Artificial intelligence chemistry最新文献

{"title":"KNIME workflows for applications in medicinal and computational chemistry","authors":"Ruchira Joshi ,&nbsp;Zipeng Zheng ,&nbsp;Palak Agarwal ,&nbsp;Ma’mon M. Hatmal ,&nbsp;Xinmin Chang ,&nbsp;Paul Seidler ,&nbsp;Ian S. Haworth","doi":"10.1016/j.aichem.2024.100063","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100063","url":null,"abstract":"<div><p>Artificial intelligence (AI) has huge potential to accelerate drug discovery, but challenges remain in implementing AI algorithms that can be used by the broad scientific community. Identification of molecular features and their subsequent use in training of machine learning models may permit prediction of new molecules with enhanced properties. Predictive modeling is particularly applicable to analysis of structure-activity relationships (SARs) and would be a useful tool in the hands of laboratory medicinal chemists. This requires a software platform that is chemically intuitive while providing the user with access to AI methods. The KNIME platform provides such an environment through inclusion of broad chemical toolsets and a user-friendly approach for utilization of machine learning for analysis of SAR data. Here, we illustrate use of KNIME for this purpose, with a focus on discovery of features of highly potent tau inhibitors from a series of structurally diverse polyphenols. Workflows are described that enable implementation of AI tools in KNIME for diverse SAR projects.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100063"},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000216/pdfft?md5=2753f7e85fd445cdf8e68194d90fd743&pid=1-s2.0-S2949747724000216-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140349865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis","authors":"Yujun Liu ,&nbsp;Xiaolong Zhang ,&nbsp;Luotong Li ,&nbsp;Xingchen Liu ,&nbsp;Tingyu Lei ,&nbsp;Jiawei Bai ,&nbsp;Wenping Guo ,&nbsp;Yuwei Zhou ,&nbsp;Xingwu Liu ,&nbsp;Botao Teng ,&nbsp;Xiaodong Wen","doi":"10.1016/j.aichem.2024.100062","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100062","url":null,"abstract":"<div><p>Fe-based Fischer-Tropsch Synthesis (FTS) enables the selective conversion of syngas into long-chain hydrocarbons, which can be further refined to produce highly demanded liquid fuels and high-value chemical products. However, developing novel heterogeneous catalysts for FTS with desirable performance characteristics is a challenging task, as their performance depends on various factors such as precursor, support material, promoters, pretreatment conditions and the catalyst structures. Thus, it remains difficult to understand the structure-performance relationship of FTS and to optimize the catalyst formulations and operating conditions rationally. By integrating traditional chemistry with machine learning, we herein establish intrinsic correlations among reduction, reaction conditions, phase information and the methane selectivity of Fe-based FTS, using high quality experimental data. The content of the iron phases in the post-reaction phase, particularly χ-Fe₅C₂, significantly influences the methane selectivity of the catalyst. Four types of additives K, Cu, SiO₂, and Ca could effectively suppress the methane selectivity, most likely by promoting or stabilizing the iron carbide phases, indicated by their strong correlation. The machine learned structure-performance relationships offers new insights into the design of Fe-based FTS catalysts, and could guide the further optimization of the preprocessing conditions and various parameter factors to minimize the methane selectivity of FTS.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100062"},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000204/pdfft?md5=062c38e0ee5dbc49728857b869639811&pid=1-s2.0-S2949747724000204-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140346879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hidden descriptors: Using statistical treatments to generate better descriptor sets","authors":"Lucía Morán-González ,&nbsp;Feliu Maseras","doi":"10.1016/j.aichem.2024.100061","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100061","url":null,"abstract":"<div><p>The application of artificial intelligence to chemistry usually focuses on the identification of good correlations between descriptors and a given property of interest. The descriptors often come from arbitrary sets, with the implicit assumption that the evaluation of a sufficiently wide range of descriptors will lead to a satisfactory choice. Recent work in our group has focused on applying statistical analysis to large amounts of DFT results with the goal of finding optimal descriptor sets for a given property, which we label as hidden descriptors. This article briefly discusses this treatment and the chemical knowledge that has been gained through its application in two different domains: metal-ligand bond strength in transition metal complexes, and energy barriers in bimolecular nucleophilic substitution reactions.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100061"},"PeriodicalIF":0.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000198/pdfft?md5=541c02174c39b94d0f3787f465d80154&pid=1-s2.0-S2949747724000198-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140345108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging technologies for drug repurposing: Harnessing the potential of text and graph embedding approaches","authors":"Xialan Dong,&nbsp;Weifan Zheng","doi":"10.1016/j.aichem.2024.100060","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100060","url":null,"abstract":"<div><p>Drug repurposing is an approach to identifying new uses for existing drugs, where advanced computational methods, such as text and graph embedding techniques, are playing an ever-increasing role. This review provides a timely overview of these embedding methods for drug repurposing and discusses their integration with machine learning. Text embedding techniques, such as Word2Vec, FastText, BERT, and Doc2Vec, enable the analysis of biomedical literature and clinical data to discover potential drug-disease relationships. These methods convert textual data into numerical representations, allowing for similarity calculations and predictive modeling. Several successful applications of text embedding for drug repurposing are highlighted. In addition, graph embedding methods, such as Node2Vec and GraphSAGE, are being employed to convert complex biological knowledge graphs into vector representations. These representations facilitate various network analysis tasks, including predicting drug-target interactions and identifying hidden associations between drugs and diseases. Case studies in both technologies demonstrate their effectiveness in drug repurposing. The advantages and limitations of both text and graph embedding technologies, and their complementarity with traditional structure-based approaches have been discussed. Finally, text and graph embedding methods can be employed in conjunction with traditional approaches of computational methods, which can offer a promising path to identifying novel drug repurposing opportunities, particularly for rare diseases.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100060"},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000186/pdfft?md5=181c1bd52e69ed59bfcbf5f5691c8a0d&pid=1-s2.0-S2949747724000186-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140181159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated learning data-driven potential models for spectroscopic characterization of astrophysical interest noble gas-containing NgH2+ molecules","authors":"María Judit Montes de Oca-Estévez ,&nbsp;Rita Prosmiti","doi":"10.1016/j.aichem.2024.100059","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100059","url":null,"abstract":"<div><p>The choice of a proper machine learning (ML) algorithm for constructing potential energy surface (PES) models has become a crucial tool in the fields of quantum chemistry and computational modeling. These algorithms offer the ability to make reliable and accurate predictions at a reasonable computational cost, and thus they can be then used in various molecular dynamics and spectroscopic studies. For that, it is not surprising that much of the current research focuses on the development of software that generates machine learning models using precalculated <em>ab initio</em> data points. This study is primarily dedicated to the application and assessment of various automated learning models. These models are trained and tested using datasets derived from CCSD(T)/CBS[56] calculations, aiming to represent intermolecular interactions in small molecules, such as the NgH<span><math><msubsup><mrow></mrow><mrow><mn>2</mn></mrow><mrow><mo>+</mo></mrow></msubsup></math></span> complexes, where Ng represents helium (He), neon (Ne), and argon (Ar) atoms. These noble gas-containing molecules have gained increasing significance in the field of molecular astrochemistry, due to the recent discovery of HeH⁺ and ArH⁺ molecular cations in the interstellar medium (ISM), thereby opening up a wide range of possibilities in this scientific area. Consequently, the ML-generated PESs are employed to compute vibrational bound states for these molecular cations, with the goal of characterizing all their known isotopologues. Furthermore, the results are compared with spectroscopic data, when available, from previous studies in the literature. Our findings have the potential to provide valuable guidance for future ML-PES development and benchmarking studies involving noble gas-containing cations of astrophysical importance.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100059"},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000174/pdfft?md5=b79fa9d5a2a5cca40b8a1c916bfa7bf5&pid=1-s2.0-S2949747724000174-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SOmicsFusion: Multimodal coregistration and fusion between spatial metabolomics and biomedical imaging","authors":"Ang Guo ,&nbsp;Zhiyu Chen ,&nbsp;Yinzhong Ma ,&nbsp;Yueguang Lv ,&nbsp;Huanhuan Yan ,&nbsp;Fang Li ,&nbsp;Yao Xing ,&nbsp;Qian Luo ,&nbsp;Hairong Zheng","doi":"10.1016/j.aichem.2024.100058","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100058","url":null,"abstract":"<div><p>We present SOmicsFusion, a software toolbox for ’fusing’ spatial omics with classical biomedical imaging modalities, capitalizing on their inherent correspondences and complementarity when characterizing the same subject. By augmenting radiological and histological images with spatially resolved molecular profiling, this fusion offers a panoramic characterization of the biochemical perturbations underlying pathological conditions, thereby advancing our understanding of diseases like brain disorders and cancers. The cornerstone of SOmicsFusion is a coregistration tool that leverages an innovative two-stage machine learning pipeline to tackle the longstanding challenge of spatially aligning data from fundamentally different modalities, priming them for subsequent fusion analysis that often requires precise pixel-wise correspondence between the datasets. Specifically, the pipeline utilizes an original dimension reduction algorithm for representational domain alignment, followed by a Deep Learning-based method for spatial domain alignment. SOmicsFusion is demonstrated using mass spectrometry imaging (MSI)-mediated spatial metabolomics and four other modalities: magnetic resonance imaging (MRI), microscopy, brain atlas, and spatial transcriptomics. By reducing coregistration errors by 38–69% compared to existing pipelines, SOmicsFusion enhances the precision of associating molecule distribution with anatomy and pathology features, ultimately leading to more statistically robust findings. Furthermore, SOmicsFusion incorporates various downstream analysis tools, including overlay visualization, spatial correlation/co-expression analysis, pansharpening, and automated anatomy annotation. These tools facilitate the extraction of biological insights that would be unattainable through individual modalities alone. For instance, the coregistration and correlation between MSI and in vivo MRI datasets unveil that the spatial heterogeneity in metabolites stems from the temporal heterogeneity in the development of cerebral ischemia-reperfusion injury.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100058"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000162/pdfft?md5=963d89fc26dbcda572405e5ce54d24ee&pid=1-s2.0-S2949747724000162-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140134391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Intelligent Platforms for High‐Throughput Chemical Synthesis","authors":"Jia-Min Lu ,&nbsp;Jian-Zhang Pan ,&nbsp;Yi-Ming Mo ,&nbsp;Qun Fang","doi":"10.1016/j.aichem.2024.100057","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100057","url":null,"abstract":"<div><p>Automation and high-throughput techniques provide a solid technical foundation for realizing the deep fusion of artificial intelligence and chemistry as well as the full utilization of their advantages. In recent years, with the unique advantages of low consumption, low risk, high efficiency, high reproducibility, high flexibility and good versatility, intelligent automated platforms for high-throughput chemical synthesis aroused widespread concerns of synthetic chemists. In this review, the automated high-throughput chemical synthesis, automated high-throughput sample treatment and characterization technique, as well as the application of artificial intelligence technique in chemical synthesis are introduced. The characteristics of the systems and platforms based on these techniques, including the iChemFoundry platform developed in the ZJU-Hangzhou Global Scientific and Technological Innovation Center, are introduced. The intelligent automated platforms for high-throughput chemical synthesis will reshape the thinking mode of traditional disciplines, promote the innovation of disruptive techniques, redefine the rate of chemical synthesis, and innovate the way of material manufacturing.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100057"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000150/pdfft?md5=25909749b7c4a1be6ae263d1cca2abf9&pid=1-s2.0-S2949747724000150-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139986849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid screening of copper-based bimetallic catalysts via automatic electrocatalysis platform: Electrocatalytic reduction of CO2 to C2+ products on europium-modified copper","authors":"Yan Shen,&nbsp;Zihan Wang,&nbsp;Yihan Wang,&nbsp;Cheng Wang","doi":"10.1016/j.aichem.2024.100056","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100056","url":null,"abstract":"<div><p>The electrocatalytic conversion of CO₂ (CO₂RR) to multi-carbon products has been an appealing strategy to reduce carbon emissions. However, rapid experimental discovery of efficient CO₂RR electrocatalysts and fast recording of full product distribution information is non-trivial. Herein, we used an electrocatalyst testing platform featuring a home-built automatic flow cell to accelerate catalysts screening. Based on 364 effective data points from 42 Cu-lanthanide bimetallic catalysts obtained within 21 working hours, we found that Eu modification over Cu can promote C₂₊ faradaic efficiency (FE). We have previously reported part of the screening data and the optimization of the Mg-Cu catalyst（<em>Angew. Chem.</em> <strong>2022</strong>, <em>134</em>, e202213423）. Here we augmented the dataset by adding the lanthanide modifiers and reported the Eu-Cu catalyst resulted from the high-throughput investigation. Our characterizations revealed that the Eu²⁺ reduced from Eu³⁺ during the catalyst synthesis prevented the agglomeration of nanoparticles, thus making europium modifications stand out from other lanthanide metal modifiers on FE C₂₊ enhancement. We then optimized the Eu-CuO_x catalyst based on the above understanding to achieve ∼80% C₂₊ FE at a high current density of 1.25 A cm⁻².</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100056"},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000149/pdfft?md5=d1c6b7f6973c2f825f4024a496be4cd7&pid=1-s2.0-S2949747724000149-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139935586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of dimensionality reduction techniques for the visualisation of chemical space in organometallic catalysis","authors":"Mario Villares ,&nbsp;Carla M. Saunders ,&nbsp;Natalie Fey","doi":"10.1016/j.aichem.2024.100055","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100055","url":null,"abstract":"<div><p>We have used a Ligand Knowledge Base for bidentate P,P-donor ligands of potential interest to homogeneous catalysis to compare three dimensionality reduction techniques, namely Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP) and t-distributed Stochastic Neighbor Embedding (t-SNE). While our previous work on Ligand Knowledge Bases has focused on PCA, here we compare this approach with more recently-published approaches and assess the information retention, visualization, clustering and interpretability which can be achieved for each approach. We find that potential advantages of t-SNE are not realized with a database of the current size (275 entries), and that there is a degree of complementarity between PCA and UMAP. The statistics underlying PCA rely on linear relationships, making interpretation of the resulting plots comparatively straightforward. Since much of chemistry relies on linear structure-property relationships and low-dimensional visualization, the explainability and information retention achieved is attractive. UMAP proved more challenging to interpret, but achieved clear clustering which was often chemically meaningful, and it would be a useful approach for ensuring that distinct subsets of compounds are sampled in a machine-learning context. This analysis also highlighted that the tunability of catalysis achieved through ligand exchange maps well onto some areas of chemical space where closely related ligands cluster, while others represent outliers; these arise from different combinations of steric and electronic effects which chemists will find intuitive.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100055"},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000137/pdfft?md5=d22dd66b98e698544ad12f66b7d830c4&pid=1-s2.0-S2949747724000137-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139943030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning assisted analysis and prediction of rubber formulation using existing databases","authors":"Wei Deng ,&nbsp;Yuehua Zhao ,&nbsp;Yafang Zheng ,&nbsp;Yuan Yin ,&nbsp;Yan Huan ,&nbsp;Lijun Liu ,&nbsp;Dapeng Wang","doi":"10.1016/j.aichem.2024.100054","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100054","url":null,"abstract":"<div><p>Designing rubber formulations can greatly benefit from using a database that stores the formulations and corresponding property data of rubber composites. Such a database can expedite the decision-making process by swiftly identifying the most suitable formulations for specific applications. However, the management of a rubber formulation database encounters various issues, including missing formulation and property data, as well as data entry errors. These issues can impede the decision-making processes and even result in incorrect decisions being made. In this study, machine learning (ML) algorithms were applied to analyze rubber formulation databases. Our findings highlight the success of the ML algorithm in effectively filling in missing data and identifying erroneous data. Furthermore, it demonstrates the accurate prediction of properties for untested formulations within the pre-determined database space. The results underline the outstanding performance of ML algorithms in expediting the rubber formulation design process and emphasize their immense potential to play a prominent role in the advancement of rubber composites.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100054"},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000125/pdfft?md5=c058446a90f81b469ca59bff1d08c2a1&pid=1-s2.0-S2949747724000125-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}