jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anshuman Swain | Narayan Datt Soni | Neil Wilson | Halvor Juul | Blake Benyard | Mohammad Haris | Dushyant Kumar | Ravi Prakash Reddy Nanga | John A. Detre | Virginia M. Lee | Ravinder Reddy
Source: Frontiers in Aging Neuroscience (article, 2023-10-09)
Field: Materials Science — Materials Chemistry

Early-stage mapping of macromolecular content in APPNL-F mouse model of Alzheimer’s disease using nuclear Overhauser effect MRI Non-invasive methods of detecting early-stage Alzheimer's disease (AD) can provide valuable insight into disease pathology, improving the diagnosis and treatment of AD. Nuclear Overhauser enhancement (NOE) MRI is a technique that provides image contrast sensitive to lipid and protein content in the brain. These macromolecules have been shown to be altered in Alzheimer's pathology, with early disruptions in cell membrane integrity and signaling pathways leading to the buildup of amyloid-beta plaques and neurofibrillary tangles. We used template-based analyzes of NOE MRI data and the characteristic Z-spectrum, with parameters optimized for increase specificity to NOE, to detect changes in lipids and proteins in an AD mouse model that recapitulates features of human AD. We find changes in NOE contrast in the hippocampus, hypothalamus, entorhinal cortex, and fimbria, with these changes likely attributed to disruptions in the phospholipid bilayer of cell membranes in both gray and white matter regions. This study suggests that NOE MRI may be a useful tool for monitoring early-stage changes in lipid-mediated metabolism in AD and other disorders with high spatial resolution. Entorhinal cortex ; White matter ; Neuroscience ; Chemistry ; Alzheimer's disease ; Pathology ; Hippocampus ; Magnetic resonance imaging ; Nuclear magnetic resonance ; Biology ; Disease ; Medicine Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Alzheimer's disease research and treatments
Authors: Garrett M. Ginell | Aidan J. Flynn | Alex S. Holehouse
Source: Bioinformatics (article, 2023-08-01)
Field: Chemistry — Spectroscopy

SHEPHARD: a modular and extensible software architecture for analyzing and annotating large protein datasets We provided SHEPHARD as both a stand-alone software package (https://github.com/holehouse-lab/shephard), and as a Google Colab notebook with a collection of precomputed proteome-wide annotations (https://github.com/holehouse-lab/shephard-colab). Computer science ; Python (programming language) ; Software ; Modular design ; Annotation ; Proteome ; Computational biology ; Data mining ; Programming language ; Bioinformatics ; Artificial intelligence ; Biology Advanced Proteomics Techniques and Applications ; Microbial Metabolic Engineering and Bioproduction ; Bioinformatics and Genomic Networks
Authors: Logan S. Richards | Maria D. Flores | Claudia Millán | Calina Glynn | Chih-Te Zee | M.R. Sawaya | Marcus Gallagher-Jones | Rafael J. Borges | Isabel Usón | José A. Rodríguez
Source: ACS Bio & Med Chem Au (article, 2023-02-23)
Field: Materials Science — Materials Chemistry

Fragment-Based <i>Ab Initio</i> Phasing of Peptidic Nanocrystals by MicroED Electron diffraction (MicroED/3DED) can render the three-dimensional atomic structures of molecules from previously unamenable samples. The approach has been particularly transformative for peptidic structures, where MicroED has revealed novel structures of naturally occurring peptides, synthetic protein fragments, and peptide-based natural products. Despite its transformative potential, MicroED is beholden to the crystallographic phase problem, which challenges its de novo determination of structures. ARCIMBOLDO, an automated, fragment-based approach to structure determination, eliminates the need for atomic resolution, instead enforcing stereochemical constraints through libraries of small model fragments, and discerning congruent motifs in solution space to ensure validation. This approach expands the reach of MicroED to presently inaccessible peptide structures including fragments of human amyloids, and yeast and mammalian prions. For electron diffraction, fragment-based phasing portends a more general phasing solution with limited model bias for a wider set of chemical structures. Fragment (logic) ; Phaser ; Angstrom ; Chemistry ; Nanotechnology ; Crystallography ; Materials science ; Physics ; Computer science ; Algorithm ; Optics Enzyme Structure and Function ; Peptidase Inhibition and Analysis ; RNA and protein synthesis mechanisms
Authors: Marwa Sabbar Falih | Ruba Fahmi Abbas | Neda Ibrahim Mahdi | Nisreen Kais Abood | Mohammed Jasim M. Hassan
Source: MethodsX (article, 2023-10-12)
Field: Chemistry — Analytical Chemistry

FIA- spectrophotometric method for the determination of amoxicillin in pharmaceuticals; application of AES, GAPI, and AGREE greenness assessment tools A new, simple, and sensitive FIA-spectrophotometric method has been developed for evaluating pure amoxicillin and pharmaceutical formulations. The FIA method involves the reaction of dapsone with sodium nitrite and hydrochloric acid. Subsequently, the diazotized dapsone is coupled with amoxicillin in an alkaline medium, resulting in a stable orange dye with a maximum wavelength of 440 nm. The developed method was validated according to the ICH guidelines and found to have a concentration range of 1-150 µg/mL, a correlation coefficient of 0.9994, a molar extinction coefficient of 0.273 × 10<sup>4</sup> L/mol.cm, and a detection limit of 0.074 µg/mL. The FIA method was then evaluated using AES, GAPI, and AGREES analytical greenness assessment tools. The FIA method uses dapsone as an eco-friendly reagent, in addition to the FIA method's advantages of reduced sample and reagent usage, reduced waste generation, and cheaper equipment. So, it has been proposed as an excellent eco-friendly method for the determination of AMX in pharmaceutical formulations. Phenolphthalein ; Reagent ; Molar absorptivity ; Chromatography ; Hydrochloric acid ; Dapsone ; Correlation coefficient ; Chemistry ; Detection limit ; Spectrophotometry ; Sodium dithionite ; Sodium nitrite ; Amoxicillin ; Mathematics ; Organic chemistry ; Medicine Analytical Methods in Pharmaceuticals ; Antibiotics Pharmacokinetics and Efficacy ; Analytical Chemistry and Sensors
Authors: Michael Jirásek | Abhishek Sharma | Jessica R. Bame | Mehr, S. Hessam M. | Nicola L. Bell | Stuart Marshall | Cole Mathis | Alasdair Macleod | Geoffrey J. T. Cooper | Marcel Swart | Rosa Mollfulleda | Leroy Cronin
Source: arXiv (Cornell University) (preprint, 2023-02-24)
Field: Chemistry — Spectroscopy

Determining Molecular Complexity using Assembly Theory and Spectroscopy Determining the complexity of molecules has important applications from molecular design to understanding the history of the process that led to the formation of the molecule. Currently, it is not possible to experimentally determine, without full structure elucidation, how complex a molecule is. Assembly Theory has been developed to quantify the complexity of a molecule by finding the shortest path to construct the molecule from building blocks, revealing its molecular assembly index (MA). In this study, we present an approach to rapidly and exhaustively calculate the MA of molecules from the spectroscopic measurements. We demonstrate that molecular complexity (MA) can be experimentally estimated using three independent techniques: nuclear magnetic resonance (NMR), tandem mass spectrometry (MS/MS), and infrared spectroscopy (IR), and these give consistent results with good correlations with the theoretically determined values from assembly theory. By identifying and analysing the number of absorbances in IR spectra, carbon resonances in NMR, or molecular fragments in tandem MS, the molecular assembly of an unknown molecule can be reliably estimated from experimental data. This represents the first experimentally quantifiable approach to defining molecular assembly, a reliable metric for complexity, as an intrinsic property of molecules and can also be performed on complex mixtures. This paves the way to use spectroscopic and spectrometric techniques to unambiguously detect alien life in the solar system, and beyond on exoplanets. Molecule ; Spectroscopy ; Chemistry ; Tandem ; Chemical physics ; Infrared spectroscopy ; Molecular model ; Nuclear magnetic resonance spectroscopy ; Computational chemistry ; Materials science ; Physics ; Stereochemistry ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Cephalopods and Marine Biology ; Astro and Planetary Science
Authors: Jiazheng Chen | Guobin Ma | Boxiang Gong | Chaoyong Deng | M. Zhang | Kaixin Guo | Ruirui Cui | Yunkai Wu | Menglan Lv | Xu Wang
Source: Nanomaterials (article, 2023-01-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bulk Photovoltaic Current Mechanisms in All-Inorganic Perovskite Multiferroic Materials After the discovery of bulk photovoltaic effect more than half a century ago, ferro-electrical and magneto-optical experiments have provided insights into various related topics, revealing above bandgap open voltages and non-central symmetrical current mechanisms. However, the nature of the photon-generated carriers responses and their microscopic mechanisms remain unclear. Here, all-inorganic perovskite Bi0.85Gd0.15Fe1-xMnxO3 thin films were prepared by a sol-gel process and the effects of Gd and Mn co-doped bismuth ferrites on their microtopography, grain boundries, multiferroic, and optical properties were studied. We discovered a simple "proof of principle" type new method that by one-step measuring the leakage current, one can demonstrate the value of photo generated current being the sum of ballistic current and shift current, which are combined to form the so-called bulk photovoltaic current, and can be related to the prototype intrinsic properties such as magneto-optical coupling and ferroelectric polarization. This result has significant potential influence on design principles for engineering multiferroic optoelectronic devices and future photovoltaic industry development. Multiferroics ; Materials science ; Ferroelectricity ; Photovoltaic system ; Photovoltaic effect ; Anomalous photovoltaic effect ; Perovskite (structure) ; Optoelectronics ; Band gap ; Thin film ; Polarization (electrochemistry) ; Current (fluid) ; Doping ; Engineering physics ; Voltage ; Nanotechnology ; Dielectric ; Electrical engineering ; Chemistry ; Physics Multiferroics and related materials ; Perovskite Materials and Applications ; 2D Materials and Applications
Authors: Aaron Scott | Erik Hartman | Johan Malmström | Lars Malmström
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-08-31)
Field: Chemistry — Spectroscopy

Explainable machine learning for the identification of proteome states via the data processing kitchen sink Abstract The application of machine learning algorithms to facilitate the understanding of changes in proteome states has emerged as a promising methodology in proteomics research. Unfortunately, these methods can prove difficult to interpret, as it may not be immediately obvious how models reach their predictions. We present the data processing kitchen sink (DPKS) which provides reproducible access to classic statistical methods and advanced explainable machine learning algorithms to build highly accurate and fully interpretable predictive models. In DPKS, explainable machine learning methods are used to calculate the importance of each protein towards the prediction of a model for a particular proteome state. The calculated importance of each protein can enable the identification of proteins that drive phenotypic change in a data-driven manner while classic techniques rely on arbitrary cutoffs that may exclude important features from consideration. DPKS is a free and open source Python package available at https://github.com/InfectionMedicineProteomics/DPKS . Python (programming language) ; Computer science ; Machine learning ; Artificial intelligence ; Proteome ; Identification (biology) ; Bioinformatics ; Programming language ; Biology Advanced Proteomics Techniques and Applications ; Explainable Artificial Intelligence (XAI) ; Metabolomics and Mass Spectrometry Studies
Authors: Hongchun Zhu | Zhiwei Lu | Chao Zhang | Yanrui Yang | Guocan Zhu | Yining Zhang | Haiying Liu
Source: Remote Sensing (article, 2023-09-08)
Field: Chemistry — Analytical Chemistry

Remote Sensing Classification of Offshore Seaweed Aquaculture Farms on Sample Dataset Amplification and Semantic Segmentation Model Satellite remote sensing provides an effective technical means for the precise extraction of information on aquacultural areas, which is of great significance in realizing the scientific supervision of the aquaculture industry. Existing optical remote sensing methods for the extraction of aquacultural area information mostly focus on the use of image spatial features and research on classification methods of single aquaculture patterns. Accordingly, the comprehensive utilization of a combination of spectral information and deep learning automatic recognition technology in the feature expression and discriminant extraction of aquaculture areas needs to be further explored. In this study, using Sentinel-2 remote sensing images, a method for the accurate extraction of different algae aquaculture zones combined with spectral information and deep learning technology was proposed for the characteristics of small samples, multidimensions, and complex water components in marine aquacultural areas. First, the feature expression ability of the aquaculture area target was enhanced through the calculation of the normalized difference aquaculture water index (NDAWI). Second, on this basis, the improved deep convolution generative adversarial network (DCGAN) algorithm was used to amplify the samples and create the NDAWI dataset. Finally, three semantic segmentation methods (UNet, DeepLabv3, and SegNet) were used to design models for classifying the algal aquaculture zones based on the sample amplified time series dataset and comprehensively compare the accuracy of the model classifications for achieving accurate extraction of different algal aquaculture information within the seawater aquaculture zones. The results show that the improved DCGAN amplification exhibited a better effect than the generative adversarial networks (GANs) and DCGAN under the indexes of structural similarity (SSIM) and peak signal-to-noise ratio (PSNR). The UNet classification model constructed on the basis of the improved DCGAN-amplified NDAWI dataset achieved better classification results (Lvshunkou: OA = 94.56%, kappa = 0.905; Jinzhou: OA = 94.68%, kappa = 0.913). The algorithmic model in this study provides a new method for the fine classification of marine aquaculture area information under small sample conditions. Aquaculture ; Computer science ; Artificial intelligence ; Segmentation ; Feature extraction ; Environmental science ; Remote sensing ; Deep learning ; Sample (material) ; Pattern recognition (psychology) ; Geography ; Fishery ; Fish <Actinopterygii> Spectroscopy and Chemometric Analyses ; Water Quality Monitoring Technologies
Authors: Allyn M. Xu | Lauren C. Tang | Marko Jovanović | Oded Regev
Source: Journal of the American Society for Mass Spectrometry (article, 2023-12-14)
Field: Chemistry — Spectroscopy

Uncovering Distinct Peptide Charging Behaviors in Electrospray Ionization Mass Spectrometry Using a Large-Scale Dataset Electrospray ionization is a powerful and prevalent technique used to ionize analytes in mass spectrometry. The distribution of charges that an analyte receives (charge state distribution, CSD) is an important consideration for interpreting mass spectra. However, due to an incomplete understanding of the ionization mechanism, the analyte properties that influence CSDs are not fully understood. Here, we employ a machine learning-based approach and analyze CSDs of hundreds of thousands of peptides. Interestingly, half of the peptides exhibit charges that differ from what one would naively expect (the number of basic sites). We find that these peptides can be classified into two regimes (undercharging and overcharging) and that these two regimes display markedly different charging characteristics. Notably, peptides in the overcharging regime show minimal dependence on basic site count, and more generally, the two regimes exhibit distinct sequence determinants. These findings highlight the rich ionization behavior of peptides and the potential of CSDs for enhancing peptide identification. Chemistry ; Analyte ; Electrospray ionization ; Ionization ; Mass spectrometry ; Peptide ; Electrospray ; Chromatography ; Analytical Chemistry (journal) ; Biological system ; Chemical physics ; Ion ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Akshay Ravish | Rashmi Shivakumar | Xi Zhang | Min Yang | Ji-Rui Yang | Ananda Swamynayaka | Omantheswara Nagaraja | M. Mahendra | Arunachalam Chinnathambi | Sulaiman Ali Alharbi | Vijay Pandey | Gautam Sethi | Kwang Seok Ahn | Peter E. Lobie | Basappa Basappa
Source: Bioengineering (article, 2023-01-24)
Field: Chemistry — Organic Chemistry

De Novo Design of Imidazopyridine-Tethered Pyrazolines That Target Phosphorylation of STAT3 in Human Breast Cancer Cells In breast cancer (BC), STAT3 is hyperactivated. This study explored the design of imidazopyridine-tethered pyrazolines as a de novo drug strategy for inhibiting STAT3 phosphorylation in human BC cells. This involved the synthesis and characterization of two series of compounds namely, 1-(3-(2,6-dimethylimidazo [1,2-a]pyridin-3-yl)-5-(3-nitrophenyl)-4,5-dihydro-1H-pyrazol-1-yl)-2-(4-(substituted)piperazin-1-yl)ethanone and N-substituted-3-(2,6-dimethylimidazo[1,2-a]pyridin-3-yl)-5-(3-nitrophenyl)-4,5-dihydro-1H-pyrazoline-1-carbothioamides. Compound <b>3f</b> with 2,3-dichlorophenyl substitution was recognized among the tested series as a lead structure that inhibited the viability of MCF-7 cells with an IC<sub>50</sub> value of 9.2 μM. A dose- and time-dependent inhibition of STAT3 phosphorylation at Tyr705 and Ser727 was observed in MCF-7 and T47D cells when compound <b>3f</b> was added in vitro. Calculations using density functional theory showed that the title compounds HOMOs and LUMOs are situated on imidazopyridine-pyrazoline and nitrophenyl rings, respectively. Hence, compound <b>3f</b> effectively inhibited STAT3 phosphorylation in MCF-7 and T47D cells, indicating that these structures may be an alternative synthon to target STAT3 signaling in BC. Imidazopyridine ; Pyrazoline ; Phosphorylation ; Chemistry ; In vitro ; Stereochemistry ; Cancer research ; Pharmacology ; Biochemistry ; Biology ; Medicinal chemistry Synthesis and biological activity ; Cytokine Signaling Pathways and Interactions ; Synthesis and Reactions of Organic Compounds
Authors: Ning Dai | Haiwei Jin | Kaixin Xu | Xudong Hu | Yanhong Yuan | Weimin Shi
Source: Applied Sciences (article, 2023-09-05)
Field: Materials Science — Polymers and Plastics

Prediction of Cotton Yarn Quality Based on Attention-GRU With the diversification of spinning order varieties and process parameters, the conventional method of determining production plans through trial spinning no longer satisfies the processing requirements of enterprises. Currently, deficiencies exist in predicting spinning quality relying on manual experience and traditional methods. The back propagation (BP) neural network within the realm of deep learning theory faces challenges in handling time series data, while the long short-term memory (LSTM) neural network, despite its intricate mechanism, exhibits an overall lower predictive accuracy. Consequently, a more precise predictive methodology is imperative to assist production personnel in efficiently ascertaining cotton-blending schemes and processing parameters, thereby elevating the production efficiency of the enterprise. In response to this challenge, we propose an attention-GRU-based cotton yarn quality prediction model. By employing the attention mechanism, the model is directed towards the input features most significantly impacting yarn quality. Real-world performance indicators of raw cotton and process parameters are utilized to predict yarn tensile strength. A comparative analysis is conducted against prediction results of BP, LSTM, and gated recurrent unit (GRU) neural networks that do not incorporate the attention mechanism. The outcomes reveal that the GRU model enhanced with the attention mechanism demonstrates reductions of 56.3%, 38.5%, and 36.4% in root mean square error (RMSE), along with 0.367%, 0.158%, and 0.190% in mean absolute percentage error (MAPE), respectively. The model attains a coefficient of determination R-squared of 0.954, indicating a high degree of fitness. This study underscores the potential of the proposed attention-GRU model in refining cotton yarn quality prediction and its consequential implications for process optimization and enhanced production efficiency within textile enterprises. Spinning ; Yarn ; Mean squared error ; Artificial neural network ; Artificial intelligence ; Mean absolute percentage error ; Computer science ; Diversification (marketing strategy) ; Production (economics) ; Machine learning ; Backpropagation ; Mathematics ; Engineering ; Statistics ; Mechanical engineering Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Dyeing and Modifying Textile Fibers
Authors: Sandeep Yadav | Shubham Sewariya | Ramesh Chandra | Prashant Singh | Ajay Kumar | Pallavi Jain | Shallu Sachdeva | Kamlesh Kumari
Source: Journal of Physical Organic Chemistry (article, 2023-03-06)
Field: Chemistry — Organic Chemistry

An investigation to understand the correlation between the experimental and density functional theory calculations of noscapine Abstract Noscapine has anti‐cancerous properties as it ceases cell division at metaphase stage. It is popular for it's anti‐antitussive properties. Various derivatives of noscapine have significant potential to act as a anticancerous ability than the parent molecule. As noscapine has low toxicity, high bioactivity and good availability, it is being explored extensively. Poor solubility of noscapine has somewhat restricted the use of noscapine. Therefore, authors aim to study the nature of noscapine in different solvents using computational methods or theoretically. Density functional theory (DFT) calculations and time‐dependent density functional theory (TD‐DFT) calculations were performed to explore the electronic structure, infrared spectrum, nuclear magnetic resonance spectrum and ultraviolet‐visible spectrum of noscapine. In dimethyl sulfoxide (DMSO), noscapine had a minimum or least free energy of −8.99 × 10 5 kcal/mol and a maximum dipole moment of 4.183 debye (D), followed by 2.942 D in chloroform. Physiochemical descriptors indicated that noscapine would interact with other molecules when DMSO or chloroform are taken as a solvent, as its polarizability and acidic character is more in DMSO and chloroform. The ability of noscapine to consume free radicles is also predicted to increase in DMSO. A red shift was observed when noscapine was taken in polar solvents as the energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) was reduced. The authors also performed linear regression analysis on infrared and carbon‐13 nuclear magnetic resonance ( 13 C‐NMR) spectra obtained from computational and experimental methods to check the correlation in their results. Noscapine ; Chemistry ; Density functional theory ; Computational chemistry ; Solvent effects ; Time-dependent density functional theory ; Solvent ; Organic chemistry ; Alkaloid Fullerene Chemistry and Applications ; Molecular Junctions and Nanostructures ; Organic Electronics and Photovoltaics