jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fatih Mehmet ATEŞ
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-11-08)
Field: Chemistry — Inorganic Chemistry

Use of Hypochlorous Acid in Infections: Bibliometric Analysis of Scientific Literature Abstract Background Historically, chemicals such as alcohols, aldehydes, chlorinated compounds and hypochlorous acid have been used to eliminate or inactivate infectious agents. Since bibliometrics is a powerful tool for uncovering scientific literature, we decided to conduct a bibliometric analysis of the literature on the use of hypochlorous acid in infections. Emerging trends and common patterns in research were explored, collaborations and networks were monitored and future directions were predicted. Methods The Scopus database was searched for documents published between 1982 and 2024 using the keywords “hypochlorous acid”, “infection” and “use”. Data analysis was performed using R-Studio 2024.09.0 Build 375 software with a machine learning bibliometric method based on the bibliometrix R package. The most relevant authors were measured by the number and fractional number of documents written. Author productivity was analyzed with Lotka’s law. Bradford’s law was applied to identify the core of journals focusing on the topic under consideration. The field of mainstream themes included isolated topics (niche themes), new topics (emerging themes), hot topics (engine themes) and core topics (core themes). Results A total of 574 documents were found, the vast majority of which were original articles (74%). The annual growth rate was 5.08%. Overall, 424 journals published more than one document. Most of the corresponding authors work in the United States. Neutrophils, animals and hydrogen peroxide are emerging themes, while hypochlorous acid, human, article represent the basic themes. Conclusions/Applications for Practice The interests of infection specialists have diversified over time and metabolism, reactive oxygen metabolites, anti-infective agents have been added. Hypochlorous acid ; Medicine ; Chemistry ; Organic chemistry Vanadium and Halogenation Chemistry ; Scientific Research and Studies ; Synthesis and Characterization of Heterocyclic Compounds
Source: Angewandte Chemie International Edition (article, 2024-12-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graphical Abstract: Angew. Chem. Int. Ed. 52/2024 In their Research Article (e202408435), Beat Fierz and co-workers show that the RING finger protein 168 (RNF168) is stimulated by di-ubiquitylated H1, which is produced by a site-specific click chemistry approach. INT ; Materials science ; Computer science ; Chemistry ; Programming language Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Axial and Atropisomeric Chirality Synthesis
Authors: Bachchu Lal | Aditya Kulkarni | Joseph McDermott | Rana Rais | Jesse Alt | Ying Wu | Hernando López-Bertoni | Sophie Sall | Umesh Kathad | Jianli Zhou | Barbara S. Slusher | Kishor Bhatia | John Laterra
Source:  (preprint, 2024-09-16)
Field: Materials Science — Biomaterials

Data from Preclinical efficacy of LP-184, a tumor site activated synthetic lethal therapeutic, in glioblastoma &lt;div&gt;Abstract&lt;p&gt;Purpose: Glioblastoma (GBM), is the most common brain malignancy with median survival &lt;2 yrs. Standard-of-care temozolomide (TMZ) has marginal efficacy in ~70% of patients due to &lt;i&gt;MGMT &lt;/i&gt;expression. LP-184 is an acylfulvene-derived prodrug activated by the oxidoreductase PTGR1 that alkylates at N&lt;sup&gt;3&lt;/sup&gt;-adenine, not repaired by MGMT. This paper examines LP-184 efficacy against preclinical GBM models and identifies molecular predictors of efficacy in clinical GBM. Experimental Design: LP-184 effects on GBM cell viability and DNA damage were determined using cell lines, primary patient-derived cells and neurospheres. GBM cell sensitivities to LP-184 relative to TMZ and MGMT expression were examined. Pharmacokinetics and CNS bioavailability were evaluated in mice with GBM xenografts. LP-184 effects on GBM xenograft growth and animal survival were determined. Machine learning, bioinformatic tools and clinical databases identified molecular predictors of LP-184 responsiveness. Results: LP-184 inhibited viability of multiple GBM cell isolates including TMZ-resistant and MGMT-expressing cells at IC&lt;sub&gt;50&lt;/sub&gt; = ~22-310 nM. Pharmacokinetics showed favorable AUC&lt;sub&gt;brain/plasma&lt;/sub&gt; and AUC&lt;sub&gt;tumor/plasma&lt;/sub&gt; ratios of 0.11 (brain C&lt;sub&gt;max&lt;/sub&gt;=839 nM) and 0.2 (tumor C&lt;sub&gt;max &lt;/sub&gt;= 2,530 nM), respectively. LP-184 induced regression of GBM xenografts and prolonged survival of mice bearing orthotopic xenografts. Bioinformatic analyses identified &lt;i&gt;PTGR1&lt;/i&gt; elevation in clinical GBM subtypes and associated LP-184 sensitivity with EGFR signaling, low nucleotide excision repair (NER) and low &lt;i&gt;ERCC3&lt;/i&gt; expression. Spironolactone, that induces ERCC3 degradation, decreased LP-184 IC&lt;sub&gt;50&lt;/sub&gt; 3-6 fold and enhanced GBM xenograft anti-tumor responses. Conclusions: These results establish LP-184 as a promising chemotherapeutic for GBM with enhanced efficacy in intrinsic or spironolactone-induced TC-NER deficient tumors.&lt;/p&gt;&lt;/div&gt; Glioblastoma ; Cancer research ; Medicine ; Oncology Nanoparticle-Based Drug Delivery ; Glioma Diagnosis and Treatment ; Radiopharmaceutical Chemistry and Applications
Authors: Dewei Wang | Jie Bao | Brian J. Koeppel | Christopher Coyle | Olga A. Marina
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Investigation of Solid Oxide Co2-Steam Co-Electrolysis System Performances with Multi-Physics Modeling and Deep Neural Network Electrolysis ; Artificial neural network ; High-temperature electrolysis ; Oxide ; Materials science ; Computer science ; Metallurgy ; Artificial intelligence ; Chemistry ; Physical chemistry ; Electrode Advancements in Solid Oxide Fuel Cells ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Xudong Chen | Anna Hartmann | Vanessa Winde | Constanze Neumann | M. Kim | Karina Targos | Dylan E. Holst | Dongying Wang | Zachary K. Wickens | Aldrin Antonio Agostinho Fernandes | Ramesh Giri | K. N. Houk | Dmitry Katayev | C Amini | Y Nurmi | D Zhang | Yixi Chen | Miao Hao | Yu Xia | Chunlei Wang | Panukorn Sombut | Lena Puntscher | Zdeněk Jakub | Matthias Meier | Jiří Pavelec | Rudolf Bliem | Matthias Schmid | Ulrike Diebold | Cesare Franchini | Gordon M. Parkinson | M Surup | M Brçnstrup | Henrik Terholsen | H Huerta-Zerón | C Mçller | H Junge | Matthias Beller | Lily Dongxia Xiao | Lu Liu | Fei Xie | J. Dong | Y Wang | Xun Xu | Wei Zhong | Haoyu Deng | X. R. Zhou | Shuang Li | Yu Song | Xia Liu | Manohar Salla | Xi Shi | Wanting Fu | W Wang | Min Gao | Shiyan Huang | X Wang | Xianjun Yu | Tianye Niu | Y Zhang | S. Wang | Minghui Han | Meng Ni | Qingyuan Wang | Hailin Zhang | Wei Xu | Hua Liu | Y. Hu | Z Wang | Ziheng Huang | Cheng‐Fu Huang | Jingxiang Lin | Chin‐Yuan Chang | Ailun Wang | Atanu Dey | Joohee Pradhan | S. Biswas | Fatemeh Rahimi | KP Biswas | Tapas Kumar Maji | H. Eric Xu | T Maji | H Xu | R Jouclas | S Laine | S Eliseeva | J Mandel | F Szeremeta | P Retailleau | J He | J.-F Gallard | A Pallier | C Bonnet | S Petoud | * Durand | * Tth ; Catalysis | J Zhu | H Xiang | H Chang | J Corcoran | R Ding
Source: Angewandte Chemie International Edition (article, 2024-04-09)
Field: Chemistry — Organic Chemistry

Graphical Abstract: Angew. Chem. Int. Ed. 16/2024 <em>Angewandte Chemie International Edition</em> is one of the prime chemistry journals in the world, publishing research articles, highlights, communications and reviews across all areas of chemistry. INT ; Chemistry ; Crystallography ; Nanotechnology ; Materials science ; Computer science ; Programming language Cyclopropane Reaction Mechanisms ; Organic Chemistry Cycloaddition Reactions ; Synthesis and Catalytic Reactions
Authors: Matthias Holzenkamp | Dongyu Lyu | Ulrich Kleinekathöfer | Peter Zaspel
Source: arXiv (Cornell University) (preprint, 2024-10-27)
Field: Materials Science — Materials Chemistry

Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials Uncertainty estimations for machine learning interatomic potentials (MLIPs) are crucial for quantifying model error and identifying informative training samples in active learning strategies. In this study, we evaluate uncertainty estimations of Gaussian process regression (GPR)-based MLIPs, including the predictive GPR standard deviation and ensemble-based uncertainties. We do this in terms of calibration and in terms of impact on model performance in an active learning scheme. We consider GPR models with Coulomb and Smooth Overlap of Atomic Positions (SOAP) representations as inputs to predict potential energy surfaces and excitation energies of molecules. Regarding calibration, we find that ensemble-based uncertainty estimations show already poor global calibration (e.g., averaged over the whole test set). In contrast, the GPR standard deviation shows good global calibration, but when grouping predictions by their uncertainty, we observe a systematical bias for predictions with high uncertainty. Although an increasing uncertainty correlates with an increasing bias, the bias is not captured quantitatively by the uncertainty. Therefore, the GPR standard deviation can be useful to identify predictions with a high bias and error but, without further knowledge, should not be interpreted as a quantitative measure for a potential error range. Selecting the samples with the highest GPR standard deviation from a fixed configuration space leads to a model that overemphasizes the borders of the configuration space represented in the fixed dataset. This may result in worse performance in more densely sampled areas but better generalization for extrapolation tasks. Kriging ; Gaussian process ; Regression ; Process (computing) ; Computer science ; Gaussian ; Machine learning ; Artificial intelligence ; Statistical physics ; Econometrics ; Statistics ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Fault Detection and Control Systems ; Gaussian Processes and Bayesian Inference
Authors: Tiep Nguyen Huu | Hae-Yong Jeong | Kyung-Doo Kim | Nguyen Xuan-Mung | Hoai-Nam Tran | Dao Nhu-Ngoc | Hoang Van-Khanh | Nguyen Ngoc Anh
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Enhancing Deep Learning Model Predictions of the Nuscale Lattice-Physics Parameters with a Combined-Sampling Algorithm Lattice (music) ; Statistical physics ; Algorithm ; Sampling (signal processing) ; Gibbs sampling ; Artificial intelligence ; Computer science ; Physics ; Optics Machine Learning in Materials Science ; Neural Networks and Applications ; Reservoir Engineering and Simulation Methods
Authors: Dan Yu | Zhichao Zhu | Qiuhui Sheng | Min Luo | Canglong Xing | Meng Zhang | Tao Fu | Wei Fan | Dongzhen Chen
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Organic Chemistry

Wearable Sensors Based on Textile Carrier for Multimodal Detection of Biomechanics and Biomolecules Wearable computer ; Textile ; Biomolecule ; Biomechanics ; Wearable technology ; Computer science ; Human–computer interaction ; Nanotechnology ; Engineering ; Materials science ; Embedded system ; Biology ; Anatomy Polydiacetylene-based materials and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Okechukwu D. Onukwuli | Ikechukwu A. Nnanwube | Fidelis Obidi Ochili | John Ifeanyi Obibuenyi
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Assessing the Efficiency of Danacid as Corrosion Inhibitor for Aluminium in HCl Medium: Experimental, Theoretical and Optimization Studies Aluminium ; Corrosion ; Corrosion inhibitor ; Metallurgy ; Materials science Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Magnesium Oxide Properties and Applications
Authors: Qi Wang | Jinzhu Lu | Yuanhong Wang | Kaiqian Peng | Zongmei Gao
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Phenotyping of Navel Orange Based on Hyperspectral Imaging Technology Hyperspectral imaging ; Navel orange ; Navel ; Computer science ; Artificial intelligence ; Biology ; Horticulture ; Anatomy Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Meat and Animal Product Quality
Authors: Johansson, Anders
Source: Digital Access to Scholarship at Harvard (DASH) (Harvard University) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Accelerating Atomistic Simulations with GPUs and Machine Learning The field of atomistic simulations is rapidly evolving. The simulations are becoming more reliable and accurate through the invention of ever more advanced ML potentials such as uncertainty aware and equivariant methods. Simultaneously, the leading GPU-based supercomputers are finally reaching the exascale, which paves the way for larger and longer simulations. This thesis revolves around the intersection of these advances and the acceleration of state-of-the-art ML potentials with modern GPUs on the world's largest supercomputers. More concretely, my focus has been the performance portability and scalability of FLARE and Allegro, two ML potentials near the opposite ends of the cost-accuracy trade-off. FLARE is a sparse Gaussian process potential that attempts to push the envelope for speed while maintaining reasonable accuracy. I have developed a Kokkos implementation of FLARE that outperformed previous state-of-the-art methods by 70\% on the second fastest supercomputer in the world, and recently reached one trillion atoms on Frontier, the fastest supercomputer. Allegro is an equivariant neural network implemented in PyTorch which sacrifices some speed to reach leading accuracy while maintaining scalability through its innovative architecture. Through the scalability and my LAMMPS interface, Allegro was able to efficiently utilize 5120 GPUs and reach relevant speeds for a wide range of biomoleular structures. Scalability ; Software portability ; Supercomputer ; Gaussian process ; Process (computing) ; Focus (optics) ; Artificial neural network ; Range (aeronautics) ; Acceleration Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Anna Hartmann | Ujjal Das | X Li | Ying‐Hui Fu | Nan Wei | Run Yu | Haq Nawaz Bhatti | Liang Zhang | Fangping Yan | Fangnan Xia | A. W. G. Ewing | Yi Long | Ying Yuan | Shiyuan Liu | Rui Zhang | Chao Wang | Jing Mao | Daniel Yuh Chao | Chen‐Yu Zhang | Shuang‐Nan Zhang | Guo Zhang | Zhi‐Zheng Wu | Shuxun Sang | J. P. Zheng | Qiang Gao | Bingru Huang | Fei Li | Kai Sun | S Chen | Yun Zheng | Xiaoliang Zhu | Min Jiang | Fu‐Hu Cao | Qiang You | X Chen | Cheng Peng | Jianhua Ma | Gang Luo | Shengyu Yan | Jian Zhang | Y Chen | Ni Chen | Z Wang | Wei Wei | Tsun‐Kong Sham | Meng Kuang | Guanjie Zheng | Jinling Wang | Jingfeng Huang | Muhammad Abdel‐Shakour | T Liu | X Wang | Y Pan | Liuping Wang | Yunpu Wang | Rong Wang | Yu Zhao | Shan Cao | Chong Li | Yaozu Wu | Li Ma | Yi Liu | Yiyu Yao | Yang Jiao | K. Zhang | Wei Meng | Changfu Yang | Guang Yang | Junfa Yang | Zhengyang Xu | Lin Yu | Binghe Wang | Rong Hu | Jiping Tang | Jingwei Lv | H. Xiao | Zhongfang Chen | X. H. Xie | Chenhao Jia | Qing Zhong | Qingmin Zhang | Dong Luo | Yuping Cao | Yiming Mu | Cheng Ren | Ying Hu | Cédric Hervieu | Esperanza Merino | Cristina Nevado | Cedric Uhlmann | Luca Münzfeld | Alan R. Hauser | Tingting Ruan | Senthil Kumar Kuppusamy | Chengzhi Jin | Mario Ruben | Karen Fink | Eufemio Moreno Pineda | Marwin Dialer | Kristian Witthaut
Source: Angewandte Chemie International Edition (article, 2024-04-15)
Field: Chemistry — Organic Chemistry

Graphical Abstract: Angew. Chem. Int. Ed. 17/2024 <em>Angewandte Chemie International Edition</em> is one of the prime chemistry journals in the world, publishing research articles, highlights, communications and reviews across all areas of chemistry. INT ; Chemistry ; Computer science ; Programming language Cyclopropane Reaction Mechanisms ; Magnetism in coordination complexes ; Organic Chemistry Cycloaddition Reactions
Source: Angewandte Chemie International Edition (article, 2024-11-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graphical Abstract: Angew. Chem. Int. Ed. 49/2024 In their Research Article (e202409295), Zhiqiang Mao, INT ; Chemistry ; Computer science ; Programming language Magnetism in coordination complexes ; Axial and Atropisomeric Chirality Synthesis ; Organic Chemistry Cycloaddition Reactions
Authors: So‐Youn Jung | Eun‐Gyeong Lee | Jai Hong Han | Seeyoun Lee | Soyeon Shin | Ju‐Yeon Lee | Sung-Soo Kim | Yumi Kim | Dong‐Young Noh
Source: Cancer Research (article, 2024-03-22)
Field: Chemistry — Spectroscopy

Abstract 2438: A retrospective pilot study to validate the utility of proteomic biomarkers in the blood for diagnosis of breast cancer Abstract Background: Previously, three-protein signature (Mastocheck®, Bertis Inc.) from plasma has been launched to overcome the limitation of low accuracy of mammography. In addition, nine-protein signature from serum was newly developed to improve the accuracy of three-protein signature as a blood-based diagnostic tool. This study aimed to validate the diagnostic utility for breast cancer of nine-protein signature with comparison with three-protein signature. Methods: A blood-based test using multiple reaction monitoring (MRM) by mass spectrometry to quantify nine proteins (APOC1, CHL1, FN1, VWF, PPBP, CLU, PRDX6, PRG4, and MMP9) in serum and three proteins (APOC1, CHL1, CA1; Mastocheck®) in plasma was investigated using blood of 30 health women and 30 breast cancer patients. Using the cut-off values confirmed by AI-based deep learning model, we evaluated the sensitivity, specificity, and accuracy of nine- and three-protein signature for detecting breast cancer. Results: Among 60 cases, the sensitivity and specificity of nine-protein signature was 87.9% and 80.7% for diagnosis of breast cancer. These values were 20% and 10% higher than those of three-protein signature, respectively (66.7% of sensitivity and 70.0% of specificity). The accuracy of nine-and three-protein signature was 83.3% and 68.3%. Conclusion: This study demonstrated that nine-protein signature could be a more accurate and effective tool for detecting breast cancer compare to three-protein signature). For a confirmatory study, we need to perform a large scaled validation study in future. Grants: This research was supported by Bertis, Republic of Korea (grant number: NCC 2341770-1) Citation Format: So-Youn Jung, Eun-Gyeong Lee, Jai Hong Han, Seeyoun Lee, Soyeon Shin, Ju Yeon Lee, Sung-Soo Kim, Yumi Kim, Dong-Young Noh. A retrospective pilot study to validate the utility of proteomic biomarkers in the blood for diagnosis of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2438. Cancer ; Medicine ; Breast cancer ; Retrospective cohort study ; Oncology ; Internal medicine ; Gynecology Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications
Source: Global NEST Journal (article, 2024-07-10)
Field: Chemistry — Organic Chemistry

Photocatalytic performance of Co3O4/Na-Alginate nanocomposite for the dynamic removal of direct red 31 dye: Degradation and mechanical pathways &lt;p&gt;This study examines the use of Co3O4 nanocomposite as a photo-catalyst in aqueous solution for photocatalytic degradation of dyes under UV. It has been observed that substrate concentrations, catalyst, pH, oxidant present and temperature, effect on dye degradation. The characterization of synthesized Co3O4 NPs and Co3O4/Na-Alg NC was done by UV-Visible, SEM, XRD and FTIR. Co3O4 NPs and Co3O4/Na-Alg NC were found to be 23 nm and 10 nm in size. The synthesized Co3O4/NaAlg NC were employed to degrade Direct Red 31 dye by optimization of experimental conditions. The highest degradation (68.24%) was attained at optimal conditions. TOC and COD were evaluated to check proficiency of this process. It was found that percentage reduction in TOC and COD was 66.7% and 65.2%. The generation of non-toxic products was validated by Direct Red 31 dye degradation route. The study concluded that the synthesized nanocomposites could be chosen for dyes-containing wastewater remediation. Because it is economical and environmentally beneficial, the produced Co3O4/Na-Alg NC is strongly advised for dye degradation.&lt;/p&gt; Nanocomposite ; Photocatalysis ; Degradation (telecommunications) ; Chemical engineering ; Chemistry ; Materials science ; Nanotechnology ; Catalysis ; Organic chemistry ; Computer science ; Telecommunications Nanomaterials for catalytic reactions
Authors: Nikhil Wilson | Ashwini Verma | Piyush Ranjan Maharana | Ameeya Bhusan Sahoo | Kavita Joshi
Source: ChemRxiv (preprint, 2024-04-22)
Field: Materials Science — Materials Chemistry

HyStor: An Experimental Database of Hydrogen Storage Properties for Various Metal Alloy Classes In this work, we introduce the HyStor database, consisting of 1280 metal alloys along with their hydrogen storage capacities (H2wt%) as a function of absorption temperature. Given the lack of updates in the existing open access HydPark database since 2002, we sourced compositions from recent research articles and various patent documents, resulting in a total of 468 compositions. The addition is reflected in the data across all existing classes of alloy compositions and low entropy alloys (LEA), medium entropy alloys (MEA), and high entropy alloys (HEA) have been included newly. This has broadened the scope of the database to encompass the latest materials of interest for hydrogen storage. HyStor contains representation of 54 elements, with a temperature range of 200-800K, and H2wt% range of 0.1-7.19. To ensure data quality, we conducted thorough checks for duplicate entries, erroneous data, and conflicting compositions within the database. Furthermore, we conducted multiple tests to identify potential outlier compositions by benchmarking the database against the pretrained HYST model on HydPark data. After eliminating these potential outliers, we successfully improved the error metrics of the HYST model, reducing the Mean Absolute Error (MAE) from 0.32 to 0.28 and increasing the R2 score from 0.78 to 0.82. We also tested individual classes and observed that the performance of the HYST model has increased for majority of the classes. Hydrogen storage ; Alloy ; Database ; Metal ; Materials science ; Computer science ; Metallurgy Hydrogen Storage and Materials
Authors: Jianhong Zhang | Min Wang | Keming Yang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-12-02)
Field: Chemistry — Analytical Chemistry

Identification of copper and lead pollution elements based on spectra of corn leaves in different leaf layers Chemistry ; Copper ; Identification (biology) ; Lead (geology) ; Pollution ; Lead pollution ; Environmental chemistry ; Botany ; Organic chemistry ; Ecology Heavy Metals in Plants ; Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis