dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, this framework provides a clear audit of the isotopic information that can actually be measured on proteinogenic amino acids. Data integration and consolidation. To quantitatively evaluate the proposed integrative framework, we produced a reference sample of  13  C-labeled proteinogenic amino acids with controlled and predictable labeling patterns in which all isotopic species are present in equal amounts  9. This sample was analyzed by NMR (ZQF-TOCSY, HSQC, HNCA and HACO-DIPSY experiments) and MS (GC-MS and LC-MS), yielding eight independent datasets (D1-D8 in Figure  2 ) containing a total of 21 isotopic measurements for alanine (Supporting information S1). For all combinations of datasets identified as providing complete isotopic coverage of alanine (Figure  3 ), we used IsoSolve to determine the abundance of each isotopomer, cumomer and EMU.
The results obtained for each combination were evaluated using two quantitative metrics  19  : accuracy (defined as the mean error and calculated from the differences between theoretical and measured abundances) and precision (defined as the mean standard deviation of the measured abundances). All the isotopomers, cumomers and EMUs of alanine were indeed quantified for all the combinations considered. The accuracy and precision of the results depended on the datasets included (Figure  5 ). A detailed analysis of the integration results reveals that precision and accuracy also depend on the isotopic species considered (Figure  6 ). Combining HSQC and LC-MS data is sufficient to reliably quantify 6 of the 8 isotopomers of alanine. Isotopomers 000 (accuracy = 0. 002, precision = 0. 009) and 011 (accuracy = 0.
020, precision = 0. 020) are for instance well resolved, but isotopomers 001 (accuracy = -0. 060, precision = 0. 070) and 100 (accuracy = 0. 102, precision = 0. 082) remain poorly resolved. Adding the TOCSY dataset significantly improves quantification for the two latter species (001: accuracy = 0. 018, precision = 0. 022; 100: accuracy = -0. 008, precision = 0. 025). The most reliable results were obtained by integrating all the datasets (001: accuracy = 0. 018, precision = 0. 013; 100: accuracy = -0. 007, precision = 0. 010). Here again, similar trends were observed for cumomers and EMUs (Figure  6 ). In combinations with measurement redundancy, the consistency of the different datasets can be evaluated using a chi-square test. For instance, when all datasets were combined (D1-8, combination #254, 10 redundant measurements), the chi-square test confirmed that all the measurements were consistent (p-value = 0.
994). When some of these measurements were artificially altered, e. g. b changed from 0. 2533 to 0. 0533 and c from 0. 2475 to 0. 4475, the pvalue decreased to 2×10  -16  , highlighting the inconsistencies between the redundant measurements. This illustrates how the proposed approach can be used to identify biased measurements to be checked before interpretation. These results confirm that integrating additional datasets improves both the accuracy and the precision of isotopomer quantification, very likely because of the high degree of redundancy (up to 10 redundant measurements) which reduces the impact of experimental noise and the potential biases of individual measurements. Overall, all isotopomers could be reliably quantified using a wide variety of data combinations, with a high accuracy and precision in most situations.
CONCLUSION The complementarity of different (MS and/or NMR) approaches dedicated to isotopic analyses is often highlighted, but the lack of a generic integrative framework able to deal with heterogeneous, partial isotopic measurements has meant that this has never been evaluated in detail. The proposed framework fills this conceptual gap by allowing any type of isotopic measurements (MS, MS/MS, 1 H-NMR,  13  C-NMR,  15  N-NMR, etc) to be included. The framework is agnostic to the analytical platform, the labeled element, the tracer isotope, and the molecule. It can also be applied to double-labeling approaches (e. g. 13  C and 15 N). This framework has been implemented as an open source Python program, IsoSolve, which is available as a command-line interface and as a Python library to streamline its integration into existing data analysis pipelines.
Using amino acids as an example application, we have demonstrated that this framework can i) clarify the actual coverage of isotopic space by identifying the (sets of) species that can actually be quantified, ii) improve this coverage by increasing the number of isotopic species that can be quantified individually, iii) evaluate the complementarity and redundancy of different techniques, iv) consolidate isotopic datasets by evaluating their consistency, identifying biased measurements, and reducing the impact of measurement noise, v) support experimental design by identifying the most relevant methods to quantify a given set of isotopic species, and vi) guide future analytical developments. Our framework connects measurements to the chemical structure of compounds and to their formal representation in isotopic models of metabolism, hence assisting both model-free and model-based data interpretation.
This framework may thus support structural investigations (e. g. metabolite identification, spectra annotation, validation of MS/MS fragmentation patterns, development of stand-ardized databases for deposition of isotopic datasets based on isotopically-resolved InChIs) as well as functional investigations of metabolic systems (e. g. experimental design, data consolidation, conversion between isotopic representations in  13  C-fluxomics workflows). It should also make isotope labeling experiments more accessible to the wider biological community. Beyond isotopic studies, this framework may prove equally valuable in other fields dealing with the analysis of combinatorial states of biological entities. This is the case in proteomics for instance, for the analysis of post-translational modifications (e.
ACKNOWLEDGMENT The authors thank  MetaboHub-MetaToul  ( Metabolomics & Fluxomics  facilities, Toulouse, France, http://www. metatoul. fr),which is part of the  French National Infrastructure for Metabolomics and Fluxomics  (www. metabohub. fr),funded by the  ANR  ( Metab-oHUB - ANR-11-INBS-0010 ), for access to NMR and MS facilities. JCP is grateful for funding from  INSERM  for his temporary fulltime researcher position.
Finally, our findings may also augment local and regional food system models and assessments, as incorporating our results in models could enable quantifying aspects beyond the use of resources onlysuch as the substantial impacts on Biosphere integrity. At the same time, it should be noted that the revealed mechanisms, their strengths, and even the directions of interactions might vary in different contexts, and the results presented here show only their aggregate outcomes. Further, a better understanding of local-scale mechanisms potentially cascading to planetary-scale feedbacks  (Rocha et al. 2018 ) is needed for prioritising management actions. Data sets for many of the control variables are already available for more local-level assessments to be carried out while maintaining global consistency.
2016; Bedford & Cooke 2001 ) from the CI they provided. This standardisation was performed to fit a PERT distribution that takes three parameters: the lower, upper and best (most likely) value. 2. Non-region-specific lower and upper values were aggregated with an unweighted median. 5. Each non-region-specific distribution was compared to the non-region-specific aggregated distribution with the Kullback-Leibler divergence metric within the 'LaplacesDemon' R package  (Statisticat, LLC 2020). The 95 th percentile of divergence values were used as the limit for aggregation acceptance for the region-specific distributions. 6. Each region-specific distribution was compared to the non-region-specific aggregated distribution with the Kullback-Leibler divergence metric. 7. Region-specific distributions with divergence below the aggregation limit were accepted for aggregation.
Acknowledgments This study was funded by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 819202 ),  Aalto University School of Engineering Doctoral Programme , Maa-ja vesitekniikan tuki ry, and the  Academy of Finland  -funded project  TREFORM  (grant no. 339834 ). BIS is supported by a  Royal Commission  for the Exhibition of.
Data Collection Data collection was performed retrospectively using a standardized data collection form. Patient and disease characteristics at the start of ICI were extracted: age, gender, Eastern Cooperative Oncology Group performance status, primary tumor (melanoma, lung cancer or urologic cancer (bladder or renal cancer)), number of metastatic sites and presence of brain, bone or visceral metastases. Adverse events were categorized as irAEs when at least possibly attributed to the ICI, based on the judgement of the treating physicians. The grading of irAE severity was based on the Common Terminology Criteria for Adverse Events, version 4. 0. IrAEs included pneumonitis, colitis, thyroiditis, dermatitis, colitis, hepatitis or any adverse events at least possibly related to an activation of the immune system according to the treating physician.
Our study has some limitations. Its retrospective nature makes it at risk of misclassification bias, but it is notable that the amount of missing data was very low, and it included a large number of patients. Only grade 3 and 4 irAEs were included in the analysis, as grade 2 irAEs were suspected to be less exhaustively described in medical files used retrospec-tively to collect data. The relation with the ICI of these severe adverse events is also expected to be easier to establish by clinicians. The study included patients with various cancer types, as the relationship between irAEs, irAEs management, and ICI efficacy was hypothesized to be similar according to tumor types. However, we cannot exclude that some mechanisms might be tumor specific. For example, the link between vitiligo occurrence and ICI efficacy might be specific to melanoma patients.
Data and Statistical Analyses Knee translations/rotations were calculated from the optically tracked positional data using a customized MatLab script (Version 9. 5, MathWorks) adopting a well-established coordinate system  [39]. Anterior laxity was calculated from the anterior tibial translation data by subtracting the neutral load case data from the 90 N anterior drawer data. Similarly, posterior laxity was calculated by subtracting the neutral data from the 90 N posterior drawer data. When combined, these give overall A-P laxity (the range of anterior-posterior tibial translation) under 90 N anterior/posterior drawer. For a typical flexion/extension cycle, the optical tracking system recorded 400 data points. To enable statistical analyses, these data were sampled at 10° flexion increments from 0° to 90° flexion.
Intra-state measures of tibial translation were tested using the Shapiro-Wilk test and found to be normally distributed. Data were analyzed with a two-way repeated measures analysis of variance in SPSS (version 25; IBM). The dependent variable was the change in tibial translation when subjected to 90N anterior or posterior drawer and the independent variables were implantation state (native, partial knee arthroplasty, TKA) and flexion angle (0-90° in 10° increments). Anterior and posterior tibial translation data were analyzed separately resulting in six analyses (both directions, for each of the three knee sets: medial, lateral and bi-unicondylar). Post hoc paired t tests with Bonferroni correction were applied when differences across tests (as indicated by the 95% confidence interval) were found.
Moreover, the intermediate models introduced in our work allow decision makers to analyse tradeoffs involved with requiring connectivity or buffer zones. The article is organized as follows. In Section 2 we incrementally develop an ILP-formulation for the GRSC-CB by first giving a generalization of the RSC without considering connectivity and buffer requirements, and then introducing constraints to model these two requirements. In Section 3, we describe a branch-and-cut framework to solve the proposed formulation. Computational results on synthetic instances, as well as case studies on real-life instances based on data of the U. S. National Gap Analysis Program and of the Northern Australia Water Futures Assessment Program are presented in Section 4. Section 5 concludes the paper.
Thus, in total, we have 240 grid-graph instances (four sets times ten instances times three scenarios times two different values of k). GAP-Instances These real-life instances are based on data of the National Gap Analysis Program (GAP), an initiative of the U. S. Geological Survey (USGS) (U. S. Geological Survey 2016a,b). We considered three states of different size, namely Oregon, Pennsylvania and Vermont. In total, 18 real-life instances are generated -three states times three scenarios (A,B,C, mentioned above) times two different values of k. A detailed case-study which is conducted on this data set is given in Section 4. 4.
1993) , and it is the result of decades of exhaustive efforts devoted to provide clear, geographically-explicit information on the distribution of native vertebrate species, their habitat preferences, and their management status, in order to determine actual needs in biodiversity protection. Among the different datasets provided by the GAP program, it is possible to obtain, for each U. S. state, a representation of its territory mapped into so-called hydrological units (HU); these units and their adjacencies are used to build G = (V, E). Although the databases supplied by the GAP program have been used before for analyzing the current wildlife conservation policies (see, e. g. ,  Drew et al. (2011) ,  Lacher and Wilkerson (2014) ,  Meretsky et al. (2012) , Minor and Lookingbill (  2010 )), we are not aware of OR-oriented papers using this GAP data.
For this study, we have used data from three U. S. states of varying sizes (Oregon, Pennsylvania and Vermont). For a given state encoded by a graph G = (V, E), the problem parameters are obtained as follows. • The set S consists of the terrestrial mammals living in the state according to the GAP data. All species which are classified as endangered or vulnerable (at federal or state level) by the corresponding Department of Wildlife are put into S 1 and the remaining species are put into S 2. The complete lists of animals in set S 1 for each considered state is given in Table  1. As an interesting observation, for Vermont, two of the species (American Marten, Eastern Mountain Lion) listed by the Department of Wildlife do not occur in the GAP-data of the state. • In the GAP-data, there are also species distribution models for each species available on a 30 meters × 30 meters cell basis, i.
In Figure  6b  we show how the number of threats spatially distributes in the considered area. Since we had explicit information of which species and which threats co-occurred in each land parcel, the suitability score for a given species s ∈ S in a given parcel i ∈ I is given by w s i = α s i 1 - #threats s i #threats s + 1 3 , where #threats s i corresponds to the number of threats in i that affect s, #threats s corresponds to total number of threats that affect species s, and α s i is a binary parameter taking value 1 if species s is hosted in land parcel i, and 0 otherwise. This expression was defined following the model proposed in  Cattarino et al. (2015). Since in this dataset species were not classified according to their level of vulnerability, we considered that all of them were part of S 1 (i.
Obtained results For the Mitchell river catchment dataset, we solved models GRSC, GRSC-B, GRSC-C and GRSC-CB, using the same algorithmic setting used for the U. S. wildlife dataset. Since S 2 = ∅, we imposed the buffer area to be a boundary of thickness given by d = 1 (Scenario C). Due to the structure of the stream network, the underlying graph was comprised by several connected components; this implied that both, the GRSC-C and the GRSC-CB were infeasible for k = 1 and k = 2, so we considered k = 3 in both cases. In Table  4  (equivalent to Table  3 ) we report a summary of the results obtained when solving the four models on the described instances; the corresponding solutions are shown in Figure  7. From the spatial point of view, we can observe that the GRSC solution (Figure  7a ) is highly fragmented (255 components, and 402 land parcels), which is basically due to the presence of many species distributed along the whole studied area.
e. , implementation of measures against the corresponding threats) due to their connectivity and compactness. From the computational point of view, it is clear that this dataset is much harder than the ones considered before. The two models requiring a buffer boundary (GRSC-B and the GRSC-CB) are considerably more difficult than those that do not require it. As can be seen from Table  4 , in these two cases, is not possible to prove optimality within the running time, and the reached gaps are 24. 34% and 36. 67%, respectively. Although the GRSC-C optimal solution was not found, the solution computed within the time-limit reached a 1. 28% gap. Despite the lack of optimality proof, the solutions provided for the different models represent the first attempt to address from a mathematical programming point of view, the conservation planning challenges arising in the Mitchell catchment area (see  Cattarino et al.
To assess the suitability of our approach, we presented a computational study considering grid graphs, as well as real-life instances representing three different states of the U. S. and a region in northern Australia. The U. S. instances were constructed using data from the National Gap Analysis Program, an initiative of the U. S. Geological Survey (U. S. Geological Survey 2016a,b), with the focus on the protection of mammal species. The Australia instance was constructed using information obtained by the Northern Australia Water Futures Assessment program Department of  Agriculture & Water Resources (2012) , and corresponds to a conservation setting of fresh water fishes. In our study, we compared the solutions obtained by using GRSC-CB model against the solutions obtained by using less restrictive models (i.
Acknowledgements  E. Álvarez-Miranda  acknowledges the support of the  National Commission for Scientific and Technological Research CONICYT (FONDECYT  grant  N. 1180670  and  Complex Engineering Systems Institute ICM : P-05-004-F/CONICYT:FB0816 ), and of the European Union's  HORIZON 2020 research and innovation programme  under the  Marie Skodowska-Curie Actions  (grant agreement N. 691149, SuFoRun Project). The research of M. Sinnl was supported by the  Austrian Research Fund  (FWF): P  26755-N19  and  P 31366-NBL.
The research on enterprises' digital transformation and ecological capacity of heavy metals has some foundation. However, there are still deficiencies in the following aspects, and this paper has made marginal contributions to this. First, based on ecological capacity theory, the ecological capacity of heavy metals in the watershed was evaluated. The feasibility and applicability of the three-dimensional state-space model applied to the evaluation of enterprise digital transformation were verified. Second, to enhance the accuracy of prediction simulation, data mining and other technologies are used to integrate quantitative and qualitative analysis focusing on visualization to reduce calculation errors. Third, the accuracy and diversity of evaluation indicators and methods should be increased.
Research samples and data Based on the information disclosure website of listed companies of the China Securities Regulatory Commission (CSRC), through digital mining technology  (Jia et al. , 2019) , enterprises with a high matching degree in the urban agglomeration of the middle reaches of the Yangtze River are selected. Companies were searched in batches according to the Google Maps API to obtain the company's securities abbreviation, listing date, registered address and other information. Finally, 430 samples were selected based on the key heavy metal treatment industries determined by the Opinions on Strengthening Pollution Prevention and Control of Heavy Metal-related Industries issued by the Ministry of Ecology and Environment of China. The data of heavy metal enterprises come from the Cninfo, Wind and CSMAR (China Stock Market Accounting Research) databases.
Secondary data such as operating conditions, financial statements and social responsibility reports of listed enterprises are extracted from the databases. The primary data of this paper are finally obtained through calculation, analysis, evaluation and simulation. Reference to existing research methods on enterprise green technology measurement  (Hassani et al. , 2020; Xie et al. , 2019). Based on data mining technology, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and content analysis method are used to score according to word frequency, data openness and implementation progress of test factors. The evaluation and analysis data of enterprise digital transformation are sorted out. The evaluation criteria are shown in Table  1.
Analytical framework Based on the TOXI module of WASP (The Water Quality Analysis Simulation Program), the concentrations of heavy metal pollution in urban agglomerations in the middle reaches of the Yangtze River in 2019 were simulated with time and space, and the ecological capacity of  heavy metals in the basin was calculated according to the material balance theory. Meanwhile, based on data mining technology, fuzzy-Dematel and a three-dimensional state-space model were used to evaluate the spatial heterogeneity of the ecological capacity of heavy metal enterprises. Combined with the inverse distance weight method in the spatial calculation module of the ArcGIS system  (Portnov et al. , 2009) , the spatial and temporal heterogeneity of the digital transformation of heavy metal enterprises and the ecological capacity of heavy metals was visualized.
Research method and technology Based on the relevant data of the concentration of heavy metals collected at each monitoring point, the heavy metal migration model has been applied to simulate concentration of heavy metal pollution. Then the ecological carrying capacity of heavy metals is calculated by applying the linear model of material balance. See line 5 for the calculation formula. The calculation process is shown in Formula 1, Formula 2 and Formula 3. The degree of digital transformation of enterprises has been calculated by applying the three-dimensional state space model based on the three major elements of business activities, environmental governance, and resource utilization. Then Fuzzy-Dematel is applied to determine the weight of each index and evaluate each element index. The inverse distance weighting method of GIS spatial analysis has been applied to analyze visually the spatial and temporal distribution heterogeneity of indicators including the ecological carrying capacity of heavy metal and the three elements of enterprises digital transformation.
Linear model of material balance The research data on the environmental capacity of heavy metals were mainly from 27 hydrological stations in urban agglomerations in the middle reaches of the Yangtze River in China. Information and data from 27 hydrological stations were collected from 19 cities (Tongling, Hefei, Chizhou, Wuhu, Jingzhou, Huanggang, Wuhan, Xiangtan, Hengyang, Yiyang, Changde, Yueyang, Zhuzhou, Yichun, Jiujiang, Ganzhou, Fuzhou, Shangrao, and Nanchang) in the middle reaches of the Yangtze River urban agglomeration in 2019. See Fig. 1  for details. The background values and environmental risk reference values of different river basins determine the static environmental capacity of rivers, namely, the static environmental capacity, which can be calculated as follows: Fig. 2. The technical framework.