dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, the data coverage by terrestrial observation networks (e. g. , the Aerosol Robotic Network; AERONET) is generally sparse despite a significant increase in the number of sampling sites worldwide during the last two decades. Likewise, simulated aerosol distributions (e. g. , The Modern-Era Retrospective analysis for Research and Applications; MERRA-2)  (Gelaro et al. , 2017)  are unable to resolve the composition of aerosol mixtures at scales comparable to those used by moderateresolution spaceborne imagers (footprint ~30 m), such as the Operational Land Imager (OLI) aboard Landsat-8. Due to these caveats, NASA's approach to the characterization of aerosol properties for each satellite observation is currently carried out based on discrete aerosol categories selected dynamically (e. g.
AERONET Measurements Optical and microphysical aerosol properties for the total atmospheric column were obtained from daily Version 3, Level 2. 0 inversion AERONET products between June 1993 and September 2019. The geographic distribution of measurements is shown in Figure  2. Our original database has 74,627 records from 806 monitoring locations. The quality control of Version 3, Level 2. 0 products includes an automatic cloud screening and instrument anomaly detection (e. g. , obstruction of field-of-view)  (Smirnov et al. , 2000; Giles et al. , 2019). The inversion of aerosol properties relies on fitting radiance measurements (i. e. , direct Sun and angular distribution of diffuse contributions) to radiance estimates derived from a radiative transfer model  (Dubovik and King, 2000). Consistently, all Sun and sky radiance measurements made by AERONET since 1993 have been performed with FIGURE 1 | Single Scattering Albedo (SSA) and asymmetry parameter (g) for selected A2010 (RH = 80%).
The models are sorted according to size fractions with increasing coarse modes from A1 through A9. A2  for specific site names). The green and magenta circles denote the sites whose observations were utilized in clustering. The sites without SSA/AE a inversions are marked with red circles. The data for developing PAM models were collected between October-1993 and September-2019. CIMEL sun-photometers  (Holben et al. , 1998). The spectral configuration of these instruments is based on the ultraviolet (UV) (λ = 0. 34 and 0. 38 µm), visible (λ = 0. 44, 0. 5, 0. 675 µm), as well as NIR and SWIR (λ = 0. 87, 1. 02 and 1. 64 µm) channels that had been slightly modified in some monitoring sites  (Barreto et al. , 2016). The daily arithmetic average of aerosol optical inversions is reported at four wavelengths (λ = 0.
3). Three aerosol properties met these criteria: SSA(0. 44), AE e, and AE a (Supplementary Appendix Table  A1 ). Indeed, preliminary results suggested minimum covariation between the variables above, as inferred from the coefficient of determination (r 2 < 0. 25). For instance, SSA(0. 87) was excluded from the analysis since preliminary results indicated a lower number of clusters when this property was included. Spurious AE a estimates were removed as they represented < 0. 5% of the total records used for clustering. An important assumption in our clustering is that aerosol properties excluded as input in the PAM classification are also unique for each cluster they are associated with because the considered aerosol properties are substantially correlated with those excluded. The AERONET dataset was stratified in different ranges of AOD as suggested in several studies  (Levy et al.
, 2007; Kahn et al. , 2016). The main application of our aerosol classification is to identify aerosol models applicable to AC over inland and coastal waters. Naturally, the AC and subsequent inversions of in-water optical properties are relatively confidently conducted if the aerosol loading were relatively low (i. e. , AOD(0. 44) ≤0. 4). Thus, only aerosol measurements obtained under this atmospheric turbidity were used for the PAM classification, reducing the number of AERONET samples by 87%, leading to a total of 9,352 samples. This data reduction did not compromise the number of computed clusters, nor the general spatial coverage and seasonal variability of aerosol properties summarized in the original dataset (i. e. , with all the AOD values included). Although accurate SSA and AEa estimates can only be achieved for both AOD(0.
44) ≥ 1) represented 15% of the AERONET dataset assembled in this study. The above pre-sorted database (N = 9,352) containing SSA and AE a inversions was split into two batches (i. e. , 75% training and 25% validation data chosen based on odd/even indices  (Omar et al. , 2005) ). Lastly, these data subsets were stratified according to AOD(0. 44) thresholds (i. e. , only records with AOD(0. 44) ≤0. 4 were preserved) before applying the PAM clustering technique (see Section 2. 3 and Section 2. 4).
The annual analysis encompassed the evaluation of two metrics: 1) dominant annual clusters, and 2) minimum Multidimensional Euclidean Distance (MED min ). Comparisons for MAR aerosols were not performed due to the lack of data given the relatively low AOD of marine environments and known difficulties for obtaining accurate SSA and AE a inversions. A dominant cluster and associated aerosol type at the AERONET site j were defined as the most frequent cluster if the minimum number of records was greater than two and no co-dominance was present (i. e. , at least two aerosol types have the same frequency). The MED min metrics were applied to an independent AERONET dataset created in section 2. 2 computed at the monthly scale based on dominant clusters derived from PAM. The MED min calculations were focused on four AERONET sites (Solar Village, XiangHe, Mexico City, and Mongu) where a consensus among studies regarding the annual dominance or co-dominance of aerosol sources (DU, MIX, U/I, and BB, respectively) were found  (Omar et al.
, 2005; Giles et al. , 2012; Russell et al. , 2014; Hamill et al. , 2016)  (Supplementary Appendix Table  A2 ). The MED is equivalent to the norm of the difference between u and v vectors: MED min min u 1,m -v 1,m 2 + u 2,m -v 2,m 2 + u 3,m -v 3,m 2 (3) where u and v represent mean aerosol properties for dominant PAM-derived cluster and independent dataset, respectively, for month m, subscripts 1, 2, and 3 indicate the arithmetic average of SSA(0. 44), AE e, and AE a , respectively. This arithmetic average is multi-annual for PAM-derived classes and monthly for the validation dataset. The aerosol type associated with the PAMderived cluster with the minimum MED was selected for each site and month. The classification performance of PAM for aerosol types was quantified as unity (Number of mismatches/(Number of matches + mismatches).
The A2010 models were incorporated into the model in a similar fashion. To further constrain the number of simulations, only a subset of A2010 models representing fine-mode, weakly-to-non absorbing models (indexed 1 through 7) modulated by a few RH cases (50, 70, and 80%) were applied (we assumed an absence of oceanic aerosols). This choice of A2010 models was determined from Landsat-8/OLI matchups processed via the SeaWiFS Data Analysis System (SeaDAS). The final combination of aerosols for ACI yielded 21 (= 7 × 3) different aerosol models and 48 (=27 + 21) for ACII. The rest of the input parameters, including atmospheric gaseous concentrations (NO 2 and O 3 at 0. 17 and 290 Dobson, respectively), water vapor amount (3 g cm -2 ), wind speed (3 m s -1 ), and surface pressure (1,017 mb), were assumed constant.
An adequate number of matchup datasets (N = 173) was extracted mainly from coastal sites, such as LISCO (N = 7), Lake Erie (N = 8), MVCO (N = 24), Venise (N = 94), and Wave CIS (N = 40) representative of blue and/or green waters. Following a preliminary evaluation of the shapes and magnitudes of these AERONET-OC R rs products, a small subset (N = 215) of an in situ R rs database  (Balasubramanian et al. , 2020; Pahlevan et al. , 2020; Smith et al. , 2021)  resampled with OLI's relative spectral response functions (Supplementary Appendix Figure  A2 ) was identified and incorporated as input to the RT code. It is recalled that the development of these two processors is merely intended for this demonstration, and they are not expected to operate across global aquatic ecosystems. More specific information that corresponds to the development of MDNs is enlisted in Table  2.
Atmospheric Correction The performance of ACI and ACII for each AERONET-OC site is illustrated in Table  6. The performance metrics, and their ratios, suggest up to 30% improvements in estimating R rs (443) and R rs (482), corroborating the added value of PAM models. The differences in the performance of the two processors appear insignificant in the 561 and 655 nm bands, an observation consistent with the expected impact of absorbing aerosols  (Nobileau and Antoine, 2005). The highest performance gain in R rs (443) is reported for the LISCO and Lake Erie sites. The LISCO site (~40 km northeast of New York City) is a region likely influenced by the U/I aerosols. To further illustrate the improved spectral patterns in retrieved R rs , six selected spectra from our matchup dataset are illustrated in Figure  8.
Spatial Comparisons The spatial analysis of PAM-derived clusters revealed that aerosols of a continental origin mainly influence most AERONET sites (Figure  4 ). The marine aerosol type only represents ~4% of the entire database. This finding agrees with  Omar et al. (2005) , who suggested a minor marine contribution (i. e. , 5%) based on nine years of measurements. Also, our results regarding BB contributions (26%) are comparable to those calculated by  Omar et al. (2005) (26. 6%). Despite these similarities, our clustering results suggest that 1. natural sources (i. e. , dust + marine) account for a more significant percentage of data due mainly to the underestimation of "dust" sites in  Omar et al. (2005)  (15%), 2. our U/I aerosol type contribution was 2-fold smaller, and 3. the contribution of the "mixed" aerosol type derived from PAM was more significant.
Unlike our distribution of AERONET sites, the monitoring locations of  Omar et al. (2005)  did not include observations in Australia that were less dense in Asia, explaining the greater importance of "dust-type" particles in our study. The PAM classification generally only agreed with AERONET reference sites annually dominated by dust. Misclassification of DU was mainly due to the co-dominance of aerosol types during the same month. This inconsistency was mainly associated with aerosol classes suggested by  Omar et al. (2005). Also, inconsistencies in discriminating aerosols were attributed to dataset variations (e. g. , sampling periods, aerosol load) at some AERONET sites chosen as reference aerosol sources (see Supplementary Appendix Table  A3 ). In our study, the Ilorin and Beijing sites corresponded to DU and U/I types (Supplementary Appendix Table  A3 ), a result that apparently contradicts  (Eck et al.
Omar et al. (2005)  concluded that aerosols at GSFC are the polluted continental type with a higher contribution during summer as detected here. However,  Omar et al. (2005)  pointed out that GSFC also experiences periods of significant clean continental aerosol (i. e. , rural or MIX) from November to February, a finding not supported by our classification that could be partly explained by differences between datasets. Giles et al. (2011)  showed that aerosols at Kanpur have a complex composition with absorption and spectral characteristics that resemble a MIX aerosol type. Also,  Giles et al. (2011)  detected the predominant influence of dust events during the spring months. The year-round aerosol composition at Banizoumbou was strongly influenced by dust originating from the Saharan desert, as pointed out by  Omar et al.
We nevertheless note that improved treatment of aerosol contribution to is only one key component of reducing the total uncertainty budget. Proper handling of skylight reflection  (Gilerson et al. , 2018)  and characterization of adjacency effects  (Sanders et al. , 2001)  are other major contributors that deserve further research. A lack of knowledge surrounding the vertical distribution of aerosols and uncertainties in ancillary data are among other weak links in AC frameworks  (Frouin et al. , 2019; Mélin et al. , 2022). We recall that future satellite instruments  (Werdell et al. , 2019; Ustin and Middleton, 2021) , which are anticipated to offer measurements within the short blue bands and UV region, will benefit even more from the PAM models once combined with the A2010 models. Adding commercial satellite images to the publicly available datasets  (Vanhellemont and Ruddick, 2018)  will expand research and application opportunities, underscoring the need for more comprehensive aerosol models.
The validation of PAM-derived aerosol types agreed well with those reported at GSFC and Kanpur, two intensely studied AERONET sites where aerosol mixtures are typically influenced by dust and biomass burning smoke. 2. Identifying MIX and U/I types based on PAM sub-types was not always consistent with the sites suggested in the literature and the independent data analyzed here. This apparent disagreement is mainly attributed to our data selection criterion (AOD(0. 44) ≤ 0. 4), the lack of data in previously reported databases, and contrasting aerosol typification reported by several studies for same reference sites (e. g. , Shirahama). 3. Improving aerosol representation over A2010 models is essential in areas where the aerosol composition is predominantly DU, U/I, and BB. 4. A practical assessment of the utility of PAM models in the context of atmospheric correction verified enhanced retrievals in Landsat-8/OLI's blue bands.
ACKNOWLEDGMENTS We are thankful to  Zia Ahmad  for his initial involvement and feedback throughout the course of this study. We are also grateful to the Principal Investigators (PIs) of the AERONET-OC sites, whose datasets aided in evaluating the usefulness of our developed aerosol models for the atmospheric correction of Landsat-8/OLI images. The PIs include  Giuseppe Zibordi ,  Sherwin Ladner ,  Hui Fang ,  Heidi Sosik ,  Tim Moore ,  Steve Ruberg ,  Menghua Wang ,  Alex Gilerson , and  Samir Ahmed.
Comparing fish oil with rape seed oil Last, to shortly demonstrate the possibility to compare ecopoints across different impact categories, fish oil is set in comparison with rape seed oil (EU: 28 Rapeseed oil, raw (economic allocation) dataset from GaBi (Sphera 2021)). Rapeseed oil was chosen as it could roughly be used for similar purposes as fish oil. However, the omega-3 content, on which the functional unit should be based on, is not comparable. One kilogram rape seed oil is therefore not a functional equivalent for 1 kg fish oil. It has to be highlighted that the comparison is mostly done to demonstrate the application of the developed approach for two similar product systems. The result should not be used to make any claims with regard to the comparable environmental performance of these product systems.
The modelled dataset for fish oil is based on an unpublished case study. Thus, detailed inventory data cannot be presented. For the rape seed oil ecopoints of climate change, and land use are determined using the EU ecofactors provided by  Muhl et al. (2019). As ecofactors are not available for marine eutrophication, an own approach was derived, using the CFs for eutrophying substances from  Frischknecht and Büsser Knöpfel (2013) , the distance flow based on  Sala et al. (2015) , and the target flow proposed by  Bjørn and Hauschild (2015)  (see Supporting information Sect. 9). In Fig. 4 , the overall results of comparing fish oil and rape seed oil are shown. It can be seen that the impacts of climate change, marine eutrophication, and land use are comparatively low. Land use impacts are higher for the rape seed oil Fig.
Additionally, the applied data is partly fragmented, because data for many species is not regularly collected. The variability of data is shown in Fig. 5 , where catch data over the time frame from 1980 to 2016 is presented. It can be seen that anchoveta (SEP) has the highest median, because it is the fish species mostly fished in the considered oceans. However, it also has the highest variability (presented by orange bar), because the overall amount caught differed significantly over time. This variability over time is smaller for the other fish species, especially for sprat (NoS) and boar fish (NoS). However, data for boar fish has only been collected for the last 10 years, compared to the other fish species where data is available since the 1980s. The presented variability is not only due to lacking data, but also because the considered system is a living environment, where variability occurs natural.
Participants The study included 39 participants who received a psychoactive psilocybin dose (>12 mg) and completed baseline and 3-month follow-up measures of trait mindfulness, as well as a post-session measure of the mystical-type experience. Of these, seven participants completed two psilocybin interventions with new baseline and follow-up measures, at least 12 months apart (mean months between interventions (SD) [range]: 21  (5) [12-28] ). Thus, in order to increase power for the analyses, a total of 46 datasets were included in the analyses pertaining to hypotheses (1) and (2). For the analyses pertaining to hypothesis (3), 32 of the included participants also completed a pre-drug Positron Emission Tomography (PET) scan with the tracer [ 11 C]-Cimbi-36 for imaging of 5-HT 2A R binding in neocortex and frontolimbic regions  (Ettrup et al.
, 2014 (Ettrup et al. , , 2016)). All participants were recruited from a database of individuals volunteering to participate in human neuroimaging studies of psilocybin. Exclusion criteria included (a) present or previous psychiatric disorder in participant or immediate family, (b) present or previous neurological illness, severe somatic illness, or present medication that could affect the results, (c) non-fluency in Danish, vision or hearing impairment, (d) Present or previous learning disabilities, (e) current pregnancy or breastfeeding for women, (f) contraindications for MR-imaging, (g) alcohol or drug abuse, (h) allergy to the test drugs, (i) significant exposure to radioactivity within the past year, e. g. , due to medical imaging, (j) ECG indicative of cardiac disease or use of medication causing prolonged QT-interval, (k) previous negative side-effects from hallucinogens, (l) use of psychedelics in the past 6 months, (m) blood donation less than 3 months before project participation, (n) hemoglobin <7.
Acknowledgments We extend our sincere gratitude to  Peter Jensen ,  Dorthe Givard ,  Drummond McCulloch ,  Lone Freyr ,  Agnete Dyssegaard ,  Josephine Torp ,  Gerda Thomsen ,  Svitlana Olsen ,  Annette Johansen ,  Catharina Messel , and  Maria Zofia Grzywacz  for help in various aspects of data collection and analysis. We are grateful to the  University of Chemistry and Technology  and the  National Institute of Mental Health  ( Prague, Czechia ) for production of psilocybin and  Glostrup Apotek  ( Glostrup, Denmark ) for encapsulation. We are grateful to  The John  and  Birthe Meyer Foundation  for the donation of the Cyclotron and PET scanner.
27, 28 Interestingly, there is an inverse correlation of mRNA levels of SMARCB1 and PD-L1. 29 These results suggest that additional genetic or epigenetic mechanisms may contribute to immunogenicity of MRTK. The tumor immune microenvironment of MRTK is predominantly composed of CD68 + monocytes and macrophages and CD8 + cytotoxic T cells. Indicative of both pro-and antitumor immune functions, overexpression of genes mediating T-cell function and activation as well as T-cell inhibition are observed. 27, 28 The presence of TILs and expression of PD-1 and PD-L1 have been observed to be associated with potential efficacy of ICIs in MRTK. 30 In a genetically engineered mouse model (GEMM) of MRTK, ICIs resulted in tumor regression and subsequent tumor immunity upon re-challenge. 28 2] [33]  Ongoing studies are investigating nivolumab (anti-PD-1) and ipilimumab (anti-CTLA-4) in pediatric patients with SMARCB1-deficient tumors (NCT: 04416568).
Translocation RCC Of children and adolescents with RCC, the most common (∼42%) is translocation RCC. 46 This is characterized primarily by the transcription factor E3 (TFE3) translocation. PD-L1 expression has been seen in patients with TFE translocation RCC (TFE-RCC). 47 Recent studies have identified CD8 + T cells infiltrating translocation RCC tumors, but they have different mechanisms of apparent T-cell exhaustion as compared to RCCs. 48 Currently, the Children's Oncology Group is assessing whether ICI (nivolumab) in addition to axitinib (a secondgeneration VEGFR tyrosine kinase inhibitor) has activity in TFE-RCC (NCT03595124).
42 To date, only one  patients with high-risk solid tumors, seven of whom had WT. 116 These antigens have documented expression on WT. [117] [118] [119] [120] [121]  In the published results, there were no dose limiting toxicities, and a majority of these patients experienced prolonged disease stabilization, consistent with in vivo efficacy of these agents. 116 The study is currently investigating the addition of lymphodepleting chemotherapy administered prior to TAA-T cell infusion to enhance T-cell persistence and function in vivo (NCT02789228), focusing on patients with WT given encouraging preliminary results.
Current therapeutic targets and active or completed clinical trials Target Therapeutic Diagnosis Clinical experience B7-H3 MGA271 (B7-H3 monoclonal Refractory solid tumors Disease stabilization and partial response; antibody) favorable safety profile 92 MGA271 (B7-H3 monoclonal Pediatric relapsed/refractory solid tumors Completed, awaiting results antibody) (NCT02982941) B7-H3 CAR-T Pediatric relapsed/refractory solid tumors Ongoing (NCT04483778) B7-H3 CAR-T Pediatric relapsed/refractory solid tumors Ongoing (NCT04897321) MGC018 (B7-H3 monoclonal Refractory solid tumors Ongoing (NCT05293496) antibody) COX-2 Celecoxib (COX-2 inhibitor) Pediatric relapsed/refractory solid tumors Ongoing (NCT02574728) Celecoxib (COX-2 inhibitor) Stage IV renal cell carcinoma PR (n = 3) and SD (n = 5) of 17 patients treated in combination with interferon alfa-2b (NCT01158534) GPC3 GPC3 peptide vaccine Pediatric relapsed/refractory solid tumors 66.
6% Response rate (CR + PR + SD) 40 GPC3 CAR-T Pediatric relapsed/refractory solid tumors Ongoing (NCT04377932) GPC3 CAR-T Pediatric relapsed/refractory solid tumors Ongoing (NCT04715191) Codrituzumab (GPC3 monoclonal Pediatric relapsed/refractory solid tumors Ongoing (NCT04928677) antibody) TAA Autologous TAA-T (WT1, Pediatric relapsed/refractory solid tumors 73% Response rate (CR + PR + SD) 116 preferentially expressed antigen of melanoma [PRAME], survivin specific) Autologous TAA-T (NY-ESO-1, Pediatric relapsed/refractory solid tumors Completed, awaiting results PRAME, survivin, MAGEA4, (NCT02239861) SSX specific) Third party TAA-T (WT1, PRAME, Pediatric relapsed/refractory solid tumors Ongoing (NCT05238792) survivin specific) PD-L1 Pembrolizumab Pediatric relapsed/refractory solid tumors Ongoing; objective response 5.
9% in patients with solid tumors/lymphoma preliminarily (0 of 3 with WT, 1 of 2 with MRTK) 132 Atezolizumab Relapsed/refractory solid tumors PFS: 1. 2 months in WT cohort (NCT02541604) PD-1/CTLA-4 Nivolumab/Ipilimumab Relapsed/refractory INI-negative tumors Ongoing (NCT04416568) PD-1 Nivolumab (with axitinib) Metastatic/unresectable TFE translocation Ongoing (NCT03595124; AREN1721) RCC (TFE-RCC) TNFa Recombinant TNFa Pediatric relapsed/refractory Wilms tumors 42. 1% Response rate (CR + PR + SD) TA B L E 1.