dataesr/scientific-paragraphs-categorization huggingface dataset:
nasa. gov/giovanni/) to access the National Aeronautics and Space Administration (NASA) OMI/Aura NO 2 Cloud-Screened Total and Tropospheric Column dataset  72  , for a region including all backtrajectories (40°E to 108°E, 40°N to 10°S). Aerosol optical density satellite measurements for the same period are reported in Fig. S9. Statistics. In total, 41 PM 2. 5 samples were initially collected during the campaign, but one filter was excluded due to technical problems with the pump at the time of collection. For the 40 samples included in the analysis, quality assessment of sample variation by PCA showed no outliers (Fig. S10 ). Multivariate analyses (i. e. PCA and OPLS models) were performed in SIMCA v. 16 (Umetrics/Satorius); See Supplementary Information, Chemometrics, and Supplementary data 3 -Models and statistics.
Acknowledgements This research was supported by grants from the  Swedish Research Council for Sustainable Development, Formas  (Grants  2017-00567  and  2020-01917 ) and the  Swedish Research Council  (Grants  2018-03409 ,  2017 -01601 ). MCOH is operated by the  Maldives Meteorological Service (MMS)  and funding for the operation of the site comes from  Formas  (Grant  942-2015-1061 ) and the  Swedish Research Council  (Grants  2015-03279  and  2017-01601 ). We thank the technical staff at MCOH for collecting and shipping air samples and quality controls. We thank  Jan T. Andersson  ( University of Münster, Germany ) for donation of sulfur-containing PAH standards, and  Joël Boustie  ( Rennes Institute of Chemical Sciences, France ) for donation of acetyl portentol standard. We thank  Hiroshi Tsugawa  ( Tokyo University of Agriculture and Technology, Japan ) for support with MS-DIAL to facilitate the analysis of environmental contaminants.
The molecular consequences of cancer associated mutations in Acute myeloid leukemia (AML) linked factors are not very well understood. Here, we interrogated the COSMIC database for missense mutations associated with the RUNX1 protein, that is frequently mis-regulated in AML, where we sought to identify recurrently mutated positions at the DNA-interacting interface. Indeed, six of the mutated residues, out of a total 417 residues examined within the DNA binding domain, evidenced reduced DNA association in in silico predictions. Further, given the prominence of RUNX1's compromised function in AML, we asked the question if the mutations themselves might alter RUNX1's interaction (off-target) with known FDA-approved drug molecules, including three currently used in treating AML. We identified several AML-associated mutations in RUNX1 that were calculated to enhance RUNX1's interaction with specific drugs.
Specifically, we retrieved data from the COSMIC database for cancer-associated mutations of RUNX1 by using R package "data. table" and "ggplot2" modules. In the presence of DNA and/or drug, we used docking scores and energetics of the complexes as tools to evaluate predicted interaction strengths with RUNX1. For example, we performed predictions of drug binding pockets involving Enasidenib, Giltertinib, and Midostaurin (AML associated), as well as ten different published cancer associated drug compounds. Docking of wild type RUNX1 with these 13 different cancer-associated drugs indicates that wild-type RUNX1 has a lower efficiency of binding while RUNX1 mutants R142K, D171N, R174Q, P176H, and R177Q suggested higher affinity of drug association. Literature evidence support our prediction and suggests the mutation R174Q affects RUNX1 DNA binding and could lead to compromised function.
RUNX1 shows allosteric functions, for example, promoting Ets1-DNA binding through DNA-enhancer driven effects upon the DNA  (Shiina et al. , 2015). That is, RUNX1 activates the ETS1 transcription factor through the TCR enhancer, enabling ETS1 to play a role in lymphoid differentiation, proliferation, apoptosis, embryonic development, and angiogenesis  (Dittmer 2003). This RUNX1-ETS model is helpful in addressing how cancer mutations play roles in DNA mediated allosteric functions and RUNX1-DNA binding. Here, we asked what are the consequences of selected RUNX1 missense mutations in relation to the association of RUNX1 with DNA, and a range of FDA (Food and Drug Administration) approved drugs, including three designed to treat AML. To address these questions, we evaluated the cancerassociated missense mutations of RUNX1 as obtained from the comprehensive Catalogue Of Somatic Mutations In Cancer (COSMIC) database (http://cancer.
Data mining Cancer associated mutation data for the RUNX family of transcription factors were retrieved from COSMIC v85 (https:// cosmic-blog. sanger. ac. uk/cosmic-release-v85/). We extracted RUNX1 DNA binding domain associated missense mutations to map them on available RUNX1 complex crystal structures. The mutations' functionalities were checked and taken into account on the basis of functional analysis through use of the hidden Markov model (FATHMM), with scores provided by the COSMIC database. The FATHMM score ranges from 0 to 1. Mutations were classified as scoring below 0. 5 score (neutral), scoring above 0. 5 and below 0. 7 (deleterious), or scoring greater than 0. 7 (pathogenic) (https://cancer. sanger. ac. uk/cosmic). We considered mutants within this pathologic (>0. 7) range (Supplementary Figure  S1 ) that were supported by the literature, or where there were indications of an interface between RUNX1 and DNA.
(https://cran. r-project. org/web/packages/data. table/ vignettes/datatable-intro. html) to process and visualize the RUNX mutational data from the COSMIC dataset. To avoid duplicates, we excluded different isoforms and evaluated only the primary isoform of RUNX1 that is most widely expressed. We used UNIPROT numbering schemes for the RUNX1 DNA binding domains. Sequence logos for human RUNX1 DNA binding domains were prepared with web logo (https:// weblogo. berkeley. edu/logo. cgi). Candidate drugs for docking were selected by examining the literature for a notable functional impact upon their targets. The structures of drug compounds were downloaded from the PubChem database (https://pubchem. ncbi. nlm. nih. gov/).
Structural modelling and mutation mapping Crystal structures used in this study were downloaded from the protein data bank. We used structures of the RUNX1-ETS complex on the TCR enhancer (PDB ID: 3WU1)  (Shiina et al. , 2015) , PARP1 (PDB ID: 5XSR)  (Chen et al. , 2018) , EGFRTKI (PDB ID: 5EDP)  (Hanan et al. , 2016) , BCR (PDB ID: 2H32)  (Bankovich et al. , 2007) , and IDH (PDB ID: 6NZM)  (Hopkins et al. , 2019). Cancer missense mutations were generated on various mutation models for RUNX1 with UCSF Chimera version 1. 15  (Pettersen et al. , 2004 ) (http://www. rbvi. ucsf. edu/ chimera). Newly generated mutation models of RUNX1-ETS on the TCR enhancer complex were subjected to energy minimization using 1,000 steps steepest decent and 500 steps of the conjugate gradient algorithm with the step size 0.
Selection and distribution of cancerassociated missense mutations We retrieved cancer-associated mutations to identify those with the potential to alter RUNX1: DNA interactions. To this end, we retrieved the COSMIC mutation dataset (release v85) for missense mutations associated with the RUNX family, for which RUNX1, RUNX2, and RUNX3 have 746, 125, and 108 missense mutations, respectively. For RUNX1, out of 746 total mutations, 417 reside within what is defined as the DNA binding domain (DBD). Based upon known RUNX1: DNA (co-crystal) structures (PDB ID: 3WU1)  (Shiina et al. , 2015) , we found a much more restricted set of six mutations likely to have a direct impact upon the DNA association of RUNX1 (Figure  1A ). RUNX1 exhibits the highest number of missense mutations in comparison with RUNX2 and RUNX3 (Figure  1B ).
As noted, we mapped missense mutations to the crystal structure of the RUNX1: DNA complex (PDB ID: 3WU1) to identify interface-interacting amino acid residues within RUNX1. Mutation mapping revealed that positions Arg80, Arg142, Asp171, Arg174, Pro176, and Arg177 interact with DNA, while COSMIC database analysis suggests that these same positions are additionally associated with cancer-associated missense mutations. Although mutations at positions His78, Lys83, Arg135, Thr169, and Val170 are likewise associated with DNA-interactions, they do not appear to be associated with any type of cancer (Figure  1C ). To see the DNA binding consequences of cancer-associated missense mutations of RUNX1 and wild type RUNX1, we respectively explored Jurkat-cell RUNX1-ChIP-Seq (GSE17954) and LNCaPcell RUNX1 Chip-Seq (GSM1527839)  (Dasari and Tchounwou 2014) , and used the homer motif of the RUNX1-ETS complex for the protein DNA model (Figure  1D ).
Cancer drug associated targets and RUNX1 We hypothesized that cancer-associated drugs would display higher modeled ΔΔG values for their expected/known targets and lesser ΔΔG values for RUNX1. On the basis of this prediction, we subjected Gefitinib, Niraparib, Ibrutinib, and Enasidenib for further docking studies with their respective published targets (Figure  4A ). We performed docking of Gefitinib with its published target, the epidermal growth factor receptor tyrosine kinase (EGFRTKI, PDB ID: 3W2S). As expected, we found that Gefitinib exhibited more significant ΔΔG values for binding to EGFRTKI in comparison to RUNX1. RUNX1 residues Asp66, Met106, Gly108, Tyr113, Thr147, Thr149, Val159, and Thr161 were modeled to interact with Gefitinib (Figure  4A ). Niraparib docking with Poly [ADP-ribose] polymerase 1Poly [ADPribose] polymerase 1 (PARP1) also showed a higher ΔΔG value for association with PARP1 than with RUNX1.
Acknowledgments YS thanks the  Department of Genetics, University of Texas MD Anderson Cancer Center, Houston Texas USA , as well as prior support (while in China) from the outstanding doctoral dissertation program of the  University of Chinese Academy of Sciences (UCAS) , and the  Guangzhou Institutes of Biomedicine and Health (GIBH). HU was supported by  UCAS. BZ acknowledges the  Guangzhou Institutes of Biomedicine and Health (GIBH)  for infrastructure and financial support.
Analysis of Plasma and Aorta Metabolomes After validation of the three quality controls (QCs), 154 and 136 metabolites were correctly measured in plasma and aorta samples, respectively, and kept for the statistical analysis (see Supplementary Data S1 and S2 for plasma and aortas, respectively). Plasma and aorta data blocks were submitted to multiblock orthogonal component analysis (MOCA) after normalizing concentrations of aorta samples by their weights. In this multiblock model, the OnPLS-based algorithm found two globally (or locally in the case of two blocks) joint components tj,1 and tj,2 (Figure  3A ). The first joint component tj,1 explained up to 31. 6% and 35. 1% of the variance in the plasma and aorta metabolite matrices (i. e. , Xpl and Xao), respectively, and was the most informative component among joint and unique components for both plasma and aortas (Figure  3B ).
Analysis of Plasma and Aorta Metabolomes After validation of the three quality controls (QCs), 154 and 136 metabolites were correctly measured in plasma and aorta samples, respectively, and kept for the statistical analysis (see Supplementary Data S1 and S2 for plasma and aortas, respectively). Plasma and aorta data blocks were submitted to multiblock orthogonal component analysis (MOCA) after normalizing concentrations of aorta samples by their weights. In this multiblock model, the OnPLS-based algorithm found two globally (or locally in the case of two blocks) joint components t j,1 and t j,2 (Figure  3A ). The first joint component t j,1 explained up to 31. 6% and 35. 1% of the variance in the plasma and aorta metabolite matrices (i. e. , X pl and X ao ), respectively, and was the most informative component among joint and unique components for both plasma and aortas (Figure  3B ).
e. , metabolite extraction). 4. 9. Statistical Analysis of Metabolomic Data 4. 9. 1. Univariate Analysis Prior to statistical analyses, only metabolites with more than 70% of their concentration values in the dynamic range were taken into consideration. Data were then stored in two matrices named X pl and X ao for plasma and aorta matrices, respectively. These two matrices have dimensions m × k and m × l, where m is the number of samples (i. e. , mice) and k and l represent the number of metabolites correctly measured in plasma and aortas, respectively. Nonparametric univariate analysis (Mann-Whitney-Wilcoxon test) was carried out to detect significant variation in median metabolite concentrations and metabolite ratios and sums in both matrices between groups of mice pairwise. Benjamini-Hochberg correction was used for correcting risk I inflation due to multiple comparisons and to maintain false discovery rate (FDR) less or equal than 5%.
Wilcoxon testing was carried out using the function wilcox. test in R. Kendall's τ for nonparametric correlation and its associated p-values were calculated using function cor. test in R software. We determined the performance of discriminant classifiers (metabolites or metabolite sums or ratios) by calculating the area under the ROC curve (AUC) associated with the logistic regression model including these classifiers. Logistic regression and calculation of AUC and its 95% confidence interval using nonparametric Delong's method were carried using the package pROC in R software  [50]. A cut-off of 0. 80 was fixed for deciding whereas the classifier had good performance (AUC ≥ 0. 8) or not (AUC < 0. 8). R software is available from https://www. R-project. org/ (functionality verified on 19 April 2022).
Multivariate Analysis Plasma and aorta metabolomic data were simultaneously analyzed by the multiblock orthogonal component analysis (MOCA) method  [51]. MOCA is an unsupervised multivariate embodiment of the OnPLS algorithm, where OnPLS stands for orthogonal partial least squares, for n data matrices (in our case n = 2: plasma and aorta data matrices). MOCA splits information structures residing in data into correlated (joint) and uncorrelated (unique) sources of variabilities. Globally joint (or locally joint in the case of only two blocks) information is systematic structure (in the sense of principal component analysis, i. e. , variance explanation) found in both plasma and aorta data blocks, whilst unique information is additional systematic structure found only in one (plasma or aorta) single data block.
Roughly speaking, when a locally joint latent variable (t j ) is projected in the column space of X pl and X ao , then a significant amount of variance in each block is captured by t j. MOCA is implemented in SIMCA software, version 16 (available from www. sartorius. com/umetrics, 19 April 2022) using default procedures for data pretreatment, i. e. , all variables were mean-centered and scaled to unit variance prior to model calculations.
• New tools and methods for monitoring of chemical and microbial pollutants, and development of on-line (real-time) monitoring of water quality and process performance. A specific challenge is the monitoring of pathogens in raw wastewater and complex matrixes (sludge and soil), as well as new pollutants (nanoparticles, microplastics, antibiotic resistance). • Investigate and develop databases for a better understanding of pathogen removal efficiencies and the variability of performance of various unit processes in multi-barrier wastewater reclamation trains. (2) Health, social, and environmental issues: • Address critical gaps in the understanding of health impacts concerning human exposure to constituents in recycled water in comparison to other well-defined health risks (e. g. , dose response, relevant pathogen selection, target population, etc.
Consequently, ion pairing reactions are usually at equilibrium and their impact on struvite precipitation can be readily estimated using well-established aqueous equilibrium models. Such models are available as standalone software packages (e. g. , PhreeqC, introduced in this chapter) or have been incorporated into commercial wastewater modelling packages (e. g. , BioWin, Sumo19). These models calculate the resulting free ion concentrations [Mg 2+ (aq)] eq , [NH 4 + (aq)] eq and [PO  4 3-(aq)] eq at equilibrium with the various relevant ion pairs, and then these concentrations can be used to calculate IAP struv or SI struv via Equations (10. 9) and  (10. 10). These can then be used to determine if a wastewater is supersaturated or undersaturated, and thus whether struvite will precipitate or dissolve.
Worked exercise 1a As an illustrated example consider a wastewater containing 200 mg P/L as phosphate, 1000 mg N/L as ammoniacal N (i. e. , NH 4 + (aq) + NH 3 (aq)), 100 mg Mg/L and 2550 mg/L of choride as measured by an analytical laboratory (these are total concentrations and include all ion pairs and free ions), at pH 6. 12 and 25°C. We will use the model software package PhreeqC with the Minteq. dat model database to determine whether this wastewater is supersaturated or undersaturated with respect to struvite. PhreeqC is available for download at https://www. usgs. gov/software/phreeqc-version-3. PhreeqC is free to use within the constraints of its User Rights Notice, available at https://water. usgs. gov/waterresources/software/PHREEQC/Phreeqc_UserRightsNotice. txt. The wastewater composition above is entered into PhreeqC, and the thermodynamic database used by the model is set to Minteq.
19)  which relates the equilibrium constants K(T 1 ) and K(T 2 ) at two temperatures T 1 and T 2 in degrees K, respectively, and where R is the universal gas law constant (8. 3145 × 10 -3 kJ per mol per K), and ΔH 0 is the standard enthalpy of reaction (52. 22 kJ per mol for the reaction in Equation (  10. 17) at 298 K). ln K T K T H R T T 2 1 0 1 2 1 1 ( ) ( )               = -            ∆ ( The resulting ion pairing effects can also be calculated at various pH and temperature values using PhreeqC (see section  10. 3. 1)  with the following code: SOLUTION 1 temp 25 pH 8 pe 4 redox pe units mg/L density 1 N(-3) 1000 Cl 0. 01 charge -water 1 # kg Note in this code that the simulation is set to balance ionic charge in the water by altering the concentration of a nominal inert counter-ion, in this case chloride, to ensure that the simulation correctly aligns its calculations with the nominally set pH value.
As soon as the new Fertilising Products Regulation will be enforced, the PPP Regulation will cease to be relevant to the wastewater sector. This regulation lays down provisions on substances and mixtures that shall apply to the manufacture, placing on the market or use of such substances on their own, in mixtures or in articles. This makes it very relevant for materials recovered from (waste-)water with the goal to bring it into the market. REACH holds industry accountable to manage the risks from chemicals and to provide safety information on the substances. Manufacturers and importers are required to gather information on the properties of their chemical substances, which will allow their safe handling, and to register the information in a central database in the European Chemicals Agency (ECHA) in Helsinki.
The Agency is the central point in the REACH system: it manages the databases necessary to operate the system, co-ordinates the in-depth evaluation of potentially risky chemicals and is building up the public database in which consumers and professionals can find hazard information. The regulation also calls for the progressive substitution of the most dangerous chemicals (referred to as 'substances of very high concern') when suitable alternatives have been identified. REACH requires increasing levels of information when larger volumes of a given substance are manufactured and placed on the market. Requirements are stipulated in the annexes and aim at reflecting the level of potential impacts from (hazardous) substances and expenses needed for compliance. Several substances are exempt from REACH: (i) radio-active substances; (ii) substances used in food; (iii) pharmaceuticals; and (iv) waste.
The recast of the Renewable Energy Directive (RED-recast) (COM(2016) 767), in Art. 26  (7)   (European Commission, 2017) , specifies the minimum greenhouse gas (GHG) emissions saving thresholds that bioenergy must comply with to count towards the renewables targets and to be eligible for public support. Annex V (liquid biofuels) and Annex VI (solid and gaseous biomass) of the RED-Recast describe the methodology for GHG savings calculations needed to comply with the GHG criteria. They also provide a list of default GHG emission values, aggregated and disaggregated, that operators can use to demonstrate compliance of their product with the GHG criteria. A corresponding JRC report (JRC Science for Policy Report, 2017) describes the input data, assumptions and methodological approach applied by the JRC when compiling the updated dataset used to calculate GHG emissions for the different biomass pathways.
LCA standards (ISO standards) The quality and relevance of LCA studies, and the extent to which they can be applied and interpreted, depends upon the methodology used. Transparent documentation is crucial. The International Organization for Standardization (ISO) published standards to provide guidance on methodological choices, transparency, and rules for reporting. Examples of LCA-related ISO standards are ISO 14040 (Environmental management -Life cycle assessment -Principles and framework) and ISO 14044 (Environmental management -Life cycle assessment -Requirements and guidelines). At a minimum, ISO standards state that a well-documented LCA should contain the following content and details: approach and method, goal definition, explanation of the scope, description of the evaluated scenario(s), selection of functional unit, description of system boundaries, and details on data collection and methodology (software, database, method, etc.
To this end, only the chemicals required (and their transport) to recover flocculants from iron sludge ('FeCl 3 recovered' scenario) are roughly compared with the purchase of a commercial flocculant, including its manufacturing and transport ('FeCl 3 commercial' scenario). Also, a third scenario, employing a recycled acid, is investigated as the 'FeCl 3 recovered green' scenario. The inventory data for these three scenarios is presented in Table  16. 2. This analysis will not only help characterize the potential of the suggested concept, but may also identify hot spots for continuation of scientific research. The quick scan LCA analysis used the SimaPro 8. 0 software, EcoInvent 3. 0 database, and ReCiPe Endpoint E 1. 12 method. The functional unit (FU) is the production of 1 kg of flocculant, as the function of both scenarios is to deliver high-quality flocculants.
They can also serve as a raw material for limestone products. In the innovative concept, the garnet seeds are replaced by calcite seeds, resulting in residual pellets that contain 100% calcite. The pure calcite pellets are dried, milled, and sieved at an external location to produce both the calcite seeds and a commercial-grade fine milled limestone product, which is sold to third parties for high-end applications. The data inventory of the conventional scenario (using garnet as seed) and the innovative concept (using calcite as seed) are presented in  Figure 16. 4 and Table 16. 3 , reflecting all process changes that will be evaluated in the LCA. In short, this analysis employed SimaPro 8. 0 software, the EcoInvent 3. 0 database, and the ReCiPe Endpoint (E) 1. 12 method. The functional unit is the removal of 1 ton of calcium carbonate from water, so that the comparison is based on the production of this recovered resource.