dataesr/scientific-paragraphs-categorization huggingface dataset:
| Universal plant blocking oligos enable profiling of microbiota in many host species A. thaliana blocking oligos are not effective against every plant host, so users would need to design and test new oligos for their purposes  (Giangacomo et al. , 2020a). Thus, we expanded to multiple hosts by designing a set of oligos to block amplification of chloroplast 16S rRNA gene and plant 18S rRNA gene using a highly diverse set of plant species (Table  S2 ). To identify regions where blocking oligos would be most effective across plant species, we used our R package AmpStop (see methods and the AmpStop section below). Candidates were first tested for specificity to plants by amplifying DNA from 21 plant species (Table  S3 ) and mixed bacterial DNA and then visualizing bands on a gel. We selected primer sets that amplified most plants but avoided amplification of bacteria or fungi (For 16S rRNA gene see Figure  S1 ).
The fungal and oomycete ITS data sets complemented the broader 18S rRNA gene data with more specificity in those groups -together, these two accounted for 44% of observed eukaryotic genera (Figure  6a ). Prokaryote data sets further demonstrate complementarity for primer sets targeting the same groups of microbes (Figure  6b ). Here, 42% of observed genera were discovered by both primer sets, with complementary diversity discovery especially in the phyla Cyanobacteria (V3-V4 data set) and Firmicutes (V5-V7 data set). For all groups, both tested primer sets tended to recover similar diversity at high taxonomic levels with differences at the genus level. A full list of which primer sets recovered which taxa are provided in Supporting Information File S2. Thus, blocking of overabundant host and microbial amplicons allows deep diversity characterization in leaves using multiple loci.
| AmpStop: An R package for quick design of blocking oligos for any nontarget organism A key advantage of using standard oligomers as a tool to block amplification is that many design options can be tested rapidly and at low cost using standard PCR techniques. A limit on rapid implementation in laboratories could be the design step, where some computational know-how is required. To reduce this burden, we created AmpStop, an R package to automate design of blocking oligos. AmpStop can be used by anyone with R and BLAST+installed on their computer. blocking oligo pairs. Since the design of peptide nucleic acid clamps follows practically identical steps  (Lundberg et al. , 2013) , the package can also be used to design them. AmpStop and detailed instructions on its use and interpretation of results is freely available on GitHub (https://github.
| DISCUSS ION Amplicon sequencing of phylogenetically or functionally informative loci has become an indispensable technique in a variety of biologyrelated fields because its targeted approach (compared to untargeted approaches like metagenomics) enables in-depth diversity characterization with accurate annotation using specialized databases  (Quast et al. , 2013). It has revealed that microbial community structuring is more complex than previously thought and suggested extensive interactions between (a) biotic factors and microbes (de  Menezes et al. , 2015)  and between microbes even across kingdoms  (Agler et al. , 2016; Lima-Mendez et al. , 2015). As we have shown here, recent advances have made sequencing up to eight loci in parallel possible, drastically increasing throughput and diversity resolution.
, 2020a). The R package AmpStop, which we make available here should ease the design process. AmpStop can also be used by researchers who do choose PNAs, as blocking oligos design essentially follows the same procedure 13. The availability of universal plant blocking oligos that block amplification of most host species will further reduce the need to make new designs. Notably, we were not able to design universal blocking oligos for the ITS region because diversity between different plant species made it impossible to find a universal blocking oligo set. On the other hand, we and others have observed that the host ITS is not efficiently amplified when there is significant target microbial DNA  (Ihrmark et al. , 2012). Thus, universal ITS blocking oligos are not as urgently needed as universal 16S rRNA gene blocking oligos.
Studies of leaves of the wild plant A. thaliana found that bacterial fraction of extracted DNA are typically very low but range up to about 25%  (Regalado et al. , 2020). We found that more blocking cycles (15 F I G U R E 7 Example output from the R package AmpStop when given as input the A. thaliana ITS1 sequence (nontarget) and the UNITE fungal ITS database (target). The x-axis represents all possible 30-base oligomers (candidate blocking oligos) along the length of the nontarget sequence. The sequences of the oligos are produced in a separate file. The y-axes show three complementary measures of how likely each candidate oligo is to amplify target sequences, where a hit represents an alignment of the oligo a target. The best candidates will minimize hits and thus be highly specific to nontargets.
(a) The total number of hits of each candidate to the target database. Those least likely to amplify targets will have few hits. However, not all hits are equally problematic. Thus, (b) shows oligos that hit a sequence in the target database along >90% of its length, which would increase the chance of amplifying a target organism and (c) shows hits aligned at the 3' end, which are especially problematic because this is the site of polymerase binding. The blue and red guidelines represent one-and two-thirds of the length of the nontarget, respectively, and forward and reverse blocking oligos are probably best chosen before and after these lines, respectively vs. 10) were necessary to efficiently block nontarget amplification in real leaf samples, but not in mock communities with 5% bacterial DNA.
Thus, we advise using fraction of bacterial reads in 16S rRNA gene data as an initial approximation to gain insight in many samples and then to design specific experiments using more precise measures. com/proje cts/Obtai ning_deeper_insig hts_into_micro biome_diver sity_using_a_simple_method_to_block_host_and_non-targe ts_ in_ampli con_seque ncing_/89504  (Mayer & Agler, 2020). Raw sequencing data has been made publicly available as a NCBI projects  (Agler et al. , 2017 ; PRJNA420016 [https://www. ncbi. nlm. nih. gov/ biopr oject/ PRJNA 420016] and  Mayer et al. , 2020, PRJNA663775  [https://www. ncbi. nlm. nih. gov/biopr oject/ PR JNA 66377 5/]). AmpStop is freely available on GitHub (https://github. com/magle r1/AmpStop).
p-values are calculated with a one-sided paired t test It requires as input only the amplified nontarget region (for example the host ITS1 sequence) and a target sequence database that is BLAST-formatted. Three functions enable users within minutes to generate a list of all possible blocking oligos, a figure showing how many times each oligo "hits" target templates and other useful metrics of specificity (Figure 7a-7c) and a list of the most promising.
The technological and the digital innovations in real estate known as PropTech  [51]  would offer solutions not only to the common challenges among real estate stakeholders-such as cost overruns, lack of transparency and fragmentation of the real estate industry-but also to address critical sustainability challenges such as the integration of life cycle analysis (LCA)  [52]  or biodiversity features. Live environmental data are more and more freely available in form of building information modelling (BIM) data and geographic information system (GIS) datasets. This would facilitate the implementation of biodiversity components into the early phases of a project, but this option is not yet empowered  [53]. Instead, BIM and latest developments in GIS have proved to be useful merely in typical design and construction use cases, generally categorised in the following eight groups  [54] : time-saving, material-saving, cost-saving, improvement in health and safety, reduction in risk, improved asset utilisation and improvement in asset quality for the end-user.
The pilot version of CASBEE-city seems to be the first case of partial integration of the City Biodiversity Index (CBI) also known as the Singapore Index on cities' biodiversity  [130]. The CBI was endorsed by the convention of biological diversity in 2009  [131, 132]  and is based on three pillars: (1) native biodiversity, 2) ecosystem services (e. g. , cooling effect of the vegetation), and (3) governance and management of biodiversity (e. g. budget devoted for biodiversity conservation initiatives). Probably, one of the reasons why the CBI index was not adopted more extensively might be the difficulty to find adequate data (open access data availability) or the high effort to gather high-resolution spatial data. Nevertheless, some of the issues can be overcome by analysing land use datasets  [133, 134]  like the free available global land use datasets  [135].
The benefits of GeoBIM analysis of the abiotic conditions are already acknowledged by real estate developers. Moreover, ecological modelling embedded in GeoBIM would also facilitate the interoperability between tools and, more importantly, improve the ability of the other GeoBIM users such as planners or designers to integrate the modelling predictions to their conventional designing process  [155, 156]. For example, to analyse the project impact on the surroundings, to better understand where and when the building will overheat, to plan and estimate the total amount of solar energy, etc. However, a gap has been identified in integrating the biotic environmental data to be used in a design project.
Digital Twins for Smart Cities-the Potential for Integrating Biodiversity in a Geodesign Perspective The evolution of environmental design and planning is geodesign  [157]. According to Ervin  [158] : "Geodesign enhances traditional Environmental planning and Design activities with the power of modern computing, communications and collaboration technologies, providing ondemand simulations and impact analyses to provide more effective and more responsible integration of scientific knowledge and societal values into the design of alternative futures. [Moreover] The technical infrastructure for geodesign has been achieved by using the tools of existing GIS, CAD and BIM systems, coupled with spreadsheets, databases and emergent web techniques. This ad-hoc approach has not been altogether satisfactory, as there are still many interoperability issues to resolve; and, perhaps more critical, there is still considerable debate about whether and how design is supported and enabled (or thwarted) by these existing tools".
The synergy among multiple disciplines is possible thanks to computing technologies enabling designers to cope-at early-stage phases-with high levels of complexity, for example, enabling them to run sophisticated LCA or integrate interactive and generative processes employing evolutionary design tools  [171]. The first software enabling to integrate LCA targets in the upstream phase using a generative approach is the VizCab© software, developed at the Polytechnic University of Lausanne (EPFL)  [170, 172]. By integrating BIM with LCC and LCA the application of these types of sustainability analysis in the early stages of a project becomes possible  [52, 173, 174]. In the context of performance-oriented design, VizCab offers a LCA-based data-driven design method based on the "combination of LCA, parametric analysis, data visualisation, sensitivity analysis, and target cascading techniques"  [175] : to make the most efficient and sustainable choice, the user first sets the overall hierarchical targets and constrains (e.
g. , that the 2000-watt society vision); then, the software assesses each sub-target (target-cascading technique), followed by the visualisation of multicriteria graphics (parallel coordinates data visualisation), which allows the user to check the impact that certain parametric choices have on the environmental targets (parametric analysis). This process is replicated several thousand times in feedback loops to feed the design alternative database (also knowledge database) which is eventually queried on a sensitive analysis base; this kind of analysis explores only among the most sensitive and relevant solutions  [175, 176]. The strength of the LCA method is that it is site specific and provides guidance at the beginning of the design process (with few details); nevertheless, the software is still in a prototyping stage and the visualisation of the knowledge database is not very user-friendly  [176].
Attempts to solve the latter issue were made using Rhinoceros© and Grasshopper© graphical algorithm editor, whilst emissions and other physical analysis were simulated with other Rhinoceros plug-ins, i. e. , Ladybug (analysis of climate data) and Honeybee (daylight simulations) (Fig. 8 )  [177] [178] [179]. The centrality of computing engine interface and architects is instead one of the priorities of the generative eco-design tool EcoGen©, which combines the computational generative process with architect's cognitive one  [180, 181]. This software resulted as the output of the Generative Eco-Design project (Eco-Conception Générative) (2011-2012, funded by the French National Research Agency (Agence Nationale de la Recherche, ANR)) initially involving the National School of Architecture of Lyon (MAP-ARIA) and Nancy (MAP-CRAI) and the University of Nancy 2 (Codisant-Sitcom, Interpsy)  [181].
The software is under constant upgrade  [171] , and it is thought to support the designers in the sketch phase by proposing several set-into-context solutions, as in the VizCab software, computed on the base of efficiency analysis. The software engine optimises bioclimatic, ecological and economic criteria to the local context: urban (plot of land and surrounding blocks), environmental (climate-solar model) and programmatic (urban planning rules, e. g. , buildable area and distance from roads). The computation is based on population evolution which uses one morphological engine and a genetic one in a systemic loop (morphogenesis, evaluation and optimization), thus generating random families of solutions (Fig. 9 ). In the assisted interactive mode, the software let the designers/users select the most suitable solution in different steps on the base of a subjective choice (e.
Each deployment (1-4) started and ended in Palmdale (California, USA) and generally consisted of a loop southward from the Arctic through the central Pacific, across the Southern Ocean to South America, northward through the Atlantic, and across Greenland and the Arctic Ocean. During ATom-3 and -4, two additional flights from Punta Arenas (Chile) sampled the Antarctic troposphere and upper troposphere/lower stratosphere (UT/LS) to 80 • S. In this work, we focus on the measurements taken during January-February 2017 (ATom-2), September-October 2017 (ATom-3), and April-May 2018 (ATom-4) (no quantum cascade laser spectrometer (QCLS) N 2 O data are available for ATom-1 in August 2016). The motivation for this paper is twofold. Firstly, we present a new retrieval strategy to account for the pressure and temperature dependence of laserbased instruments, and specifically for the use of quantum cascade laser spectrometers on aircraft.
All these components are mounted on a temperaturestabilized, vibrationally isolated optical bench. The temperature in QCLS is controlled by Peltier elements coupled with a closed-circuit recirculating fluid kept at 288. 0 ± 0. 1 K. QCLS measures CH 4 and N 2 O by scanning the spectral interval of 1275. 45 ± 0. 15 cm -1. A second laser is used to scan CO at 2169. 15 ± 0. 15 cm -1. The supply currents to QCLS are ramped at a rate of 3. 8 kHz to scan the laser frequency for 200 channels (steps in frequency) in laser 1 and 50 channels in laser 2; an extra 10 channels are used to measure the laser shut off (zero-light level). The spectra and fit residuals for CH 4 , N 2 O, and CO are shown in Fig. S1  of the Supplement. Mixing ratios are derived at a rate of 1 Hz by a leastsquares spectral fit assuming a Voigt line profile at the pressure and temperature measured inside the sample cell and using molecular line parameters from the HIgh-resolution TRANsmission molecular absorption database (HITRAN,  Rothman et al.
A data logger (CR10X, Campbell Scientific) was used to automate the sampling sequence. The CR10X controlled the pressure controller on the cell and managed the data transfer. We use gas cylinders traceable to the National Oceanic and Atmospheric Administration World Meteorological Organization scales for calibration (NOAA-WMO-X2004A scale for CH 4 , WMO-X2014A for CO, and NOAA-2006A for N 2 O). These gas standards were recalibrated before, during and after the deployments to maintain traceability. The low mixing ratio gas cylinder contained 298. 5 ± 0. 3 ppb of N 2 O, 1692. 4 ± 0. 2 ppb of CH 4 , and 119. 1 ± 0. 3 ppb of CO. The high mixing ratio gas cylinder contained 399. 1 ± 0. 3 ppb of N 2 O, 2182. 5 ± 0. 3 ppb of CH 4 , and 192. 8 ± 0. 5 ppb of CO. Detailed information on calibrations of the gas cylinders used during ATom is given in Table  S1  of the Supplement.
These spectral artifacts ultimately reduced the accuracy of mixing ratios retrieved from spectral fitting. The spectral artifacts most strongly affected the measurements of CH 4 and N 2 O. Several post-processing methods using the TDL-Wintel software were explored to improve the precision and accuracy of ATom QCLS N 2 O data, most with little success. Since the measured spectra were all saved, it is possible to refit the data with different fit parameters. A limited number of interference fringes may be included in the set of fitting functions. However, none of the previously used full refitting strategies significantly improved the data accuracy. We have achieved significant improvements in the precision and accuracy of the ATom QCLS N 2 O data using a new method dubbed the "Neptune algorithm," developed by Aerodyne Research, Inc.
We paired the mixing ratio records with the corresponding spectra (1 s resolution) for each species (CH 4 and N 2 O). 2. We grouped the mixing ratios and spectra by typeinto calibrations (zeros, low span, and high span) and air samples -and by time. The spectral data were thus arranged in an array with point number in the spectrum as x and spectrum number as y. We calculated an average spectrum for each group type and subtracted these from each individual spectrum within a group. 3. We zeroed out the spectral arrays at the positions of the absorption lines to concentrate on the fluctuations observed in the baseline and to prevent the PCA from finding line-depth fluctuations as relevant vectors during the calibrations. Some degree of smoothing (in x) was applied to the subtracted spectra so that high-frequency fluctuations, which have little influence on the mixing ratio determination, are not represented.
The linear combination of amplitudes that links spectral fluctuations in the baseline to mixing ratio fluctuations during calibrations was then applied to the full data set. That generated the retrieval errors for uncalibrated mixing ratios for the whole time series. We subtracted the errors from the initial retrievals from the TDLWintel-QCLS software and computed calibrated mixing ratios using the corrected retrievals for both calibrations and samples. An example of the result of applying the Neptune algorithm to the N 2 O samples and calibrations for the ATom-4 flight on 12 May 2018 is shown in Fig. 1b. The approach used here to minimize the effect of changes in pressure and temperature in optical instruments was based on the observation of fluctuations of the baseline during calibrations.
Accuracy of N 2 O measurements from QCLS We evaluated N 2 O mixing ratios measured by QCLS against three other instruments that measured N 2 O on the NASA DC-8 aircraft during ATom. In addition, we compared the set of four airborne measurements to data from the flask sampling network at ground stations from the NOAA Global Monitoring Laboratory (GML, https://gml. noaa. gov/, last access: 10 December 2020) to evaluate the differences between the airborne data and the ground-based measurements in the NOAA reference network.
13 ppb in AT-2, 1. 49 ± 0. 19 ppb in AT-3, and 1. 18 ± 0. 17 ppb in AT-4. The final official archive data file includes a new column where these corrections have been applied (N 2 O_QCLS_ad). These results show the very close comparability of the ATom airborne N 2 O instruments (differences were < 0. 5 ppb for UCATS and PANTHER instruments and 0 ppb in the case of PFP) relative to the surface stations and demonstrate the feasibility of using ATom N 2 O measurements to evalu-ate the impact of stratospheric air and meridional transport of N 2 O emissions on N 2 O tropospheric column measurements over the ocean basins. In the following section, we define the boundary conditions that were used to evaluate that impact, which were based on the NOAA Greenhouse Gas Marine Boundary Layer Reference from the NOAA GML Carbon Cycle Group (NOAA/ESRL GML CCGG, https://gml.
Tropospheric N 2 O tends to increase towards northern latitudes as a result of higher anthropogenic emissions in the Northern Hemisphere relative to the Southern Hemisphere. More details on the variability of N 2 O mixing ratios along the tropospheric column are described in Sect. S1. We study the impact of N 2 O sources and stratospheric air on the N 2 O column based on the anomalies (enhancements and depletions) we observed in the airborne N 2 O mixing ratios relative to the N 2 O "background," defined here as the NOAA-MBL product. We use the NOAA-MBL product to constrain a latitudinal gradient of N 2 O mixing ratios at the surface for each deployment. These data have been widely used to estimate the N 2 O background  (Assonov et al. , 2013; Nevison et al. , 2011). More information about the NOAA-MBL product and the latitudinal gradient of their measurements is discussed in Sect.
N 2 O enhancements over the Pacific Episodes of N 2 O enhancement were frequently observed at mid-latitudes in the southern Pacific Ocean, and these were linked by the associated footprints to emissions over the continents. In this region, N 2 O enhancements are predominantly associated with air masses with enhanced H 2 O 2 , PAA, and CO. For example, consider Fig. 5 , which shows data from profile 12, obtained at 49. 5-50 • S and near the Date Line on 3 May 2018. A distinct peak in N 2 O of amplitude 1 ppb at 1700 m altitude is significantly correlated with enhancements in CH 3 CN. These associations and the footprints suggest a regional contribution from fuel types from the industrial zone of Australia (Fig. 5c ), which is also supported by the aerosol characterization from PALMS (not shown for brevity).
During ATom, we observed large contributions to the tropospheric N 2 O over the Atlantic Ocean from Africa, along with some contributions from Europe and South America. During AT-2, we found strong correlations in the subtropical and tropical regions over the Atlantic between N 2 O, H 2 O 2 , PAA, HCN, CO, CO 2 , SO 2 , OA, NH + 4 , and SO 2- 4 at altitudes between 0 and 2. 5 km, representing the combined influence of photochemistry (r 2 N2OvsPAA = 0. 94), biomassburning events from the Congo region (r 2 N2OvsHCN = 0. 95), and the industrial production of N 2 O from oil and gas emissions from the Niger River Delta in Africa (r 2 N2OvsSO2 = 0. 84). An example is shown in Fig. 8  for 15 February 2017 (see also Fig. S12  and the land contribution in Fig. S13 ). To understand the origin of the enhancements in N 2 O, we calculated the enhancement expected in the atmosphere based on monthly mean estimates of anthropogenic emissions from the Emissions Database for Global Atmospheric Research (EDGAR, http://edgar.