dataesr/scientific-paragraphs-categorization huggingface dataset:
Calibration, Reference Data, and Interoperability An important additional factor in Landsat-8 OLI product geometric accuracy is the use of ground control points during product generation to ensure multi-temporal registration for products throughout the Landsat archive. These control points were originally derived from the Global Land Survey (GLS) data set composed of Landsat 7 data circa 2000  [10]. The use of ground control for registration can be thought of as a type of per-product calibration wherein the ground control points serve as the calibration standard. The geometric accuracy of Landsat-8 products is thus dependent upon the accuracy of the control point standard as much as or more than the inherent accuracy of the calibrated Landsat-8 OLI system. The accuracy of the GLS framework is estimated to be 25.
Pseudo Invariant Calibration Sites (PICS) Absolute Calibration Based on Hyperspectral Sensor Models Two different models are being used to predict the TOA reflectance of the PICS regions using hyperspectral satellite data as the source of the "field" data. The models consider the atmospheric conditions, BRDF, and solar and viewing geometries in converting hyperspectral TOA reflectances to multispectral TOA reflectances (either OLI or MSI bandpasses). The South Dakota State University Absolute PICS (APICS) model uses Hyperion data  [23] , and the ESA Database for Imaging Multispectral Instruments and Tools for Radiometric Intercomparison (DIMITRI)-PICS model uses Medium Resolution Imaging Spectrometer (MERIS) data  [16]. Results using these models are shown in Figure  9  and indicate that the two instruments are calibrated to within 2% of each other, except for possibly the CA band.
Pseudo Invariant Calibration Sites (PICS) Absolute Calibration Based on Hyperspectral Sensor Models Two different models are being used to predict the TOA reflectance of the PICS regions using hyperspectral satellite data as the source of the "field" data. The models consider the atmospheric conditions, BRDF, and solar and viewing geometries in converting hyperspectral TOA reflectances to multispectral TOA reflectances (either OLI or MSI bandpasses). The South Dakota State University Absolute PICS (APICS) model uses Hyperion data  [23] , and the ESA Database for Imaging Multi-spectral Instruments and Tools for Radiometric Intercomparison (DIMITRI)-PICS model uses Medium Resolution Imaging Spectrometer (MERIS) data  [16]. Results using these models are shown in Figure  9  and indicate that the two instruments are calibrated to within 2% of each other, except for possibly the CA band.
Aquatic Applications: Potentials and Limitations Within the next decade, the Landsat-8 and Sentinel-2 virtual constellation will be the primary source of well-monitored Earth-observing datasets for monitoring water resources, including lakes, reservoirs, rivers, bays, and other nearshore coastal areas. Because of the improvements in the sensor technology, the image data from this constellation was anticipated to outperform heritage missions like Landsat-5 and -7  [53] [54] [55]  or the SPOT. The primary advantages come from (a) the improved SNR and (b) the additional spectral bands at ~443 nm and within the NIR region. The high data quality together with 2. 9-day revisit time of the constellation revolutionizes the way satellite data are utilized for science algorithm development and/or monitoring applications in inland and nearshore coastal waters.
Currently, preliminary vicarious calibrations  [61]  conducted for a handful of OLI  [62]  and MSI images  [63]  using in situ data autonomously measured at the Marine Optical Buoy (MOBY) and the Buoy for the Long Term Acquisition of Time Series (BOUSSOLE) site have yielded reasonable relative consistencies in TOA, , and TSS products. Analyzing the near-simultaneous overpasses after vicarious calibration indicated that the corresponding products agree, on average, within 0. 5% in TOA and 8% in using in situ measurements at the MOBY and the BOUSSOLE site with the red channel showing the largest differences. This largest discrepancy in the red channel is attributed to imperfect spectral-band adjustments in the red spectral bands. Figure  15  illustrates the Relative Spectral Responses (RSRs) (only within 600-700 nm) overlaid onto hyperspectral in situ.
This goes beyond existing quality assurance (QA) flags and allows for providing a confidence level to each pixel enabling effective decision-making. Such uncertainty products for Landsat-8 and Sentinel-2 are only possible when calibration uncertainties or instrument artifacts are well known. It is, thus, crucial to have accurate knowledge of radiometric performance of this constellation throughout its lifetime. Currently, preliminary vicarious calibrations  [61]  conducted for a handful of OLI  [62]  and MSI images  [63]  using in situ data autonomously measured at the Marine Optical Buoy (MOBY) and the Buoy for the Long Term Acquisition of Time Series (BOUSSOLE) site have yielded reasonable relative consistencies in TOA, R rs , and TSS products. Analyzing the near-simultaneous overpasses after vicarious calibration indicated that the corresponding products agree, on average, within 0.
Data Interoperability for the International Community The international community, through the Committee on Earth Observation Satellites (CEOS), is expressing an expectation of data interoperability as a general principle, beyond specific application areas such as those discussed above. Interoperability is seen as a key requirement to deliver benefit from operational remote-sensing satellite systems such as Landsat and Sentinel 2, and as a logical 'next step' to build on the success of free and open data  [65]. CEOS (www. ceos. org) seeks to optimize the benefits of space-based Earth observation through high level cooperation in areas such as mission planning and provision of compatible data products and policies. As applications become more operational, data suppliers will need to ensure:.
• Continuity so that down-stream products can be developed with confidence that the data streams will continue, with smooth transitions as old instruments are retired and replaced with newer versions; • Interoperability between data streams to allow reliable and high-quality products, as demonstrated in the NASA HLS Project; and • That data are fit for use by specialists in agriculture, security, civil engineering, disaster management and so on, who may not be remote sensing scientists. These users will need data that are 'ready to use'. CEOS is therefore establishing a framework for analysis-ready data for land applications, defined as: CEOS Analysis Ready Data for Land (CARD4L) are satellite data that have been processed to a.
• Continuity so that down-stream products can be developed with confidence that the data streams will continue, with smooth transitions as old instruments are retired and replaced with newer versions; • Interoperability between data streams to allow reliable and high-quality products, as demonstrated in the NASA HLS Project; and • That data are fit for use by specialists in agriculture, security, civil engineering, disaster management and so on, who may not be remote sensing scientists. These users will need data that are 'ready to use'. CEOS is therefore establishing a framework for analysis-ready data for land applications, defined as: CEOS Analysis Ready Data for Land (CARD4L) are satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort, and, interoperability both through time and with other datasets.
CEOS analysis ready data places an expectation that satellite data will not only be calibrated at sensor level, but will also be processed to provide a quantitative land surface measurement. At time of writing, land surface reflectance and land surface temperature have been identified by the CEOS community as analysis ready data products for optical land observing satellites. Although measurements taken with differing instruments (e. g. , OLI, MSI) will not be identical, they will be fundamentally comparable and, therefore, a critical step toward enduring interoperability. Several surface reflectance products are already being produced. Australia [66], Canada, China, the European Space Agency  [67] , France, the United Kingdom and the USA  [68]  are actively developing or have developed surface reflectance products from Landsat and Sentinel-2 platforms.
In the private sector, Planet released a surface reflectance specification in 2017  [69]. In addition, Kirches et al. [70]  indicated the development of surface reflectance products for land cover climate change, analysis ready data has been developed in Switzerland  [71]  and Masek's efforts were summarized previously. However, these efforts lack coordination and technical consistency; for example, in approaches to corrections for the atmosphere, the bidirectional reflectance distribution function and terrain. CEOS is developing a framework for analysis ready data that can guide the efforts of individual agencies toward a consistent overall approach. To meet the rising expectations of the international community for interoperable data, including preparing data for exploitation in new architectures such as data-cubes  [72]  calibrated data from Landsat and Sentinel 2 will need to be further processed to produce land surface measurements of surface reflectance, validated against in situ measurements.
Improve Communications As is often the case with any human endeavor, communications can be a limiting factor. This was felt to be the case with calibration and data interoperability at several levels. First, better communications were suggested between agencies responsible for the satellite missions. One simple suggestion would be to promote attendance at each agency's calibration meetings, either in person or via distance methods. However, more broadly, better communications were suggested with the remote-sensing community as a whole. In addition, it was noted that the information communicated needed to be both timely and accurate-despite the fact that these two characteristics can often be at odds with one another. Additional elements include information that is both strategic as well as authoritative.
Adopt Collection-Based Processing A collection, in the context of this discussion, relates to all products downstream of the rawest form of the main input data (telemetry), produced sequentially by a given entity. Collection-based processing means that the entire data collection from an instrument is processed whenever an upgrade is necessary, and not just a part of it. Minor changes to a collection can be referred to as updates, while reprocessing of the entire collection can be considered upgrades. This approach has been used with MODIS data since its inception, has recently been adopted by USGS EROS for Landsat data, and is under consideration for Sentinel 2 as the system matures. This approach also implies that reprocessing of data is done on an infrequent basis and only when necessary to ensure data quality.
The significant benefit to users is that they can always have access to a complete data set from any instrument and be assured of incorporation of the latest enhancements into the entire data set. For the specific sensors being considered, the panel recommended that Sentinel 2A/B join Landsat and adopt collection-based processing. However, this approach can also be recommended for sensor systems in general. One last recommendation on this topic is that, if possible, Landsat and Sentinel 2 should coordinate their collection upgrades so they occur at the same time.
Standardized incidence analysis Considering that young people are the most affected by malaria in endemic areas and that the age-structure of the population varies across the country, the standardized incidence was calculated by taking into account age structure. The indirect Standardized Incidence Ratio (SIR) method was used which is based on the population size and the distribution in each age class per district  [26] [27] [28]. For this analysis, both malaria and population data were grouped into four overlapping age classes: 0-4, 5-14, 15-25 years old, and above 25 years old. Stratification-level incidence was calculated for each district i and each age class j using: p(j) = i D i j / i N i j where D i (j) and N i (j) are the number of malaria cases and population size for district i for age class j, respectively.
In particular, the biased reporting nature and ability for the HMIS to collect high quality data is a common issue for routinely collected data, particularly those from low income countries  [43]. This study focuses on the data from 2010 to 2014 to help mitigated some of these effects. Before 2008, RDTs were not available in the country and diagnosis was done using microscopy, which was not available at all facilities. Instead, the study focused on data post the arrival of RDTs. These data are likely more reliable since the quality of malaria diagnosis was improved. However, temporal and spatial differences in diagnostics still exist and as a result the study focused on comparing incidence within years and within strata as opposed to between years or strata to help reduce some of the biases within these different units.
Nucleotide sequence accession number The  16S  RNA gene sequences from Gorillibacterium timonense strain SN4 T and Vitreoscilla massiliensis SN6 T were deposited in GenBank under accession numbers  LN870297  and  LN870312 , respectively. Deposit in a culture collection Gorillibacterium timonense strain SN4 T and Vitreoscilla massiliensis strain SN6 T were deposited in the  Collection de Souches de l'Unité des Rickettsies (CSUR ,  WDCM 875 ) under numbers  P2011  and  P2036 , respectively.
Acknowledgements. We are thankful to  Yasmin Ochoa ,  Bill Gomez , and  Cindy Wilber  for their help in the greenhouse and slug collection as well as  Ray Von Itter  and  Dave Wilson  for aiding in logistics and maintenance in the greenhouse facility. Thanks as well to  Stanford University 's  Undergraduate Advising and Research and the Sherwood Fund  for the Major Grant that supported IN. Additional funds to support this research were also provided by  Stanford University's School of Humanities and Sciences through the Dirzo Lab. We would also like to thank the two anonymous reviewers for providing constructive comments. Edited by: Daniel Montesinos Reviewed by: two anonymous referees.
1. 0) bdgdiff command with the default settings we identified 16,409 peaks that were either lost or gained in hlx1MO, when compared to wild-type siblings (Fig. 2c  and Supplementary Data 2). Using Genomic Regions Enrichment of Annotations Tool (GREAT) analysis, we found that the majority of peaks were located between 5 and 500 kB of the transcription start site (TSS) and regulate a variety of processes (Supplementary Fig. 3a  and Supplementary Data 2). We used a nominal cut-off of 25 kb from the TSS and assigned the differential peaks to 6431 genes (Supplementary Data 2). IPA analysis of these genes revealed that mitochondrial dysfunction (rtFET, P < 3. 47E-09) and OXPHOS (rtFET, P < 8. 13E-08) were among the upper enriched categories (Supplementary Data 2). To gain mechanistic insights we performed footprinting analysis  31  in control and hlx1MO datasets and obtained 54,588 and 50,471 footprints, respectively.
Motif discovery revealed HOX motifs enriched in control-only footprints and AP-1 motifs in hlx1MO-only fooptrints (Supplementary Fig. 3b ). To determine whether these motifs were significantly differentially footprinted between the two datasets, we computed motif self-enrichments and cooccurrence enrichments from specific footprint populations over background occurrences computed from reciprocal datasets. This analysis revealed loss of HOX motifs and gain of AP-1 motifs in hlx1MO (co-occurrence enrichment computation z = 3. 583 and z = 13. 241, respectively) (Supplementary Fig. 3c ). Tn5 insertion profiles at these sites revealed diverging profiles at footprinted Hoxc9 and AP1 motifs (Student's t-test, Hoxc9 control specific P = 2. 9884E-34, AP-1 hlx1MO specific P = 1. 24262E-37) (Fig. 2d ).
Analysis of relative footprint occurrences to their cognate datasets also revealed that Hoxc9 and AP-1 motifs were more present in control and hlx1MO-specific footprints, respectively (Supplementary Fig. 3d ). After integration of our RNA-Seq and ATAC-Seq data from the hlx1MO, we identified 739 (35%, hg. t. , P < 1. 930E-62) deregulated genes with changes in chromatin accessibility (Fig. 2e  and Supplementary Data 2). Comparison of the ATAC-Seq from hlx1MO and RNA-Seq from fli:hHLXOE embryos demonstrated that 1963 (31%, hg. t. , P < 3. 082e-116) deregulated genes exhibited differences in chromatin accessibility (Fig. 2e , Supplementary Fig. 3e , and Supplementary Data 2). Interestingly, ETC genes that were downregulated upon hHLX overexpression also showed changes in chromatin accessibility in two different types of ATAC-Seq analyses (whole region or sub-nucleosomal) (Fig.
2f  and Supplementary Data 2). Concomitantly, some ETC genes showed upregulation of expression in hlx1MO (Fig. 2f ). Finally, ppardb (peroxisome-proliferator activated receptor δ) also showed changes in chromatin accessibility in subnucleosomal analysis (Fig. 2e  and Supplementary Data 2). Recently PPARδ, a regulator of metabolic functions, was shown to be critical for LT-HSC stemness  32. We therefore performed qPCR for ppar genes in hxl1MO, fli:hHLXOE, and Runx: hHLXOE embryos. Indeed, the expression of the ppar receptors was significantly decreased in hxl1MO (Fig. 2g ), but only pparda and ppardb were increased in fli:hHLXOE or Runx:hHLXOE embryos (Fig. 2h, i ). Together, these data demonstrate that Hlx1 regulates the expression of ETC and ppar genes in zebrafish endothelial cells and HSPCs.
3b ). In contrast, TMRM was higher in endothelial cells of hlx1MO, yet the total mitochondrial Fig. 1  hlx1 regulates hematopoietic stem cell formation and myeloid cell maturation in zebrafish. a-b Whole-mount in situ hybridization (WISH) for runx1 (a) and rag1 (b) in control or fli:hHLXOE zebrafish embryos at 36 or 96 hpf, respectively. Arrows indicate HSPCs. Numbers in the bottom right corner of panels indicate the number of zebrafish embryos with the indicated phenotype compared to the total number of zebrafish analyzed. Quantification of WISH was performed using FIJI software and statistical significance of three independent experiments in 12 zebrafish embryos was evaluated by Student's t-test, *P < 0. 05, **P < 0. 01 (mean + s. d. ). c WISH for the early myeloid marker pu. 1 in control or fli:hHLXOE zebrafish embryos at 48 hpf.
From the database BloodSpot  35  and previously published data  36, 37  , we confirmed that HLX is expressed in murine and human hematopoietic progenitor cells, but also in mature myeloid lineages (Supplementary Fig. 4a-c ). Moreover, HLX is upregulated in AML patient samples, as it has been previously published  21  and according to information from cBioportal (Supplementary Fig. 4d ). We next investigated whether AML patients show similar transcriptional signatures to those of hHLXOE zebrafish embryos. We obtained data generated by the TCGA Research Network (http://cancergenome. nih. gov/) from 193 AML patients. Normalized gene expression data of 156 AML samples were used for further analysis. We performed correlation analysis among all genes with significant expression (RSEM values >50) across all patients and the HLX gene.
5e ). Binding motifs for multiple transcription factors were identified in both K562 and HL60 cells (Fig. 4e  and Supplementary Data 4). Importantly, independent motif analysis uncovered motifs for homeobox containing factors such as HMBOX1 (Fig. 4e  and Supplementary Data 4). To unravel the chromatin landscape around the HLXbound genomic regions, we analyzed available K562 chromatin data from the ENCODE database  38. HLX-bound genomic regions were located mostly on open chromatin and active enhancers, as indicated by co-localization with active histone marks (Fig. 4f ). Next, we asked whether HLX binding is associated with changes in gene expression in human hematopoietic cells. To this end, we knocked out HLX in K562 cells using CRISPR-Cas9 technology and performed RNA-Seq analysis (Supplementary Fig.
5d ). Additionally, the protein levels of LC3-II, an autophagosome marker, are markedly increased in these cells upon chloroquine treatment (Fig. 5g ). Together, these results suggest that HLX overexpression in AML cells affects mitochondrial metabolism and fatty acid synthesis possibly through the upregulation of PPARδ gene expression. Additionally, HLX overexpression, possibly through downregulation of ETC genes, results in AMPK activation and autophagy. HLX regulates the metabolic state of CD34 + human cells. Our findings could have important implications for patients with hematopoietic disorders, including AML. We therefore performed colony-forming unit (CFU) assays on human CD34 + HSPCs to measure the effects of HLX modulation in normal hematopoiesis. Consistent with our zebrafish results and with published data on mouse HSPCs  21  , HLX knockdown (sh-HLX) caused a significant reduction in the number of hematopoietic colonies, whereas HLX overexpression (CD34 + HLX) caused the opposite phenotype and large myeloid colonies (Fig.
). e Representative motifs identified in HLX-bound genomic regions in K562 cells (P-values binomial test, see also Supplementary Data 4). HOX motif is shown in the frame. f Heatmap of ChIP-Seq signals, comparing HLX-bound regions in K562 cells to publicly available CHIP-Seq data for histone marks in K562 cells from the ENCODE database. CTCF was used as negative control. g Gene expression in control samples plotted vs. gene expression in HLX KO K562 cells. Differentially expressed genes (>2-fold, NBT, P < 0. 05) are colored in red and blue for upregulated and downregulated genes, respectively (also see Supplementary Data 5). h Average expression levels of selected genes from RNA-Seq of K562 HLX KO cell line (Student's t-test, *P < 0. 05, ***P < 0. 001) Thus, we show here that AMPK inhibition decreases the viability of AML cell lines.
Detection of autophagic flux. Autophagy was measured using the Cyto-ID ® Autophagy detection kit (Enzo, ref. ENZ-51031), according to the manufacturer's instructions. Briefly, cells were grown at a density of 1 × 10 6 cells/mL for 16 h at 37 °C, in the presence of DMSO or Chloroquine (60 µM). Cells were washed with PBS, pelleted at 1000 rpm for 5 min, and incubated with cyto-ID green reagent for 30 min at 37 °C in the dark prior to flow cytometry analysis on LSRFortessa cell analyzer (BD Biosciences). Mean fluorescence intensity (MFI) was quantified using FlowJo software (Tree Star, Inc. ). Real-time qPCR. Total RNA was extracted from human or zebrafish cells using the RNA Clean & Concentrator-5 kit (Zymo Reasearch) kit or TRI Reagent, according to the manufacturer's instructions. cDNA was prepared with the SuperScript™ First-Strand Synthesis System for RT-PCR kit (Thermo Fisher Scientific).
I34361; 50 nM) at 28 or 37 °C, respectively, for 30 min, then washed with PBS-5% FBS and analyzed by flow cytometry with LSRFortessa or LSR II cell analyzers (BD Biosciences). MFI was quantified using FlowJo software (Tree Star, Inc. ). To measure the ratio of mitochondrial and nuclear DNA (mtDNA/nDNA), genomic DNA was extracted from 10 5 cells using DNeasy Blood & Tissue Kit (Qiagen) and real-time qPCR was performed as described before  70   Genome-wide analysis. RNA-Seq libraries, RNA-Seq preparation and analysis methodology, ATAC-Seq libraries, ATAC-Seq analysis methodology, Chromatin Immunoprecipitation, ChIP-Seq libraries, ChIP-Seq analysis, gene ontology, pathway and network analysis, motif analysis, digital genomic footprinting for ATAC-Seq, network construction, hierarchical differentiation tree can be found in the Supplementary Information.