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作者： Mengbo Wang Xue Jiang Guangdong Innovative Technical College

With the development of mobile Internet technology, Internet of things, cloud computing and other information technology, big data is more and more widely used in real life. Big data refers to the use of all data for analysis and processing. According to big data's 4 V characteristic: Volume(large number), Velocity(high speed), Variety(variety), Value(value density), this paper explores its role in data processing and analysis, and explores the unknown field of data processing and analysis. There is no mature application of big data analysis method in chemical data processing and analysis field at present. Need According to the existing application examples, combined with the characteristics of analytical chemical data processing and analysis, the concrete operation flow is formed. The emphasis of this paper is to understand the application and characteristics of big data's analytical work in various fields at the present stage, and to explore the concrete operation flow and effect of big data analysis applied to the processing and analysis of analytical chemical data.

关键词： Big data data processing and analysis Analytical chemistry

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A complementary metaproteomic approach to interrogate microbiome cultivated from clinical colon biopsies

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PROTEOMICS 2024年第21-22期24卷 e2400078页

作者： Duong, Van-An Enkhbayar, Altai Bhasin, Nobel Senavirathna, Lakmini Preisner, Eva C. Hoffman, Kristi L. Shukla, Richa Jenq, Robert R. Cheng, Kai Bronner, Mary P. Figeys, Daniel Britton, Robert A. Pan, Sheng Chen, Ru Univ Texas Hlth Sci Ctr Houston Brown Fdn McGovern Med Sch Inst Mol Med Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr UTHlth Houston Grad Sch Biomed Sci Houston TX USA Baylor Coll Med Dept Med Houston TX USA Baylor Coll Med Dept Mol Virol & Microbiol Houston TX USA Univ Texas MD Anderson Canc Ctr Div Canc Med Dept Genom Med Houston TX USA Univ Ottawa Fac Med Sch Pharmaceut Sci Ottawa ON Canada Univ Utah Dept Pathol Salt Lake City UT USA Baylor Coll Med Brown Fdn Inst Mol Med Houston TX 77030 USA

The human gut microbiome plays a vital role in preserving individual health and is intricately involved in essential functions. Imbalances or dysbiosis within the microbiome can significantly impact human health and are associated with many diseases. Several metaproteomics platforms are currently available to study microbial proteins within complex microbial communities. In this study, we attempted to develop an integrated pipeline to provide deeper insights into both the taxonomic and functional aspects of the cultivated human gut microbiomes derived from clinical colon biopsies. We combined a rapid peptide search by MSFragger against the Unified Human Gastrointestinal Protein database and the taxonomic and functional analyses with Unipept Desktop and MetaLab-MAG. Across seven samples, we identified and matched nearly 36,000 unique peptides to approximately 300 species and 11 phyla. Unipept Desktop provided gene ontology, InterPro entries, and enzyme commission number annotations, facilitating the identification of relevant metabolic pathways. MetaLab-MAG contributed functional annotations through Clusters of Orthologous Genes and Non-supervised Orthologous Groups categories. These results unveiled functional similarities and differences among the samples. This integrated pipeline holds the potential to provide deeper insights into the taxonomy and functions of the human gut microbiome for interrogating the intricate connections between microbiome balance and diseases.

关键词： data processing and analysis gut microbiome mass spectrometry metaproteomics proteomics taxonomy

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Prediction interval estimation of sinter drum index based on light gradient boosting machine and kernel density estimation

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IRONMAKING & STEELMAKING 2023年第8期50卷 909-920页

作者： Xia, Guanglei Wu, Zhaoxia Liu, Mengyuan Jiang, Yushan Northeastern Univ Qinhuangdao Sch Control Engn 143 Taishan Rd Qinhuangdao Hebei Peoples R China Northeastern Univ Qinhuangdao Inst Data Anal & Intelligence Comp Qinhuangdao Peoples R China

Owing to the uncertainty operation in the sintering process, it is easy to produce uncertain prediction errors in the single drum index prediction model, which makes the prediction results lack certain reliability. Accurate and reliable prediction of the drum index can help improve the drum index. In this paper, a prediction interval estimation method of drum index based on a light gradient boosting machine (LightGBM) and kernel density estimation (KDE) is proposed. LightGBM can obtain accurate points prediction of drum index, and then use the KDE method to obtain the estimated prediction interval of drum index. The comparison results of different methods show that LightGBM has high prediction performance, and KDE can well quantify the prediction error of drum index, which verifies the effectiveness of the prediction interval estimation method combined with LightGBM and KDE, and provides more reliable decision-making information for the optimisation of sintering process parameters.

关键词： Iron ore sintering drum index LightGBM kernel density estimation prediction interval estimation machine learning data processing and analysis sinter quality

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processing and analysis of X-ray photoelectron diffraction data using IGOR Pro

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JOURNAL OF APPLIED CRYSTALLOGRAPHY 2018年第3期51卷 935-942页

作者： Liang, Xihui Lubin, Christophe Mathieu, Claire Barrett, Nicholas CEA IRAMIS SPEC LENSIS F-91191 Gif Sur Yvette France Guangzhou Res Inst Optomechanoelect Technol 3 Keyan Rd Guangzhou 510663 Guangdong Peoples R China

A software package is presented for nearly real-time display of diffractograms during X-ray photoelectron diffraction (XPD) data acquisition and for processing and analysis after an experiment. During the experiment, the package is able to automatically read X-ray photoelectron spectroscopy (XPS) data, perform initial data processing and project intensity values as XPD diffractograms. Four display modes are supported. After the experiment, the package is able to open, process and analyze XPD patterns. The processing functions include rotation, cropping, creating a full 2 pi pattern using symmetry operations, smoothing and converting a pattern to an image suitable for publication. The analysis functions include displaying polar angles, azimuthal angles, intensity, the core level spectrum at a selected angular data point, and azimuthal and radial profiles. The package also integrates fitting functions for core level spectra. The package is developed using the IGOR Pro scripting language. A graphical user interface has been developed to allow all the operations just by mouse clicking. The package is designed to interface directly with an XPD system at the Saclay center of the French Atomic Energy and Alternative Energies Authority, but the algorithms are generally applicable and can be readily adapted to other XPD systems.

关键词： X-ray photoelectron diffraction X-ray photoelectron spectroscopy data processing and analysis IGOR Pro

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data processing and analysis in real-world traditional Chinese medicine clinical data: challenges and approaches

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STATISTICS IN MEDICINE 2012年第7期31卷 653-660页

作者： Liu, Baoyan Zhou, Xuezhong Wang, Yinhui Hu, Jingqing He, Liyun Zhang, Runshun Chen, Shibo Guo, Yufeng Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China China Acad Chinese Med Sci Guanganmen Hosp Beijing 100053 Peoples R China China Acad Chinese Med Sci Inst Basic Res Clin Med Beijing 100700 Peoples R China

Traditional Chinese medicine (TCM) is a clinical-based discipline in which real-world clinical practice plays a significant role for both the development of clinical therapy and theoretical research. The large-scale clinical data generated during the daily clinical operations of TCM provide a highly valuable knowledge source for clinical decision making. Secondary analysis of these data would be a vital task for TCM clinical studies before the randomised controlled trials are conducted. In this article, we discuss the challenges and issues, such as structured data curation, data preprocessing and quality, large-scale data management and complex data analysis requirements, in the data processing and analysis of real-world TCM clinical data. Furthermore, we also discuss related state-of-the-art research and solutions in China. We have shown that the clinical data warehouse based on the collection of structured electronic medical record data and clinical terminology would be a promising approach for generating clinical hypotheses and helping the discovery of clinical knowledge from large-scale real-world TCM clinical data. Copyright (c) 2011 John Wiley & Sons, Ltd.

关键词： traditional Chinese medicine real-world clinical data data processing and analysis

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Automated Quality Inspection of Formwork Systems Using 3D Point Cloud data

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BUILDINGS 2024年第4期14卷 1177页

作者： Wu, Keyi Prieto, Samuel A. Mengiste, Eyob de Soto, Borja Garcia New York Univ Abu Dhabi NYUAD Div Engn SMART Construct Res Grp Expt Res BldgPOB 129188 Abu Dhabi U Arab Emirates

Ensuring that formwork systems are properly installed is essential for construction safety and quality. They have to comply with specific design requirements and meet strict tolerances regarding the installation of the different members. The current method of quality control during installation mostly relies on manual measuring tools and inspections heavily reliant on the human factor, which could lead to inconsistencies and inaccurate results. This study proposes a way to automate the inspection process and presents a framework within which to measure the spacing of the different members of the formwork system using 3D point cloud data. 3D point cloud data are preprocessed, processed, and analyzed with various techniques, including filtering, downsampling, transforming, fitting, and clustering. The novelty is not only in the integration of the different techniques used but also in the detection and measurement of key members in the formwork system with limited human intervention. The proposed framework was tested on a real construction site. Five cases were investigated to compare the proposed approach to the manual and traditional one. The results indicate that this approach is a promising solution and could potentially be an effective alternative to manual inspections for quality control during the installation of formwork systems.

关键词： 3D laser scanning concrete construction construction automation data processing and analysis quality inspection temporary structure

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Facilitating Reproducibility in Catalysis Research with Managed Workflows and RO-Crates: A Galaxy Case Study

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CHEMCATCHEM 2025年第10期17卷

作者： Nieva-de-la-Hidalga, Abraham Liborio, Leandro Austin, Patrick Devadasan, Subindev Underwood, Tom Belozerov, Alexander Wilding, Martin Ramanan, Nitya Catlow, C. Richard A. Cardiff Univ Sch Chem TRH Bldg Maindy Rd Cardiff CF24 4HQ Wales UK Catalysis Hub Res Complex Harwell Didcot OX11 0FA England Rutherford Appleton Lab Sci & Technol Facil Council Sci Comp Dept Didcot OX11 0QX England Diamond Light Source Ltd Didcot OX11 0DE England UCL Chem Dept London WC1H 0AJ England Cardiff Univ Sch Comp Sci & Informat Cardiff CF24 4AG Wales

Publishing supporting data significantly impacts researchers' productivity, especially in experiments requiring extensive tracking of data, processing steps, parameters, and outputs. A managed workflow environment, combined with RO-Crates, addresses these data management challenges. Workflows provide an alternative for handling complex data analyses by orchestrating various processing tools. The RO-Crate format, a community-driven proposal for packaging data, provenance, and workflows, facilitates publishing and reproducibility. The Galaxy workflow management system integrates workflows and RO-Crates, enabling the export of analyses, which can be shared and restored by other users. Using Galaxy, we demonstrate how to improve support for reproducibility. We tested our approach by designing an experiment using diverse supporting data from selected papers. In the experiment, we identified specific FAIRness and completeness issues hindering result reproduction, even when authors made significant efforts to document and publish their supporting data. In comparison, the proposed approach supports reproducibility by packaging datasets in RO-Crate format, streamlining the process. The Galaxy RO-Crates, published as supporting materials, enhance data sharing, transparency, and reproducibility, thus supporting the advancement of FAIR research practices in catalysis research.

关键词： data processing and analysis FAIR data objects Reproducibility of results Workflow management system X-ray absorption spectroscopy

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Technical report: The clinically useful selection of proteins protocol: An approach to identify clinically useful proteins for validation

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JOURNAL OF PROTEOMICS 2024年 296卷 105110页

作者： Swaney, Ella E. K. Hearps, Stephen Monagle, Paul Roehrl, Michael H. A. Ignjatovic, Vera Murdoch Childrens Res Inst Haematol Grp 50 Flemington Rd Melbourne 3052 Australia Murdoch Childrens Res Inst Clin Epidaemiol & Biostat 50 Flemington Rd Melbourne 3052 Australia Univ Melbourne Dept Paediat Melbourne 3050 Australia Royal Childrens Hosp Dept Clin Haematol 50 Flemington Rd Melbourne 3052 Australia Sydney Childrens Hosp Kids Canc Ctr High St Sydney 2031 Australia Beth Israel Deaconess Med Ctr Dept Pathol 330 Brookline Ave Boston MA 02115 USA Harvard Med Sch 25 Shattuck St Boston MA 02115 USA Johns Hopkins All Childrens Inst Clin & Translat R 600 5th St SouthSuite 3200 St Petersburg FL 33701 USA Johns Hopkins Univ Sch Med Dept Pediat 3400 N Charles St Baltimore MD 21218 USA Johns Hopkins All Childrens Inst Clin & Translat R 600 5 th SouthSuite 3200 St Petersburg FL 33701 USA

Clinical proteomics studies aiming to develop markers of clinical outcome or disease typically involve distinct discovery and validation stages, neither of which focus on the clinical applicability of the candidate markers studied. Our clinically useful selection of proteins (CUSP) protocol proposes a rational approach, with statistical and non-statistical components, to identify proteins for the validation phase of studies that could be most effective markers of disease or clinical outcome. Additionally, this protocol considers commercially available analysis methods for each selected protein to ensure that use of this prospective marker is easily translated into clinical practice. Significance: When developing proteomic markers of clinical outcomes, there is currently no consideration at the validation stage of how to implement such markers into a clinical setting. This has been identified by several studies as a limitation to the progression of research findings from proteomics studies. When integrated into a proteomic workflow, the CUSP protocol allows for a strategically designed validation study that improves researchers' abilities to translate research findings from discovery-based proteomics into clinical practice.

关键词： Biomarkers data processing and analysis Targeted proteomics

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ChatGPT for textile science and materials: A perspective

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MATERIALS TODAY COMMUNICATIONS 2023年 37卷

作者： Xu, Yiqin Zhi, Chao Guo, Hao Zhang, Mimi Wu, Huiping Sun, Runjun Dong, Zijing Yu, Lingjie Xian Polytech Univ Sch Text Sci & Engn Xian 710048 Shaanxi Peoples R China Xian Polytech Univ State Key Lab Intelligent Text Mat & Prod Xian 710048 Shaanxi Peoples R China

The advancements in Artificial Intelligence (AI), notably OpenAI's ChatGPT, introduce novel research perspectives and applications to textile science and industry. This study primarily encompasses two domains: academic research and industrial applications. Within the realm of textile science, using textiles and carbon microspheres as examples, we employ ChatGPT to translate demand language into code, exploring its potential for data processing and visualization;in collaboration with Stable Diffusion's "text-to-image" technology, we visualize concepts in textile design;by integrating Segment Anything Model (SAM)' s image segmentation technology, ChatGPT achieves precise detection of textile defects;and this research also delves into the integration of ChatGPT with finite element modeling software, proposing a more efficient and accurate strategy for composite material modeling. In the textile industry context, the application of ChatGPT offers continuous process optimization and spurs the adoption of innovative techniques and methodologies, thereby advancing sustainable innovation within the sector. This paper presents a thorough survey of ChatGPT, aiming to highlight the transformative capabilities of this AI model and thus suggest a path towards a more innovative and sus-tainable future for the textile science and textile industry.

关键词： ChatGPT data processing and analysis Textile design Defect detection Materials modeling

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Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit

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PROTEOMICS 2024年第8期24卷 2300112-2300112页

作者： Picciani, Mario Gabriel, Wassim Giurcoiu, Victor-George Shouman, Omar Hamood, Firas Lautenbacher, Ludwig Jensen, Cecilia Bang Mueller, Julian Kalhor, Mostafa Soleymaniniya, Armin Kuster, Bernhard The, Matthew Wilhelm, Mathias Tech Univ Munich TUM Sch Life Sci Computat Mass Spectrometry Freising Weihenstephan Germany Tech Univ Munich Chair Prote & Bioanalyt TUM Sch Life Sci Freising Weihenstephan Germany Tech Univ Munich TUM Sch Life Sci Computat Mass Spectrometry D-85354 Freising Weihenstephan Germany

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub () and can easily be installed locally through the cross-platform PyPI Python package.

关键词： bioinformatics bottom-up proteomics data processing and analysis mass spectrometry LC-MS/MS technology

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