dataesr/scientific-paragraphs-categorization huggingface dataset:
DART is considered one of the most advanced 3-D RTMS and that it has been positively evaluated in the framework of the Radiative transfer inter-comparison project (RAMI)  [27, 28]. Our methodology for modelling Tmrt can be applied at different scales and under a range of parameters including the urban pattern, surface material of ground, walls, roofs, and vegetation properties (coverage, shape, spectral properties, Leaf Area Index, and Leaf Area Density). Three dimensional scenes can be generated from multi-source remote sensing data as shown on our previous study on 3-D Reconstruction of urban landscapes from satellite data, where a detailed workflow for the derivation of 3-D urban scenes was presented, together with an evaluation of 3-D radiative budget over different urban typologies found in Singapore  [21] ; additionally, scenes can be generated from Aerial LiDAR Scanning (ALS) point cloud data  [29]  or using 3-D modelling software.
Scene Generation and Parameter Setting A 3-D model of the Tanjong Pagar District, Singapore, was reconstructed. The building height, vegetation shape, and distribution was obtained from the Aerial LiDAR Scanning dataset of Singapore  [34]. This dataset was acquired by the Optech Pegasus HA500 sensor with a planar density of about 30 points/m 2 which proved to be well adapted to map vegetation. The buildings were reconstructed using 3DsMax software, obtaining a Level of Detail (LOD) of 1. 2. The north part of the scene corresponds to the existing urban setting, whereas its southern part (former Tanjong Pagar Port) corresponds to a design proposal with a high-density urban form  [29]. The scene elements were grouped by surface material, allowing them to be linked to their respective surface temperatures and optical spectral properties in the DART database.
Discussion This work introduced a new method, which uses for the first time the DART model to estimate and map Tmrt at different scales. Our motivation to use the DART model to estimate Tmrt stems from its capability to assimilate a large variety of 3-D data derived from state-of-the-art remote sensing techniques; providing the possibility to better analyze how Tmrt is influenced by vegetation and its biophysical properties, by the optical properties of surface materials, by surface temperature, and also by local atmospheric conditions. The field evaluation indicated good agreement between DART-simulated Tmrt and field estimated Tmrt at the Cantonment towers measurement site. However, a substantial underestimation is observed in areas with absence of solar radiation and in the late afternoon.
Secondly, the designation of optical properties was merely based on site inspection and matching with the closest material existing within the DART database, this, however, is a source of uncertainty since the spectral library of DART might not well represent the actual characteristics of the surfaces of our study area. An overview of the spectral signatures of construction materials and vegetation used on our simulations can be found in Appendix D. The correct consideration of optical properties of surface materials play an important role in the radiative budget and thus, on Tmrt. To obtain a more accurate representation of optical properties, spectral signatures could be collected using a spectroradiometer and measuring the emissivity of the surfaces in the actual site. Unfortunately, we did not have access to such equipment.
A detailed methodology on the derivation spectral signatures of urban materials through emittance and reflectance spectroscopy can be found in Kotthaus et al. 2014  [46]. Secondly, the designation of optical properties was merely based on site inspection and matching with the closest material existing within the DART database, this, however, is a source of uncertainty since the spectral library of DART might not well represent the actual characteristics of the surfaces of our study area. An overview of the spectral signatures of construction materials and vegetation used on our simulations can be found in Appendix D. The correct consideration of optical properties of surface materials play an important role in the radiative budget and thus, on Tmrt. To obtain a more accurate representation of optical properties, spectral signatures could be collected using a spectroradiometer and measuring the emissivity of the surfaces in the actual site.
Conclusions The increasing availability of data of urban areas opens up new possibilities for detailed analysis in terms of urban microclimate and thermal comfort. Affordable laser scanners are being developed, opening opportunities for wide range of research on the processing and data retrieval for urban 3-D mapping. This unprecedented increase of data and computation capacity highlights the need for more detailed simulation tools and methods for planners and designers to evaluate the performance of existing areas and to assess future designs under different climatic scenarios. This study explored the potential of using the 3-D physically based RTM DART to model Tmrt. We presented a new method for the estimation and mapping Tmrt at different scales with a detailed consideration of surface materials and vegetation.
Acknowledgments: The authors would like to thank the  DART team of CESBIO (CNES, CNRS ,  IRD, and University of Toulouse ) for the consistent support with DART model. Special thanks to  Professor Wong Nyuk Hien  ( Building Department, National University of Singapore ) for providing the Net radiometer devices used for field evaluation. We acknowledge the  Singapore Land Authority  for providing the LiDAR data and  Naika Meili  for providing the surface temperatures derived from the UT&C model the authors appreciate the valuable remarks and suggestions from anonymous reviewers.
db 2D lambertian model Sand white Property name Glass Lambertian Glass 2D lambertian database Lambertian_mineral. db 2D lambertian model glass_window Property name Aluminium roof Lambertian Aluminium Roof 2D lambertian database Lambertian_mineral. db 2D lambertian model aluminium_window Property name Badminton Court Lambertian Badminton Court 2D lambertian database Lambertian_mineral. db 2D lambertian model concrete_brown.
This means that metabolite concentrations are expressed as arbitrary units or ion intensities. In untargeted MS-based metabolomics, metabolite abundances are generally provided in chromatographic peak areas. Those peak areas highly depend on the LC-HRMS conditions and thus remain linked to a dataset and are difficult to be directly compared with those from other metabolomics profiling experiments performed at different time periods or within different laboratories. The most used detection methods for global metabolomics approaches are NMR, GC/ MS, and liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). These untargeted approaches are used especially in the first steps of biomarker discovery. Conversely, targeted approaches are focused on a limited number of compounds, belonging to a given chemical class or metabolic pathway.
In this context, this review deals with the main challenges linked to analytical chemistry that need to be overcome to foster the implementation of metabolomics in personalized medicine and in clinical practice, from data production for biomarker discovery and validation, to the translation of metabolomics signatures into assays for medical laboratories and point-of-care tests. Metabolomics for personalized medicine: more standardized and sharable metabolomics datasets are still required Several recent reviews have already addressed clinical applications of metabolomics in various fields such as oncology, cardiology, neurology, diabetes, kidney and liver diseases, and also response to treatments (aspirin, simvastatin, or antihypertensive drugs)  [16, [18] [19] [20] [21]. In their article entitled "Metabolomics for the masses: the future of metabolomics in a personalized world," Trivedi et al.
The same conclusions are presented in the position paper of Pinu et al. , which summarizes the discussions on translational metabolomics undertaken during the peer session of the Australian and New Zealand Metabolomics conference in 2018. In addition to issues related to the lack of standardization of data acquisition protocols and to partial identification and too limited annotation of datasets, these authors also emphasized the insufficient recognition of metabolomics by funding agencies, the difficulty of gathering multiple fields of expertise within a given group, and the necessity to develop routine tests and portable devices  [16]. A search in the PubMed database (March 2021) over the 2015-2020 period with keywords related to the main omics techniques, combined with the terms "disease*," "*marker* or signature*," and "patients," and restricted to the occurrence of these words in titles and/or abstracts, yielded 4874 publications.
At the same time, data repositories have been developed, making publicly available projects and studies including raw data together with analytical and biological metadata necessary for statistical analysis, as well as processed and annotated datasets. The two main data warehouses in the field of metabolomics are MetaboLights  [22, 23]  and Metabolomics Workbench  [24]. They allow sharing of protocols, assays, and even tools to perform analyses and meta-analyses on the datasets (for Metabolomics Workbench). At the time of writing this review, MetaboLights (https://www. ebi. ac. uk/ metabolights, accessed March 2021) contained 554 studies, of which 30% deal with human studies (i. e. , related to "homo sapiens" in the browse study menu) and about 18% are performed on human blood and urine samples (i.
e. , related to "blood," "serum," "plasma," "whole blood," and "urine" in the browse study menu). Metabolomics Workbench (https:// www. metabolomicsworkbench. org, accessed March 2021) included 1370 studies of which 41% dealt with human biological samples. A large number of projects are related to diseases such as cancer (182 projects) or diabetes  (63 projects). It should also be noted that spectral data warehouses such as MassIVE (https://massive. ucsd. edu), a community resource developed by the NIH center for computational mass spectrometry for the free exchange of mass spectrometry data, are likely to host metabolomics datasets (404 out of 11,065, March 2021). The development of data warehouses has been facilitated by the efforts made within the community to standardize the different steps of the metabolomics analysis workflow.
g. , thanks to optimized and validated acquisition workflows, dedicated spectral databases) -Standardized data production workflow with improved robustness (e. g. , with QCs, batch-to-batch consistency/ normalization, large-scale quantification) and capability of automated interpretation of the huge amount of data generated -Linked untargeted and targeted quantitative approaches for proper analytical validation of biomarker candidates The issue of design of experiment regarding cohort samples: toward more standardization Metabolomics is a question-driven method. Thus, the prerequisite of a successful metabolomics experiment is a welldefined clinical question, which would unarguably imply active discussions between physicians, analytical chemists, and data scientists, each one knowing the constraints and requirements of their respective field.
Complementarily, accurate retention time(s) and MS/MS spectra can provide high confidence in metabolite identification when matched to reference data included in chemical/spectral libraries. Among those complementary information lines, one of the most valuable elements to confirm metabolite annotation or reduce the list of possible annotations is the acquisition of fragmentation spectra and their comparison to reference MS/MS spectra included in reference mass spectral libraries  [82]. Differently from proteins, fragmentation of metabolites under low-energy conditions is relatively unpredictable (at least with a high confidence level). Therefore, the most relevant mass spectral databases for definitive identification of metabolite biomarker candidates are those obtained from pure authentic standards, and are thus unfortunately limited by their (most often commercial) availability.
Databases are essential but often reflect only what is identified and commercially available Most widely used (public and proprietary) spectral databases include MassBank  [84] , HMDB  [85] , GNPS  [86] , MoNA (https://mona. fiehnlab. ucdavis. edu/), LIPID MAPS  [87] , NIST 20 (https:// chemdata. nist. gov/dokuwiki/doku. php?id = chemdata:msms) , METLIN  [88] , and mzCloud (https://www. mzcloud. org/). For a more detailed comparison and discussion of these databases, we recommend these articles in the field  [89, 90]. However, in brief, procedures for data collection and curation, instruments used (e. g. , Orbitrap, Q-TOF), fragmentation conditions (e. g. , resonant and/or non-resonant conditions, number of collision energies, MS n ), and type of molecules differ from one database to the other.
Presently available spectral databases are therefore not strongly overlapping  [90] , which underlines their complementarity as exploited by recent studies for larger metabolite identification  [91]. Interestingly, a European proposal for quality control and quality assurance of tandem mass spectral libraries has been recently published and also reported that Q-TOF and Orbitrap-based instruments yielded comparable MS/MS spectra  [92]. As a representative example, the MS/MS spectrum of taurocholic acid as acquired in the positive ion mode on a first-generation Thermo Q-Exactive instrument (at a normalized collision energy "NCE" of 20%) has been successfully matched to spectra stored in the MoNA database and previously recorded both on a Q-Exactive HF and a Waters Q-TOF II instruments used under different fragmentation conditions (Fig.
3 ). This opens the door to spectral databases generated in the FAIR (i. e. , "Findable, Accessible, Interoperable, Reusable") data context  [93]  also with a standardized way of describing the observed ions  [94]. Although covering more and more metabolites, not all metabolites found naturally in biological matrices are currently commercially available to feed those databases, which clearly limits their expansion. As a matter of fact, metabolomics researchers usually focus on metabolites that are present in databases or websites. Although very exciting for the analytical chemist, the exploration of unknown metabolites, the socalled dark matter  [95] , is one of the biggest challenges and a research field on its own and is therefore still insufficiently explored. Therefore, acquisition and thorough annotation (or putative identification) of MS/MS data from biologically relevant and recurrently observed metabolite features absent from databases and chemical provider catalogs is required for their further implementation and storage in specific shared databases.
It can include (i) run order randomization to control unwanted variation and unwanted correlation; (ii) internal standards added to samples in order to report the quality of data within and between batches; (iii) blank samples to assess the occurrence of contaminations, interferences, artifact feature, carry-over phenomenon, and matrix effect (if internal standards are added); and (iv) QC samples (including diluted QC in order to highlight artifactual features). QC samples should be representative of the qualitative and quantitative composition of the samples to be analyzed. The samples can be obtained by pooling aliquots of each original sample and then following the same sample preparation protocol of the biological samples. Proposed in 2006 for the first time for untargeted metabolomics studies in order to check for data quality  [100] , QC samples have been and are used in numerous studies and several publications have highlighted their importance in the metabolomics workflow  [59, 61, 98, 99, 101].
e. , detection and integration of LC/MS peaks, i. e. , features), feature alignment and feature integration workflow. This is a key step in untargeted metabolomics, which leads to data matrices that have to be cleaned and annotated before being subjected to statistical analyses. Software packages dedicated to NMR, GC-MS, or LC-HRMS are available for that, and their description is beyond the scope of this review. There is a need to standardize these pre-processing steps  [102] , but most importantly to standardize the sharing of processed data so that they can be re-analyzed by other investigators to advance in the development of these tools. This can be achieved by integrated software tools that combine several data treatment steps, from data preprocessing to statistical analyses, such as workflow4metabolomics  [103]  or Metaboanalyst  [104] , the latter also proposing some data visualization and metabolic pathway enrichment tools.
Such metabolomics data-processing platforms enable to store and share all the parameter values related to data pre-processing and processing. Dataset annotation and metabolite identification are crucial steps enabling to convert hundreds to thousands of metabolites features contained in peak tables into biologically interpretable data. The way of reporting metabolite identification has already been highlighted as a key issue. The Chemical Analysis Working Group of the Metabolomics Standards Initiative has thus proposed four levels of confidence for metabolite identification  [105] : -Level 1: fully identified compounds based on at least 2 orthogonal (i. e. , independent) data related to an authentic standard analyzed in the same experimental conditions. -Level 2: putatively annotated compounds based on characteristic physicochemical properties or spectral similarity with spectral libraries.
In this case, there is one candidate, but the authentic standard is not available for confirmation. -Level 3: putative characterized compound classes, based on characteristic physicochemical properties of a chemical class (i. e. , a cylcarnitine derivatives, or sulfoconjugates). In this case, several candidates are possible and it is not possible to highlight a precise chemical structure. -Level 4: unknown compounds. [M+H]+ ion at m/z 516. 2974. Spectral matching was performed using the MS-DIAL version 4. 12) software  [208]. DP, dot product; rev. DP, reverse dot product Although they are simple to implement, these identification levels have limitations since they cannot avoid ambiguities. For example, the use of at least two orthogonal data together with that of a standard, as it is required for a level 1 metabolite identification, does not always lead to an unambiguous identification, as observed with optical isomers such as enantiomers, for example  [106].
The authors focused on 200 identified metabolites contained in their spectral database. Normalization was performed by dividing each feature's intensity recorded on the different days by the corresponding feature's intensity recorded in the freshly collected urine sample at day 0 for every subject. Furthermore, scaling factors were calculated by dividing the average intensities recorded in -80 °C stored samples per feature and for each batch by the corresponding intensities recorded in the reference batch. By these means, they found that urine metabolic profiles are altered starting from 5 days when stored at + 4 °C, and after one month at -20 °C. The temperature of -80 °C was considered as the most convenient urine long-term storage condition. In another study, Palmer et al. have investigated the 12-month stability of dried blood spots (DBS) and dried urine spots (DUS) at different storage temperatures (-20, + 4, and + 21 °C) and compared it to plasma and urine biofluids stored at the same storage temperatures and time by using LC/ HRMS-based untargeted metabolomics  [115].
7% or -14. 5%, respectively  [116]. Otherwise, Wagner-Golbs et al. analyzed EDTA plasma samples stored for up to 16 years by gas and liquid chromatography-tandem mass spectrometry-based quantitative metabolomics. They found that 226 out of 231 metabolites remained stable during the first seven years of storage  [117]. There is a limited number of large-scale studies using untargeted metabolomics that have been published. Among those, a representative one is that of Dunn et al. who have reported on the molecular phenotyping of 1200 "healthy adults" from the UK in the age range of 19-81 years, by using GC/MS and LC/HRMS-based metabolomics  [118]. The data were acquired across 11 months in 10 batches including samples from 120 subjects analyzed within a five-day period. Data were processed using dedicated GC-and LC-HRMS workflows, and inter-batch normalization was achieved thanks to a unique QC sample and the LOESS algorithm.
From 259, 7813, and 7914 metabolite features initially detected in GC-MS, LC-HRMS (positive mode), and LC-HRMS (negative mode), the implementation of signal correction, batch integration, and quality assurance procedures led to 126, 2181, and 2283 metabolite features combined within a single multi-batch data matrix and available for statistical analyses. Another interesting study is that of Sindelar et al. on the use of metabolomics to highlight prognostic markers of COVID-19 severity  [119]. Seven hundred and four human plasma samples were collected at six-month longitudinal points from 341 patients, and SRM 1950 was used as QC sample. Given that the metabolic profiles were acquired over several months, the combined data showed strong batch effects that proved efficiently corrected by combined batch correction  [120].
Chromatographic retention time shifts are the main issues regarding batch fusion over long periods. Particular software solutions have been described to correct within-and betweenbatch variability drifts in terms of mass accuracy, intensity, and retention times  [121, 122]. Nevertheless, if there are too large differences in retention times between batches, it can become difficult to correctly align peaks with common automatic signal detection and alignment software tools  [123, 124]. Then, targeted detection of metabolites that are present in laboratory spectral databases can be carried out using peak integration software from instrument suppliers. The batch fusion process would then be carried out more easily on the basis of known metabolites (targeted data treatment) and no longer based on m/z ratios and retention times alone and in a more blinded way (untargeted data treatment).
The metabo-ring initiative gathered 5 NMR and 11 different LC/HRMS platforms with the objective of assessing the reliability of untargeted metabolomics approaches in obtaining comparable metabolomics profiles. Biological samples obtained from 2 different conditions were analyzed by the partners using their own in-house protocols. It was observed that, despite large differences in the number of spectral features produced after post-processing and the heterogeneity of the analytical conditions and the data treatment, the spectral information within and across technologies (NMR vs. LCMS) was highly convergent regarding 2 test datasets in terms of statistical analysis  [126]. Izumi et al. performed an inter-laboratory comparison study on cell line extracts including 12 participating laboratories using their own analytical methods (capillary electrophoresis coupled to mass spectrometry, GC/MS and LC/MS with different kinds of high-resolution mass spectrometers and chromatographic conditions, corresponding to 15 and 9 methods for hydrophilic and lipophilic compounds, respectively).