jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yongle Chen | Zhuokun Du | Hongxian Zhao | Wei Fang | Ying Liu | Yangjun Zhang | Wanjun Zhang | Weijie Qin
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-06-30)
Field: Chemistry — Spectroscopy

SPPUSM: An MS/MS spectra merging strategy for improved low-input and single-cell proteome identification Abstract Single and rare cell analysis provides unique insights into the investigation of biological processes and disease progress by resolving the cellular heterogeneity that is masked by bulk measurements. Although many efforts have been made, the techniques used to measure the proteome in trace amounts of samples or in single cells still lag behind those for DNA and RNA due to the inherent non-amplifiable nature of proteins and the sensitivity limitation of current mass spectrometry. Here, we report an MS/MS spectra merging strategy termed SPPUSM (same precursor-produced unidentified spectra merging) for improved low-input and single-cell proteome data analysis. In this method, all the unidentified MS/MS spectra from multiple test files are first extracted. Then, the corresponding MS/MS spectra produced by the same precursor ion from different files are matched according to their precursor mass and retention time (RT) and are merged into one new spectrum. The newly merged spectra with more fragment ions are next searched against the database to increase the MS/MS spectra identification and proteome coverage. Further improvement can be achieved by increasing the number of test files and spectra to be merged. Up to 18.2% improvement in protein identification was achieved for 1 ng HeLa peptides by SPPUSM. Reliability evaluation by the “entrapment database” strategy using merged spectra from human and E. coli revealed a marginal error rate for the proposed method. For application in single cell proteome (SCP) study, identification enhancement of 28%-61% was achieved for proteins for different SCP data. Furthermore, a lower abundance was found for the SPPUSM-identified peptides, indicating its potential for more sensitive low sample input and SCP studies. Proteome ; Mass spectrometry ; Mass spectrum ; Identification (biology) ; Chemistry ; Computational biology ; Spectral line ; Human proteome project ; Analytical Chemistry (journal) ; Computer science ; Chromatography ; Proteomics ; Biology ; Biochemistry ; Physics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Valentin Wesp | Lukas Scholz | Janine M. Ziermann | Stefan Schuster | Heiko Stark
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-08-27)
Field: Materials Science — Biomaterials

Constructing networks for comparison of collagen types Abstract Collagens are structural proteins that are predominantly found in the extracellular matrix of multicellular animals, where they are mainly responsible for the stability and structural integrity of various tissues. All collagens contain polypeptide strands (ɑ-chains). There are several types of collagens, some of which differ significantly in form, function, and tissue specificity. Because of their importance in clinical research, they are grouped into subdivisions, the so-called collagen families, and their sequences are often analysed. However, problems arise with highly homologous sequence segments. To increase the accuracy of collagen classification and prediction of their functions, the structure of these collagens and their expression in different tissues could result in a better focus on sequence segments of interest. Here, we analyse collagen families with different levels of conservation. As a result, clusters with high interconnectivity can be found, such as the fibrillar collagens, the COL4 network-forming collagens, and the COL9 FACITs. Furthermore, a large cluster between network-forming, FACIT, and COL28a1 ɑ-chains is formed with COL6a3 as a major hub node. The formation of clusters also signifies, why it is important to always analyse the ɑ-chains and why structural changes can have a wide range of effects on the body. Extracellular matrix ; Multicellular organism ; Interconnectivity ; Sequence (biology) ; Function (biology) ; Node (physics) ; Chemistry ; Cell biology ; Computational biology ; Biology ; Evolutionary biology ; Cell ; Biochemistry ; Computer science ; Artificial intelligence Collagen: Extraction and Characterization ; Cell Adhesion Molecules Research ; Osteoarthritis Treatment and Mechanisms
Authors: Shuhui Bi | Xue Li | Lisha Chen | Xinhua Qu | Tao Shen | Liyao Ma
Source: Research Square (preprint, 2023-07-25)
Field: Chemistry — Analytical Chemistry

Evidential apple classification model based on combination of Gaussian mass functions Abstract The establishment of stable and highly precise prediction models is of great theoretical and practical significance for the research of the nondestructive testing of the internal quality of fruits with NIR technology. In this paper, focused on Yantai red Fuji apples, probabilistic neural networks (PNN) and partial least squares (PLS) prediction models are developed based on sample data of apple NIR spectra and soluble solid content (SSC). In order to solve the problems of poor adaptability, model instability and uncertainty caused by the hard partitioning of classification in the current research, the two models established in this paper are fused using DS evidence theory. To improve the prediction accuracy of the fusion models, within the framework of DS evidence theory, mass function generation and combination rule selection are discussed. According to the distance between the SSC prediction value and the classification boundary, a Gaussian-based mass function generation approach is designed. Besides, considering Dempster’s rule of combination, Yager’s rule, Sun Quan’s rule and Li-Bicheng’s rule, the effects of combination rules on the apple classification results are investigated. The results show that the classification accuracies of the PLS model and PNN model are 93.9394% and 90.1515% respectively, while the classification accuracy of the fusion model reaches 96.2121%. Compared with our previously proposed linear triangular mass function generation method, the Gaussian-based mass function can better represent the SSC value’s support degree of class prediction. In addition, compared with Dempster’s rule, Dempster’s rule achieves fusion results that are more consistent with the actual situation of apple classification. The proposed combination of Gaussian-based mass function and Li-Bicheng’s rule of combination can not only improve the mass function and make the classification process closer to the practical application, but also increase the prediction accuracy of the fusion model based on DS evidence theory. Gaussian ; Function (biology) ; Artificial intelligence ; Gaussian function ; Pattern recognition (psychology) ; Computer science ; Gaussian process ; Mathematics ; Dempster–Shafer theory ; Algorithm ; Data mining ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Rebecca Garton | Alice Smallwood | Sarah Whitehouse | David Lake | Gabriel Clarke | Derya Kara-Fisher | Emma Stephen | Ziva Whitelock | Leo Curtis | Andrea Whiteside | Jeanne Andres | Aaron Wheeler | Jean-Christophe Baret | Amy Herr | Sverine Le Gac | Hongkai Wu | Esther Amstad | Anja Boisen | Qun Fang | Piotr Garstecki | Martin Gijs | Mark Gilligan | Keisuke Goda | Tony Huang | Yanyi Huang | Daniel Irimia | Noo Li | Michelle Khine | Sunghoon Kwon | Wlibur Lam | Abraham Lee | Gwo-Bin Lee | Weihua Li | Xiujun Li | Ai Liu | Adrian Neild | Nam-Trung Nguyen | Ian Papautsky | Jianhua Qin | Smuel Snchez | Anderson Shum | David Sinton | Sindy Tang | Yi-Chin Toh | Albert Van Den Berg | Joel Voldman | Jeff Tza-Huei | Wang | David Weitz | George Whitesides | James Chaoyong | Yang | Ki Po | Yuen | Roland Zengerle | Weian Zhao | Ruben Van Den Eeckhoudt | An-Sofie Christiaens | Frederik Ceyssens | Vasileios Vangalis | Kevin Verstrepen | Nico Boon | Filip Tavernier | Michael Kraft | Irene Taurino | Dongliang Li | Xun Zhu | Dingding Ye | Yang Yang | Hong Wang | Rong Chen | Qiang Liao | Yan Qiu | Chao Lu | Fubing Bao | Guoqing Hu | Nicholas Tan | Kwan Zen | Kwek Kerwin | Kim Zeming | Mavis Teo | Jialing Loberas | Chin Lee | Da Goh | Steve Yang | James Oh | Hoi Hui | Simon Po | Han Cool | Hou | Jongyoon Han | Saijie Wang | Quanchen Xu | Zhihan Zhang | Shengbo Chen | Yizhou Jiang | Zhuowei Feng | Dou Wang | Xingyu Jiang
Source: Lab on a Chip (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Contents list Permissions Request permissions Contents list Lab Chip, 2023, 23, 4151 DOI: 10.1039/D3LC90086J This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Attribution ; Chromatin structure remodeling (RSC) complex ; Computer science ; World Wide Web ; Engineering ; Library science ; Computer security ; Chemistry ; Psychology ; Social psychology Radioactive element chemistry and processing
Authors: Aleksandr Sinitca | Asya Lyanova | Dmitrii Kaplun | Pavel Zelenikhin | R. G. Imaev | Artur Gafurov | Bulat Usmanov | Denis V Tishin | Airat R. Kayumov | Mikhail I. Bogachev
Source: The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences (article, 2023-05-12)
Field: Chemistry — Analytical Chemistry

MULTI-CLASS SEGMENTATION OF HETEROGENEOUS AREAS IN BIOMEDICAL AND ENVIRONMENTAL IMAGES BASED ON THE ASSESSMENT OF LOCAL EDGE DENSITY Abstract. Imaging techniques employed in biomedical and ecological applications typically require complex equipment and experimental approaches, including sophisticated multispectral cameras, as well as physical markup of samples, altogether limiting their broad availability. Accordingly, computerized methods allowing to obtain similar information from images obtained in visible light spectrum with reasonable accuracy are of considerable interest. Edge detection methods are commonly used to find discriminating curves in image segmentation. Here we follow an alternative route and employ edge detection results as a separate metric characterizing local structural properties of the image. In turn, their characteristics such as density or orientation averaged in a gliding window are used as a virtual channel substituting multispectral imaging and/or physical markup of samples, and the following image segmentation procedures are performed by thresholding. In complex segmentation scenarios, a single fixed threshold often appears sufficient, and thus relevant adaptive multi-threshold algorithms are of interest, with slope difference distribution (SDD) thresholding algorithm representing a prominent example. Multispectral image ; Thresholding ; Segmentation ; Computer science ; Artificial intelligence ; Computer vision ; Image segmentation ; Metric (unit) ; Enhanced Data Rates for GSM Evolution ; Pattern recognition (psychology) ; Orientation (vector space) ; Image (mathematics) ; Mathematics ; Engineering Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification
Authors: Olivia Y. Long | Cheng Guo | Shanhui Fan
Source: arXiv (Cornell University) (preprint, 2023-06-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Topological nature of non-Hermitian degenerate bands in structural parameter space In photonics, band degeneracies at high-symmetry points in wavevector space have been shown to exhibit rich physical phenomena. However, obtaining degenerate bands away from such points is highly nontrivial. In this work, we achieve complex band degeneracy in a photonic crystal structure over a region of momentum space. We show that this band degeneracy corresponds to polarization-independent transmission, which can be harnessed for nonlocal metasurface design. Moreover, we find that the band degeneracy manifests as a topological singularity in the structural parameter space of the system. Our work highlights the importance of topological concepts in the design of polarization-independent photonic structures. Degenerate energy levels ; Degeneracy (biology) ; Physics ; Polarization (electrochemistry) ; Photonic crystal ; Topological degeneracy ; Topology (electrical circuits) ; Photonics ; Position and momentum space ; Hermitian matrix ; Parameter space ; Space (punctuation) ; Electronic band structure ; Singularity ; Symmetry (geometry) ; Quantum mechanics ; Theoretical physics ; Mathematics ; Topological order ; Geometry ; Quantum ; Computer science ; Symmetry protected topological order Metamaterials and Metasurfaces Applications ; Quantum Mechanics and Non-Hermitian Physics ; Advanced Fiber Laser Technologies
Authors: Aidil Fahmi Shadan | Hafizan Juahir
Source: Unesa Journal of Chemistry (article, 2023-05-17)
Field: Chemistry — Spectroscopy

Identification and Prediction of Fresh Gasoline Locations and Branding Using Newly Targeted Compound Chromatograms with Chemometrics and Machine Learning The detection and use of gasoline at scenes of crimes such as arson is of high interest in forensic investigations. In this work, gas chromatography-mass spectrometry (GC-MS) was used to analyse the gasoline samples and chemometrics namely principal component analysis (PCA), discriminant analysis (DA), and classification and regression tree (CART) machine learning were applied to identify and discriminate the gasoline brands and locations of origin. This study includes three popular gasoline brands collected from stations in eight different Malaysian states, including one oil refinery. The PCA result of 73.6% variation of the first and second principal components for the new targeted compounds chromatogram (TCC) and DA using the discriminant-analysis method correctly classified 94.3% of training samples for location-of-origin and 71.7% of training samples for brand. A novel two-C&amp;R-trees (CART) machine-learning model is also developed and effectively applied to 100 unidentified gasoline samples, with a mean absolute error of 1.1% (location) and 0.4% (brand). The obtained results demonstrate this methodology’s potential to help resolve criminal investigations. Chemometrics ; Identification (biology) ; Chemistry ; Gasoline ; Chromatography ; Biochemical engineering ; Organic chemistry ; Engineering Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Nancy Jenifer | J.W Evelyn Rainy | Janaki Velmurugan
Source: Journal of Cancer and Tumor International (article, 2023-12-31)
Field: Materials Science — Biomaterials

Role of Nanotrace Element in Prostate Cancer an Update Recently, nanomedicine has had a great impact on drug discovery and made the way for the drug delivery system for therapeutic utility. There are many nanoscale products increasing in the research field and in the medical sector. Nanoparticle modified drugs are being developed and brought into the market for the treatment of cancer. The nanoparticle drug carrier can improve the stability of the drug by decreasing the cancerous cell and involving the drug at disease site. Essential trace elements are mostly important for the physiological and biochemical aspects in the human system. Nowadays nanotrace elements are used for the treatment of prostate cancer. In this review the venture and exploration of application of nanomedicine and the use of nanoparticles with essential trace elements will give a wide range of benefits for the treatment for prostate cancer. Prostate cancer ; Element (criminal law) ; Cancer ; Prostate ; Medicine ; Internal medicine ; Political science Nanoparticle-Based Drug Delivery
Authors: Sarita Patel | Manisha Pradhan | Ashish Patel
Source: International Journal of Chemical and Biological Sciences (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Evaluation of heavy metals in herbal plants growing in region of Madhya Pradesh Popularity of herbal drugs is increasing all over the world because of lesser side effects as compared to synthetic drugs. Besides, it costs less and easily available to poor people particularly in developing countries. But quality assurance of herbal drugs is very necessary prior to its use. Because in today polluted environment, even herbal drugs are not safe. The quality and safety of herbal drugs is becoming a major concern worldwide particularly due to contamination by heavy metals. The amount of heavy metals in the plants was analyzed to show the potential threat of their effects to the animals and human beings who consume them as such or their derived products. The work is much more beneficial as the actual nutrient content of the medicinal plants in terms of the essential trace elements could also be identified. The concentration levels (mg/kg) of the selected trace metals (Pb, Cd, Cu, Cr, Fe, Ni and Zn) were estimated in some of the important herbal plants of the Singrauli region. The atomic absorption spectrophotometer was employed for the estimation of heavy metals of three different plant species that were collected from different locations within Singrauli region. The plants selected for the analysis were Zingiber officinale (Ginger), Amorphophallus paeoniifolius (Suran) and Cuminum Cyminum (Cumin). The metal contents in the samples were determined and were found that there is no cadmium, chromium, lead and nickel in plant samples. The results show that Zingiber officinale has the level of metals in the range of Fe>Zn>Cu, Amorphophallus paeoniifolius Fe>Zn>Cu and Cuminum Cyminum Fe>Zn>Cu. Plants absorb heavy metals from the environment and can therefore be used as an indicator of environmental pollution. Zingiber officinale ; Cadmium ; Heavy metals ; Medicinal plants ; Environmental chemistry ; Atomic absorption spectroscopy ; Traditional medicine ; Toxicology ; Chemistry ; Contamination ; Environmental science ; Biology ; Medicine Heavy Metals in Plants ; Heavy metals in environment
Authors: M. A. Tit | Sergey Y. Belyaev | A.G. Shcherbak | O.S. Yulmetovа
Source: Scientific and technical journal of information technologies mechanics and optics (article, 2023-08-01)
Field: Materials Science — General Materials Science

Modeling of the process of spherical form correction for rotors of electrostatically suspended gyros Improvement of the manufacturing technology for gyroscopic devices, which autonomously generate motion parameters of moving objects, has strategic importance and priority for various industries. The object of current research is a spherical rotor of an electrostatically suspended gyroscope which geometric parameters determine the accuracy characteristics of the device. The paper presents results of the process modeling of spherical form correction for rotors of electrostatically suspended gyroscopes at the stage of its manufacture during the coating deposition process. The proposed mathematical model of the deposition process is based on the placement of a movable screen with a hole between a rotor and a spray source. The axis of the hole lies on the dynamic axis of the rotor and it provides a formation of a spherical segment on the coating rotor surface. During deposition of an additional layer, the screen or rotor moves along the dynamic axis of the rotor changing the distance between the rotor and the screen, and there is additional rotation of the rotor around its dynamic axis. It allows adjusting the curvature of the formed coating on the rotor surface. An analytical model of the technological process for correcting the shape of spherical rotors of electrostatically suspended gyroscopes has been developed. A mathematical description, control factors and significant parameters of the process are given. The results of practical testing of the developed model are presented. The presented mathematical model makes it possible to correct the shape of the rotors during the deposition of a functional coating expanding the technological possibilities and increasing the accuracy of rotors. Rotor (electric) ; Gyroscope ; Rotation (mathematics) ; Process (computing) ; Deposition (geology) ; Mechanical engineering ; Coating ; Curvature ; Rotational speed ; Fused deposition modeling ; Materials science ; Computer science ; Engineering ; Aerospace engineering ; Nanotechnology ; 3D printing ; Geometry ; Artificial intelligence ; Mathematics Material Properties and Applications ; Engineering Technology and Methodologies ; Surface Treatment and Coatings
Authors: Hongtao Li
Source: International Journal of Science and Research (IJSR) (article, 2023-05-05)
Field: Materials Science — Materials Chemistry

Review of Life Evaluation of Oil and Gas Long - Term Pipelines In recent years, China's oil and gas pipeline industry is booming, and the working life of oil and gas pipelines increases year by year, it will face problems in terms of pipeline corrosion, which requires a life evaluation of the pipeline to provide specific recommendations for the replacement and overhaul of the pipeline. This paper introduces the corrosion factors of pipelines: the environmental factors around the pipeline, the factors of pipeline corrosion protection, and the material factors of the pipeline itself. The current research status, research methods and shortcomings of the pipeline life evaluation, as well as the future direction of pipeline life evaluation methods to improve the outlook, and to complement the life evaluation technology of oil and gas pipelines. Term (time) ; Pipeline transport ; Petroleum engineering ; Fossil fuel ; Environmental science ; Forensic engineering ; Engineering ; Waste management ; Physics ; Environmental engineering Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Structural Integrity and Reliability Analysis
Authors: Sylvain Fontaine | Floriana Gargiulo | Michel Dubois | Paola Tubaro
Source: arXiv (Cornell University) (preprint, 2023-10-02)
Field: Materials Science — Materials Chemistry

Epistemic integration and social segregation of AI in neuroscience In recent years, Artificial Intelligence (AI) shows a spectacular ability of insertion inside a variety of disciplines which use it for scientific advancements and which sometimes improve it for their conceptual and methodological needs. According to the transverse science framework originally conceived by Shinn and Joerges, AI can be seen as an instrument which is progressively acquiring a universal character through its diffusion across science. In this paper we address empirically one aspect of this diffusion, namely the penetration of AI into a specific field of research. Taking neuroscience as a case study, we conduct a scientometric analysis of the development of AI in this field. We especially study the temporal egocentric citation network around the articles included in this literature, their represented journals and their authors linked together by a temporal collaboration network. We find that AI is driving the constitution of a particular disciplinary ecosystem in neuroscience which is distinct from other subfields, and which is gathering atypical scientific profiles who are coming from neuroscience or outside it. Moreover we observe that this AI community in neuroscience is socially confined in a specific subspace of the neuroscience collaboration network, which also publishes in a small set of dedicated journals that are mostly active in AI research. According to these results, the diffusion of AI in a discipline such as neuroscience didn't really challenge its disciplinary orientations but rather induced the constitution of a dedicated socio-cognitive environment inside this field. Cognitive neuroscience ; Cognitive science ; Field (mathematics) ; Discipline ; Neuroscience ; Psychology ; Citation ; Cognition ; Data science ; Sociology ; Computer science ; Social science Machine Learning in Materials Science ; Bioinformatics and Genomic Networks ; Scientific Computing and Data Management