, we compared the sequence characteristics between constitutive and retained introns. The results show that the main AS types include intron retention (IR), alternative acceptor sites (AA), alternative donor sites (AD) and cassette exon (exon skipping, ES). Among of them, IR was the prevelent pattern with frequencies of 30.8-31.2%. Motif analysis of 5' and 3' 200bp intron sequences found rich U(T) in the motifs for both constitutive and retained introns. By further analysis of base composition of sequences flanking splice sites, we detected a notable difference in U(T) content between introns and their neighboring exons in constitutive introns, but not in retained introns. The results in this study suggested that the lack of significant changes in U(T) content between retained introns and neighboring exons might be a potential cis feature of intron retention. RNA splicing ; Intron ; Group II intron ; Biology ; Computational biology ; Genetics ; Gene ; RNA Metallurgy and Material Science
Authors: Josiel do Nascimento Cruz | Alexandre J. R. Serres | Raimundo C. S. Freire | Edson Guedes da Costa | George Victor Rocha Xavier | A. Oliveira | Vladimir Lopes de Souza | Pavlos I. Lazaridis
Source: Sensors (article, 2023-11-21)
Field: Materials Science — Materials Chemistry

The Practical Application of Bio-Inspired PMA for the Detection of Partial Discharges in High Voltage Equipment In this paper, the practical application of a bio-inspired antenna for partial discharge (PD) detection in high voltage equipment was evaluated in order to validate the efficiency of using this technology for PD monitoring purposes. For this, PD measurements using the bio-inspired antenna were performed on operational 69 kV potential transformers (PT) in a real substation. After the field experiment, laboratory measurements using the IEC 60270 standard method and a bio-inspired antenna were performed, simultaneously, over the evaluated PT. The results obtained at the substation indicated suspicious frequencies of partial discharge activity in two out of three evaluated potential transformers, mainly for the frequencies of 461 MHz, 1366 MHz, 1550 MHz and 1960 MHz. During the laboratory tests, the presence of partial discharge activity over the suspicious potential transformers was confirmed with the detection of PD apparent charge levels above 20 pC. Finally, the frequency spectrum obtained from the PD signals detected by the bio-inspired antenna in the laboratory presented similar frequency values to those obtained during the practical application at the substation, making it a promising indicator for future defect classification studies using artificial intelligence. Partial discharge ; Transformer ; Voltage ; High voltage ; Electrical engineering ; Antenna (radio) ; Computer science ; Engineering ; Electronic engineering High voltage insulation and dielectric phenomena ; Electrostatic Discharge in Electronics ; Power Transformer Diagnostics and Insulation
Authors: Teaya Yang | David Hathcock | Yuchao Chen | Paul L. McEuen | James P. Sethna | Itai Cohen | Itay Griniasty
Source: arXiv (Cornell University) (preprint, 2023-01-04)
Field: Materials Science — Materials Chemistry

Bifurcation instructed design of multistate machines We propose a novel design paradigm for multistate machines where transitions from one state to another are organized by bifurcations of multiple equilibria of the energy landscape describing the collective interactions of the machine components. This design paradigm is attractive since, near bifurcations, small variations in a few control parameters can result in large changes to the system's state providing an emergent lever mechanism. Further, the topological configuration of transitions between states near such bifurcations ensures robust operation, making the machine less sensitive to fabrication errors and noise. To design such machines, we develop and implement a new efficient algorithm that searches for interactions between the machine components that give rise to energy landscapes with these bifurcation structures. We demonstrate a proof of concept for this approach by designing magneto elastic machines whose motions are primarily guided by their magnetic energy landscapes and show that by operating near bifurcations we can achieve multiple transition pathways between states. This proof of concept demonstration illustrates the power of this approach, which could be especially useful for soft robotics and at the microscale where typical macroscale designs are difficult to implement. Computer science ; Bifurcation ; Microscale chemistry ; Robotics ; Energy (signal processing) ; Soft robotics ; Dynamical systems theory ; Finite-state machine ; Topology (electrical circuits) ; Mechanism (biology) ; State (computer science) ; Control engineering ; Artificial intelligence ; Control theory (sociology) ; Robot ; Control (management) ; Algorithm ; Engineering ; Mathematics ; Physics ; Nonlinear system Shape Memory Alloy Transformations ; Advanced Materials and Mechanics ; Micro and Nano Robotics
Authors: Samuel Talkington | Santiago Grijalva
Source:  (article, 2023-06-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase Retrieval via Model-Free Power Flow Jacobian Recovery Phase retrieval is a prevalent problem in digital signal processing and experimental physics that consists of estimating a complex signal from magnitude measurements. This paper expands the classical phase retrieval framework to electric power systems with unknown network models and limited access to observations of voltage magnitudes, active power injections, and reactive power injections. The proposed method recovers the phase angles and the power-phase angle submatrices of the AC power flow Jacobian matrix. This is made possible by deriving topology and parameter-free expressions for the structural symmetries of the power flow Jacobian that do not depend on the phase angles. These physical laws provide structural constraints for the proposed phase retrieval method. The paper then presents sufficient conditions for guaranteed recovery of the voltage phase angles, which also depend solely on voltage magnitudes, active power injections, and reactive power injections. The method offers two significant benefits: both estimating the voltage phase angles and recovering the power flow Jacobian matrix—a basis for approximating the power flow equations. Simulations on widely studied open-source test networks validate the findings. Jacobian matrix and determinant ; AC power ; Power (physics) ; Voltage ; Control theory (sociology) ; Three-phase ; Computer science ; Flow (mathematics) ; Power-flow study ; Topology (electrical circuits) ; Mathematics ; Algorithm ; Applied mathematics ; Physics ; Geometry ; Engineering ; Electrical engineering ; Artificial intelligence Magnetic Properties and Applications ; Advanced X-ray Imaging Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Junki Kim | Hyungyong Lee | Tongjoo Suh
Source: Horticultural Science and Technology (article, 2023-08-31)
Field: Chemistry — Analytical Chemistry

Analysis of Predictions of Garlic Bulb Weight using LASSO Regression Model As part of a preemptive response to ensure a stable supply and demand policy for garlic (Allium sativum L.), the Korean government strives to determine the final yield of garlic by observing its growth status by season. In particular, estimation of the bulb weight of garlic is one of the main steps in estimating garlic production, conducting observational work on spherical weight prediction is getting more important is getting more important. The observation data on garlic can be viewed as data for predicting the final weight, but since root growth occurs intensively at a specific time, there is a limit to predicting the final bulb weight only by the bulb weight corresponding to each observation time point. In addition, since the factors affecting the final bulb weight of garlic may be different for each growing stage, an estimation is required for each stage to predict the final bulb weight at each time point. In this study, garlic bulb weight estimation analysis, which reflects the characteristics of Korean garlic grown in an outfield, is performed using growth survey data. A stage-by-stage model for predicting the bulb weight of garlic at each time point is established, based on the fact that the factors influencing the bulb weight of garlic may differ according to the growth stage. In the process, LASSO regression is used for variable selection and coefficient estimation of the bulb weight of garlic. As a result of the estimation, significant factors regarding growth, lot and environment were identified stage by stage, providing indicators for forecasting final bulb weight of garlic at each stage. Allium sativum ; Bulb ; Linear regression ; Dry-bulb temperature ; Statistics ; Regression analysis ; Mathematics ; Biotechnology ; Biology ; Botany ; Meteorology ; Geography Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Plant Physiology and Cultivation Studies
Authors: Sebastian Deorowicz | Adam Gudyś
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-07-22)
Field: Materials Science — Materials Chemistry

Efficient protein structure archiving using ProteStAr Abstract Motivation The introduction of Deep Minds’ Alpha Fold 2 enabled prediction of protein structures at unprecedented scale. AlphaFold Protein Structure Database and ESM Metagenomic Atlas contain hundreds of millions of structures stored in CIF and/or PDB formats. When compressed with a general-purpose utility like gzip, this translates to tens of terabytes of data which hinders the effective use of predicted structures in large-scale analyses. Results Here, we present ProteStAr, a compressor dedicated to CIF/PDB as well as, supplementary PAE files. Its main contribution is a novel approach to predict atom coordinates on the basis of the previously analyzed atoms. This allows efficient encoding of the coordinates which are the largest component of the protein structure files. By default, the compression is lossless, though the lossy mode with a controlled maximum error of coordinates reconstruction is also present. Compared to the competing packages, i.e., BinaryCIF, Foldcomp, PDC, our approach offers superior compression ratio at established reconstruction accuracy. By the efficient use of threads at both compression and decompression stages, the algorithm takes advantage of multicore architecture of current central processing units and operates with speeds about 1 GB/s. The presence of C++ API further increases the usability of the presented method. Availability and implementation The source code of ProteStAr is available at https://github.com/refresh-bio/protestar . Computer science ; Lossless compression ; Lossy compression ; Encoder ; Terabyte ; Computational science ; Data compression ; Code (set theory) ; Data structure ; Source code ; Data mining ; Parallel computing ; Algorithm ; Computer engineering ; Artificial intelligence ; Operating system ; Programming language Enzyme Structure and Function ; Genomics and Phylogenetic Studies ; Protein Structure and Dynamics
Authors: Anushna Bhattacharya | Vikas Tiwari | Tarak Karmakar
Source: arXiv (Cornell University) (preprint, 2023-12-07)
Field: Materials Science — Materials Chemistry

Electrostatic-driven Self-assembly of Janus-like Monolayer-protected Metal Nanoclusters The generation of controlled microstructures of functionalized nanoparticles has been one of the crucial challenges in nanoscience and nanotechnology. Efforts have been made to tune ligand charge states that can affect the aggregation propensity and modulate the self-assembled structures. In this work, we modelled zwitterionic Janus-like monolayer ligand-protected metal nanoclusters (J-MPCs) and studied their self-assembly using atomistic molecular dynamics and advanced enhanced sampling simulations. The oppositely-charged ligands functionalization on two hemispheres of a J-MPC elicits asymmetric solvation, primarily driven by distinctive hydrogen bonding patterns in the ligand-solvent interactions. Electrostatic interactions between the oppositely charged residues in J-MPCs guide the formation of one-dimensional and ring-like self-assembled superstructures with molecular dipoles oriented in specific patterns. The pertinent atomistic insights into the intermolecular interactions governing the self-assembled structures of zwitterionic J-MPCs obtained from this work can be used to design a general strategy to create tunable microstructures of charged MPCs. Nanoclusters ; Janus ; Chemical physics ; Intermolecular force ; Solvation ; Nanotechnology ; Electrostatics ; Self-assembly ; Monolayer ; Materials science ; Ligand (biochemistry) ; Molecular dynamics ; Hydrogen bond ; Janus particles ; Dipole ; Chemistry ; Computational chemistry ; Solvent ; Molecule ; Physical chemistry Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Theoretical and Computational Physics
Authors: Hiroki Morita | Shun Chida | Masaki Takato | Keiko Kondo | Masato Katahira | Luisa Brazão Simao | Minoru Takeda
Source: Journal of Applied Microbiology (article, 2023-08-30)
Field: Materials Science — Biomaterials

Enzymatic degradation of glucosaminoglucan and cellulase resistance of cellulose nanofiber coated with glucosaminoglucan The chitosanase and cellulase hydrolyzed the β-1,4-linkage from Glc to GlcN and were expected to recognize the tetramer and hexamer units of GG depending on their final products. The cellulose nanofibers acquired cellulase resistance via amination with GG, probably because of the lower activity of cellulase to GG than cellulose. Cellulase ; Cellulose ; Degradation (telecommunications) ; Nanofiber ; Chemistry ; Enzyme ; Biochemistry ; Microbiology ; Materials science ; Biology ; Nanotechnology Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; Biofuel production and bioconversion
Authors: Pallavi Kumari | P. Ballone | Candelas Paniagua | Radwa H. Abou‐Saleh | Yoselin Benitez‐Alfonso
Source: ChemRxiv (preprint, 2023-11-28)
Field: Materials Science — Biomaterials

Cellulose-Callose Hydrogels: a Computational Exploration of their Nanostructure and Mechanical Properties Polysaccharides play a crucial role in virtually all living systems. They also represent the biocompatible and fully sustainable component of a variety of nanoparticles, which are of increasing interest in biomedicine, food processing, structural reinforcement of polymeric materials and cosmetics. The computational modelling of complex polysaccharide phases will assist in understanding the properties and behaviour of all these systems. In this paper, structural, bonding and mechanical properties of $10$ wt\% cellulose-callose hydrogels ($\beta$-glucans co-existing in plant cell walls) were investigated by atomistic simulations. Systems of this kind have recently been introduced in experiments revealing unexpected interactions between the polysaccharides. Starting from initial configurations inspired by X-ray diffraction data, atomistic models made of $\sim 1.6 \times 10^6$ atoms provide a qualitatively consistent view of these hydrogels, displaying stability, homogeneity, connectivity and elastic properties beyond those of a liquid suspension. The simulation shows that the relatively homogeneous distribution of saccharide nanofibres and chains in water is not due to the solubility of cellulose and callose, but to the formation of a number of cross-links among the various sample components. The broad distribution of strength and elasticity among the links implies a degree of anharmonicity and irreversible deformation already evident at low external load. Besides the qualitative agreement with experimental observations, the simulation results display also quantitative disagreements in the estimation of elastic coefficients such as the Young's modulus that require further investigation. Complementary simulations of dense cellulose-callose mixtures (no hydrogels) highlight the role of callose in smoothing the contact surface of different nanofibres forming larger bundles. Cellulose-callose structures in these systems displayed an enhanced water uptake and delayed dye release when compared to cellulose alone, highlighting potential new applications as drug delivery scaffolds. The simulation trajectories provide a tuning and testing ground for the development of coarse-grained models that are required for the large scale investigation of mechanical properties of cellulose and callose mixtures in a watery environment. Self-healing hydrogels ; Cellulose ; Materials science ; Crystallinity ; Chemical engineering ; Composite material ; Polysaccharide ; Molecular dynamics ; Nanotechnology ; Biological system ; Polymer chemistry ; Chemistry ; Computational chemistry ; Organic chemistry Advanced Cellulose Research Studies ; Polysaccharides Composition and Applications ; Advanced Materials and Mechanics
Authors: Ambroise de Izarra | François‐Xavier Coudert | Alain H. Fuchs | Anne Boutin
Source: ChemRxiv (preprint, 2023-11-06)
Field: Chemistry — Inorganic Chemistry

Predictive Thermodynamic Model for Intrusion of Electrolyte Aqueous Solutions in Nanoporous Materials We performed Monte Carlo simulations in the osmotic ensemble in three representative hypothetical pure-silica zeolites that are silicalite-1, chabazite and faujasite for which adsorption isotherm of water has been calculated from grand canonical Monte Carlo simulations. Monte Carlo moves in the osmotic ensemble to insert electrolyte reveal that ions do not penetrate the zeosils at water intrusion pressure because they are better solvated in the bulk for low-diameter pore zeosils, which is associated to a lower ion solvation. Then, Chemical potential of water in electrolyte solutions has been calculated and highlight the same dependency in pressure as the chemical potential of pure water. From these conclusions, we propose a thermodynamic model that revisit the law of osmotic pressure in order to predict the intrusion pressure in zeosils taking account of the nature of electrolytes in solution. Electrolyte ; Monte Carlo method ; Solvation ; Thermodynamics ; Osmotic coefficient ; Aqueous solution ; Chemistry ; Nanoporous ; Water model ; Materials science ; Ion ; Molecular dynamics ; Activity coefficient ; Physical chemistry ; Physics ; Computational chemistry ; Nanotechnology ; Mathematics Zeolite Catalysis and Synthesis ; Nanopore and Nanochannel Transport Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Maria H. Rasmussen | Julius Seumer | Jan H. Jensen
Source: Angewandte Chemie (article, 2023-10-13)
Field: Materials Science — Materials Chemistry

Toward De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates for Alcohol‐Mediated Morita–Baylis–Hillman Reactions** Abstract Recently we have demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover a new and efficient catalyst for the alcohol‐mediated Morita–Baylis–Hillman (MBH) reaction. In particular, the discovered catalyst was shown experimentally to be eight times more active than DABCO, commonly used to catalyze the MBH reaction. This represents a breakthrough in using generative models for catalyst optimization. However, the GA procedure, and hence discovery, relied on two important pieces of information; 1) the knowledge that tertiary amines catalyze the reaction and 2) the mechanism and reaction profile for the catalyzed reaction, in particular the transition state structure of the rate‐determining step. Thus, truly de novo catalyst discovery must include these steps. Here we present such a method for discovering catalyst candidates for a specific reaction while simultaneously proposing a mechanism for the catalyzed reaction. We show that tertiary amines and phosphines are potential catalysts for the MBH reaction by screening 11 molecular templates representing common functional groups. The method relies on an automated reaction discovery workflow using meta‐dynamics calculations. Combining this method for catalyst candidate discovery with our GA‐based catalyst optimization method results in an algorithm for truly de novo catalyst discovery. Catalysis ; DABCO ; Chemistry ; Combinatorial chemistry ; Alcohol ; Drug discovery ; Reaction mechanism ; Organic chemistry Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Changsheng Ma | Qiang Yang | Shangsong Liang | Xin Gao
Source:  (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

A Distribution Preserving Model for Molecular Graph Generation Generating molecular graphs using deep graph generative models is a challenging task that involves optimizing a given target within an enormous search space while adhering to chemical valence rules. Despite promising results, existing models mainly focus on learning molecular graph structures at the individual level while ignoring inter-molecular relationships regarding molecular characterization features and molecular activity. This can lead to the generation of molecules that are unresponsive to their true neighbors possessing similar characterization features, resulting in a divergence between the learned generation distribution and the actual molecular distribution. In this paper, we propose a distribution preserving model, designed to maintain the inter-molecular relationships of the original distribution within the generated space. Specifically, the model operates on a student-teacher paradigm, where the teacher module learns the inter-molecular relationship dynamics of the original distribution, and imparts this knowledge to the student module, which is responsible for generating molecules. Experimental results on two benchmark datasets demonstrate that our model outperforms the state-of-the-art models in generating valid, novel and unique molecules. Moreover, our model is verified on preserving molecule distribution in the generation space. Computer science ; Molecular graph ; Generative model ; Graph ; Molecular dynamics ; Theoretical computer science ; Benchmark (surveying) ; Chemical space ; Generative grammar ; Artificial intelligence ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling