jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Angelo Frei | Mirco Scaccaglia | Sofia Fulgencio
Source: ChemRxiv (preprint, 2024-04-26)
Field: Chemistry — Organic Chemistry

Exploration of Rhenium Bisquinoline Tricarbonyl Complexes for their Antibacterial Properties Metal complexes have emerged as a promising source for novel classes of antibacterial agents to combat the rise of antimicrobial resistance around the world. In the exploration of the transition metal chemical space for novel metalloantibiotics, the rhenium tricarbonyl moiety has been identified as a promising scaffold. Here we have prepared eight novel rhenium bisquinoline tricarbonyl complexes and explored their antibacterial properties. Significant activity against both Gram-positive and Gram-negative bacteria was observed. However, all complexes also showed significant toxicity against human cells, putting into question the prospects of this compound class as metalloantibiotics. To better understand their biological effects, we conduct the first mode of action studies on rhenium bisquinoline complexes and show that they are able to form pores through bacterial membranes. Their straight-forward synthesis and tuneability suggests that further optimisation of this compound class could lead to compounds with enhanced bacterial specificity. Rhenium ; Chemistry ; Combinatorial chemistry ; Organic chemistry Synthesis of Tetrazole Derivatives ; Inorganic and Organometallic Chemistry ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Osamu Wada | Ramachari Doddoji | Chan‐Shan Yang | Takashi Uchino | Ci‐Ling Pan
Source: Journal of Applied Physics (article, 2024-02-27)
Field: Materials Science — Ceramics and Composites

Absorption dispersion below boson peak frequency in oxide glasses studied by THz-time domain spectroscopy The dispersion properties of the absorption coefficients [α(ν)] of different multi-component silicate oxide glasses have been studied in the frequency region below the boson peak by using THz-time-domain spectroscopy. The value of α(ν)/ν2 has been shown to exhibit a minimum level (R) at low frequency and subsequently a linear increase in the form of r(ν/νBP + E) with increasing frequency, where νBP is the boson peak frequency and R, r, and E are material-specific constants. It has also been found that R ∝ r and E is a constant common to most glasses. This α(ν)/ν2 behavior is ascribed to the dispersion property of the light-vibration coupling coefficient under the reasonable vibrational density of state function. The minimum (constant) and linear terms of the α(ν)/ν2 spectrum are originated from the physical/chemical disorder-induced charge fluctuations in the long-range scale (constant term) and short-range scale (linear term), respectively. The fluctuating charge (σ1) caused by uncorrelated, long-range disorders has primary significance for determining the sub-THz absorption dispersion properties, and its value has been determined for each glass material. Dispersion (optics) ; Spectroscopy ; Absorption spectroscopy ; Absorption (acoustics) ; Condensed matter physics ; Materials science ; Analytical Chemistry (journal) ; Molecular physics ; Atomic physics ; Physics ; Chemistry ; Optics ; Quantum mechanics Glass properties and applications ; Terahertz technology and applications ; Phase-change materials and chalcogenides
Authors: Xiaoyang Bi
Source: FARMACIA (article, 2024-04-29)
Field: Materials Science — Biomaterials

ANALYSIS OF MAGNETIC RESONANCE PROBE-BASED OXALIPLATIN-RESVERATROL DRUG RELEASE-CONTROLLED NANOPARTICLE THERAPY FOR COLORECTAL CANCER Oxaliplatin ; Resveratrol ; Colorectal cancer ; Medicine ; Magnetic resonance imaging ; Drug ; Materials science ; Nuclear magnetic resonance ; Oncology ; Cancer research ; Pharmacology ; Internal medicine ; Cancer ; Radiology ; Physics Nanoparticle-Based Drug Delivery
Authors: Valentina Erastova | Ivana Radosavljević Evans | William Nelson Glossop | Songül Güryel | Paul Hodgkinson | Hannah E. Kerr | Vasily S. Oganesyan | Lorna K. Softley | Helen M. Wickins | Mark R. Wilson
Source: ChemRxiv (preprint, 2024-02-28)
Field: Chemistry — Spectroscopy

Unravelling guest dynamics in crystalline molecular organics using solid-state NMR and molecular dynamics simulation Solid-state NMR and atomistic molecular dynamics (MD) simulations are used to understand the disorder of guest solvent molecules in two cocrystal solvates of the pharmaceutical furosemide. Traditional approaches to interpreting the NMR data fail to provide a coherent model of molecular behaviour and indeed give misleading kinetic data. In contrast, direct prediction of the NMR properties from MD simulation trajectories allows the NMR data to be correctly interpreted in terms of combined jump-type and libration-type motions. Time-independent component analysis of the MD trajectories provides additional insight, particularly for motions that are invisible to NMR. This allows a coherent picture of the dynamics of molecules restricted in molecular-sized cavities to be determined. Molecular dynamics ; Cocrystal ; Libration (molecule) ; Molecule ; Chemistry ; Solid-state nuclear magnetic resonance ; Chemical physics ; Computational chemistry ; Physics ; Nuclear magnetic resonance ; Hydrogen bond ; Mathematics ; Organic chemistry Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Muon and positron interactions and applications
Authors: Gergő Róth | Tibor Nagy | Ákos Kuki | Levente Novák | Dávid Nyul | Miklós Zsuga | Sándor Kéki
Source: Macromolecular Materials and Engineering (article, 2024-10-24)
Field: Chemistry — Organic Chemistry

Detailed Compositional and Structure–Property Analysis of Ethylene Oxide‐Propylene Oxide Triblock Copolymers Abstract The polyethylene oxide‐polypropylene oxide (PEO‐PPO) based triblock copolymers are notable amphiphilic copolymers with a diverse range of applications. The presence of homo‐, and/or diblock impurities in copolymers with PEO‐PPO‐PEO triblock has been demonstrated. This finding suggests that the samples are blends rather than pure triblock copolymers. Furthermore, copolymers with triblock copolymer content of 0 or less than 20% by molar percentage have been identified. The hydrophilic‐lipophilic balance (HLB) is also calculated based on the exact composition of the blends. The effect of HLB values and compositional data on the initial foam height (in casein solution), the surface tension, and the contact angle are investigated. The correlation coefficients for the PPO‐PEO‐PPO copolymers versus HLB values are found to be high, while those obtained for the PEO‐PPO‐PEO copolymers versus values of HLB are significantly lower. The lower correlation coefficients for the PEO‐PPO‐PEO samples can be attributed to the presence of homo‐ and diblock (co)polymer contaminants. In addition, a linear regression model has been constructed to find a mathematical relationship between the percentage of ethylene oxide, the average number of propylene oxide units, and the properties of the copolymer. Copolymer ; Materials science ; Ethylene oxide ; Propylene oxide ; Oxide ; Polypropylene ; Polymer chemistry ; Chemical engineering ; Polymer ; Composite material Advanced Polymer Synthesis and Characterization ; Asphalt Pavement Performance Evaluation ; Polymer crystallization and properties
Authors: M. Shanmuka Srinivas | Sangeeth Purushothaman
Source: Prabha Materials Science Letters (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Improving the Load Carrying Capacity through Asymmetric Spur Gear using WC-Fe Metal Matrix Composite for Automobile Applications Gears are the components that have been used for a century to transmit power from one place to another place. Symmetric spur gears are commonly used in the gearbox to transmit power between two parallel shafts. In order to increase the load carrying capacity of the spur gear with reduced noise and vibration, its profile can be modified. This can be achieved by changing the gear tooth design into asymmetric form. Asymmetrically toothed gears are used in power transmission for transmitting the power in one direction. These teeth help to improve the load carrying and power transmitting capacity of the gear. The strength can be further increased by changing the material used in gears. This can be made possible by using Metal Matrix Composite as a gear material. The metal matrix composites have high stiffness and strength to weight ratio compared to the Alloy Steel. The main objective of this project is to increase the load carrying capacity of the spur gear by using WC-Fe metal matrix composite material. The symmetric and asymmetric gear modeling has been done for existing car gear (Maruthi 800) using Ansys parametric design language software. The contact stress, bending stress and von Mises stress of the composite, as well as conventional gear material, is analyzed and the results are compared to find the percentage increase in strength of the gear. Composite number ; Spur gear ; Materials science ; Matrix (chemical analysis) ; Metal matrix composite ; Metal ; Composite material ; Spur ; Structural engineering ; Metallurgy ; Engineering Metal Alloys Wear and Properties ; Advanced materials and composites ; Advanced machining processes and optimization
Authors: Laura Mosebach | Shin‐Ichiro Ozawa | Muhammad Younas | Huidan Xue | Martin Scholz | Yuichiro Takahashi | Michael Hippler
Source: Preprints.org (preprint, 2024-04-16)
Field: Chemistry — Spectroscopy

Chemical Protein Crosslinking-Coupled Mass Spectrometry Reveals Interaction of LHCI with LHCII and LHCSR3 in C. reinhardtii The photosystem I (PSI) of the green alga Chlamydomonas reinhardtii associates with 10 light-harvesting proteins (LHCI) to form the PSI-LHCI complex. Binding of LHCII to PSI may also occur and is controlled by a process called state transitions. Hereby, two LHCII trimers bind to the PSAL, PSAH and PSAO side of PSI to produce the PSI-LHCI-LHCII complex. In this work, we took advantage of chemical crosslinking of proteins in conjunction with mass spectrometry to identify protein-protein interactions between the light-harvesting proteins of PSI and PSII. We detected binding of LHCBM proteins to the LHCA1-PSAG side of PSI and identified protein-protein interactions of LHCSR3 with LHCA5 and LHCA3. Our data indicate that the binding of LHCII to PSI is more versatile as anticipated and reveal that LHCSR3 likely regulates excitation energy transfer to the PSI core via LHCA5/LHCA3. Mass spectrometry ; Chemistry ; Biophysics ; Chromatography ; Biology Mass Spectrometry Techniques and Applications ; Enzyme Structure and Function ; Ion-surface interactions and analysis
Authors: Daniel Veras Corrêa | Fábio Rafael Echer | Lucas Prado Osco | Ana Paula Marques Ramos
Source: Colloquium Agrariae (article, 2024-04-15)
Field: Chemistry — Analytical Chemistry

MACHINE LEARNING INTEGRATION, REMOTE SENSING DATA PREPROCESSING TECHNIQUES TO MAP PESTS COTTON CROPS Cotton has a considerable economic impact on agribusiness. Strategies to reduce production loss due, for example, to pest attacks are increasingly required. Spodoptera frugiperda, known as fall armyworm, causes irreversible damage to cotton. In this context, a current approach is the use of hyperspectral measurements obtained by remote sensors and processed by machine learning algorithms. However, such measures generate data redundancy, making it difficult to extract information. An alternative is to apply pre-processing techniques, but little is known about the impact these generate on the learning ability of algorithms. This study evaluates the performance of machine learning algorithms in identifying cotton plants attacked by pests using pre-processed and raw hyperspectral measurements. Data are collected by EMBRAPA, and consist of hyperspectral measurements, in the range of 350-2500 nm, referring to eight days of collections in healthy cotton plants and attacked by S. frugiperda. Pre-processing techniques to try are baseline removal, smoothing, first and second order derivatives. A group of machine learning algorithms, such as Random Forest, Support Vector Machine, Extra Tree, was used to model pre-processed and non-pre-processed hyperspectral measurements. According to the proposed metric, the F-Score and the Extra Trees (ExT) algorithm performed better (0.77). So it overlapped the other results with the preprocessed dataset. In addition to obtaining the most important lengths for the algorithm to have its best performance. Concluding that machine learning with spectroscopy can help the field in a promising way. Studies in other crops and with other factors applied to the plant are recommended. Preprocessor ; Agronomy ; Crop protection ; Agricultural engineering ; Computer science ; Artificial intelligence ; Biology ; Machine learning ; Engineering Spectroscopy and Chemometric Analyses
Authors: Mercedes Bertotto | Hendrik de Villiers | Aneesh Chauhan | Esther Hogeveen-van Echtelt | Manon Mensink | Željana Grbović | Dimitrije Stefanović | Marko Panić | Sanja Brdar
Source: Journal of Near Infrared Spectroscopy (article, 2024-11-03)
Field: Chemistry — Analytical Chemistry

Predicting fungal infection sensitivity of sepals in harvested tomatoes using imaging spectroscopy and partial least squares discriminant analysis Tomatoes ( Solanum lycopersicum L.) are a widely grown and globally traded vegetable, essential for both local consumption and international trade. However, approximately 30% of harvested tomato yields are lost due to fungal decay during postharvest handling. Timely disease identification is crucial to prevent such losses, but certain tomato varieties exhibit higher susceptibility to fungal infections than others. Additionally, there are variations in susceptibility among individual sepals, with unknown underlying causes. Traditional methods for assessing fungal presence in plants have limitations, such as sample destruction and a focus on symptom detection rather than evaluating susceptibility to fungal infection. Hence, there is a demand need for an accurate, non-destructive method capable of predicting susceptibility to fungal infection. The use of hyperspectral imaging (HSI) with chemometrics presents a pioneering approach to address this need. In this study, three tomato cultivars (‘Brioso,’ ‘Cappricia,’ and ‘Provine’) were studied. Hyperspectral images were captured on day-1 of harvest, followed by controlled fungal growth conditions. Ground truth assessments were conducted by three experts on day-3 and day-4, averaging severity scores assigned per sepal. The methodology involved extracting spectra from HSI images and calibrating and validating models using partial least squares discriminant analysis (PLSDA), aiming to optimize model parameters for accurate predictions. The models were categorized into those developed using data from a single variety (intravariety) and those utilizing data from multiple varieties combined (global models). The best-performing intravariety model was established using the Cappricia variety, achieving a balanced accuracy of 0.84. Conversely, a global model combining Cappricia and Provine varieties achieved a balanced accuracy of 0.70. Overall, the results suggest that distinguishing between more and less susceptible sepals is feasible under controlled conditions. Partial least squares regression ; Linear discriminant analysis ; Sensitivity (control systems) ; Analytical Chemistry (journal) ; Spectroscopy ; Mathematics ; Pattern recognition (psychology) ; Biology ; Materials science ; Chemistry ; Statistics ; Chromatography ; Artificial intelligence ; Computer science ; Physics ; Engineering Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Plant Pathogens and Fungal Diseases
Authors: Yan Huo | Diyuan Guan | Dong Ling-yan
Source: Crystals (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

Online Defect Detection in LGA Crystallization Imaging Using MANet-Based Deep Learning Image Analysis In this paper, a MANet-based image detection approach is designed to inspect crystal defects during the cooling crystallization process, like that involving β-form L-glutamic acid (LGA), utilizing an online imaging device. The steps in the presented strategy encompass crystal image preprocessing, crystal image segmentation, and crystal classification. Firstly, the guided image filter is introduced to preprocess the collected crystallization images for offline training and online detection. Then, by using an image augmentation strategy to enlarge the number of crystal image samples for training, the MANet-based network is improved for crystal image segmentation. Accordingly, by defining some features, needle-like crystals can be categorized into four types with an efficient classifier for the detection of normal and defective crystals. The experimental results for the batch crystallization of β-form LGA are provided to illustrate the validity of the presented detection methodology. Artificial intelligence ; Preprocessor ; Crystallization ; Computer science ; Segmentation ; Image (mathematics) ; Image segmentation ; Image processing ; Pattern recognition (psychology) ; Crystal (programming language) ; Computer vision ; Materials science ; Engineering Crystallization and Solubility Studies ; Image Processing Techniques and Applications ; Advanced Chemical Sensor Technologies
Authors: Xiaolong Dong | Mingxin Zhang | Liying Zhang | Haipeng Ji
Source: Chinese Journal of Luminescence (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Mn&lt;sup&gt;4+&lt;/sup&gt;-activated Fluoride Phosphors with Dominant Photoluminescence Wavelength Longer than 631 nm 631 nm Mn 4+ Materials science ; Photoluminescence ; Phosphor ; Fluoride ; Wavelength ; Excitation wavelength ; Optoelectronics ; Inorganic chemistry ; Chemistry Luminescence Properties of Advanced Materials ; Inorganic Fluorides and Related Compounds ; Silicone and Siloxane Chemistry
Authors: Lucas Etourneau | Laura Fancello | Samuel Wieczorek | Nelle Varoquaux | Thomas Bürger
Source: Biostatistics (article, 2024-12-31)
Field: Chemistry — Spectroscopy

Penalized likelihood optimization for censored missing value imputation in proteomics Label-free bottom-up proteomics using mass spectrometry and liquid chromatography has long been established as one of the most popular high-throughput analysis workflows for proteome characterization. However, it produces data hindered by complex and heterogeneous missing values, which imputation has long remained problematic. To cope with this, we introduce Pirat, an algorithm that harnesses this challenge using an original likelihood maximization strategy. Notably, it models the instrument limit by learning a global censoring mechanism from the data available. Moreover, it estimates the covariance matrix between enzymatic cleavage products (ie peptides or precursor ions), while offering a natural way to integrate complementary transcriptomic information when multi-omic assays are available. Our benchmarking on several datasets covering a variety of experimental designs (number of samples, acquisition mode, missingness patterns, etc.) and using a variety of metrics (differential analysis ground truth or imputation errors) shows that Pirat outperforms all pre-existing imputation methods. Beyond the interest of Pirat as an imputation tool, these results pinpoint the need for a paradigm change in proteomics imputation, as most pre-existing strategies could be boosted by incorporating similar models to account for the instrument censorship or for the correlation structures, either grounded to the analytical pipeline or arising from a multi-omic approach. Imputation (statistics) ; Missing data ; Computer science ; Data mining ; Expectation–maximization algorithm ; Statistics ; Machine learning ; Maximum likelihood ; Mathematics Advanced Proteomics Techniques and Applications ; Gene expression and cancer classification ; Advanced Biosensing Techniques and Applications
Authors: Efthymios Oikonomou | Yannick Juli | Rajkumar Reddy Kolan | Lara Kern | Thomas Gruber | Christian Alzheimer | Patrick Krauß | Andreas Maier | Tobias Huth
Source: Communications Chemistry (article, 2024-11-30)
Field: Chemistry — Spectroscopy

A deep learning approach to real-time Markov modeling of ion channel gating The patch-clamp technique allows us to eavesdrop the gating behavior of individual ion channels with unprecedented temporal resolution. The signals arise from conformational changes of the channel protein as it makes rapid transitions between conducting and non-conducting states. However, unambiguous analysis of single-channel datasets is challenging given the inadvertently low signal-to-noise ratio as well as signal distortions caused by low-pass filtering. Ion channel kinetics are typically described using hidden Markov models (HMM), which allow conclusions on the inner workings of the protein. In this study, we present a Deep Learning approach for extracting models from single-channel recordings. Two-dimensional dwell-time histograms are computed from the idealized time series and are subsequently analyzed by two neural networks, that have been trained on simulated datasets, to determine the topology and the transition rates of the HMM. We show that this method is robust regarding noise and gating events beyond the corner frequency of the low-pass filter. In addition, we propose a method to evaluate the goodness of a predicted model by re-simulating the prediction. Finally, we tested the algorithm with data recorded on a patch-clamp setup. In principle, it meets the requirements for model extraction during an ongoing recording session in real-time. Computer science ; Gating ; Hidden Markov model ; Channel (broadcasting) ; SIGNAL (programming language) ; Noise (video) ; Dwell time ; Algorithm ; Artificial intelligence ; Speech recognition ; Pattern recognition (psychology) ; Markov model ; Filter (signal processing) ; Markov chain ; Machine learning ; Computer vision ; Image (mathematics) ; Telecommunications Mass Spectrometry Techniques and Applications ; Ion channel regulation and function ; Neural dynamics and brain function
Authors: Jessica Arcudia | Filiberto Ortíz‐Chi | Jorge Barroso | Gabriel Merino
Source: Nanoscale (article, 2024-11-25)
Field: Materials Science — Materials Chemistry

Comprehensive determination of highly symmetric transition metal dichalcogenide multilayers This study expands the JAM notation to systematically explore stacking configurations of transition metal dichalcogenide (TMDC) multilayers, covering both conventional and Janus structures. We extended JAM to represent four TMDC types: 1H, 1T, Janus 1H, and Janus 1T, adding characters to describe these structures. Additionally, we updated the JAM algorithm to generate stacking configurations and produce VASP-compatible POSCAR files. Using bilayer MoSSe as a model system, we analyzed stability, band gap, and interlayer interactions across all stackings. This expanded JAM approach enables detailed exploration of TMDC multilayers and supports further research into 2D materials. Transition metal ; Stacking ; Materials science ; Janus ; Nanotechnology ; Metal ; Chemistry ; Metallurgy ; Catalysis ; Organic chemistry 2D Materials and Applications