dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional GO annotation study and pathway enrichment analysis were performed on the transcriptome-or translatomeassociated DEGs and revealed biological processes and signaling cascades associated to Mtb infection and rapamycin treatment (Figures  2, 3 ). In particular, in the transcriptomes of both Mtbinfected DC and Mtb-infected DC treated with rapamycin there was an enrichment in GO terms related to cytokine production, cytokine signaling and innate immune response (Figure  2A ). In the GO enrichment profile of the translatome, in addition to innate immune response, biological processes linked to type I interferon (IFN) signaling and defense response to other organisms were found (Figure  2B ). Pathway enrichment analysis was performed with both Reactome and KEGG databases and, in line with GO study results, the modulation of signaling cascades related to antigen processing and presentation, TLR receptors signaling, cytosolic nucleic acid sensors and cytokine-cytokine receptors was highlighted at transcriptome level for Mtb-infected DC in a rapamycin-independent manner (Figure  3 ).
These data prompted us to investigate whether the fine modulation of pro-and anti-inflammatory cytokine levels in our experimental model involved GSK-3b, a constitutively active Ser/Thr kinase, which, when phosphorylated at the inhibitory S9 site by p70S6K, undergoes proteasomal degradation  (25) (26) (27). To address this hypothesis, GSK-3b phosphorylation status was investigated. A gradual increase in the phosphorylation level at S9 site was revealed indicating that, in DC, GSK-3b is rapidly and persistently turned off upon Mtb infection (Figure  7A ). Interestingly, phosphorylation of p70S6K at T389 was lowered by the addition of rapamycin in both control and Mtb-infected DC at 16 and 24 h, with a more pronounced effect in infected cells at the latter time point (Figure  7B ). Similarly, GSK-3b phosphorylation at S9 site was abrogated by rapamycin, which in turn promoted also the phosphorylation of glycogen synthase (GS), one of the canonical substrates of GSK-3b.
This study showed that rapamycin likely promotes transcription of a panel of proinflammatory/regulatory cytokines including IL-23, TNF-a, IL- 1b and IL-6 in addition to IL-12 and inhibits the expression of the anti-inflammatory IL-10. These findings mirror previous data on rapamycin-driven CD4 + T-cell activation and differentiation via IL-12/IL10 release from LPS-stimulated DC  (48). Our data were validated by measuring the level of secreted cytokines. In addition to the PI3K/mTOR pathway, it has been shown that the constitutively active kinase GSK-3b contributes to the balance of pro-and anti-inflammatory cytokines in LPSor Listeria monocytogenes-stimulated APC  (25, 37, 49, 50). In particular, the mTOR-dependent inhibition of GSK-3b favors nuclear translocation of CREB and in turn its binding to the coactivator of transcription CBP, while it reduces the amount of NF-kB p65 associated with CBP, thus impacting on the balance of pro-and anti-inflammatory cytokines  (25, 26).
INTRODUCTION Astronomy, like many other branches of science, has been experiencing an explosive increase in data volumes, which are doubling roughly every two years. This revolution has driven a renaissance in many areas of astronomy, most notably in the time domain. At some level, all astronomical sources exhibit changes in their brightness with time, driven by a myriad of different phenomena. The study of source variability has benefited greatly from the data deluge providing insight into a broad range of astrophysical processes and phenomena. The light curves of variable objects contain information about the nature of the objects and the physical processes that are responsible for the observed changes. Variable objects are a key tool in astrophysics and are the main science driver in many fields.
2015). The necessary first step in enabling all these applications is to identify variable sources and classify them into known object types while simultaneously looking for new classes. The astronomical community has extensive experience in dealing with large samples of light curve data enabled by survey telescope automation and advances in both the camera technology and data processing and analysis techniques. Notable examples of large-scale surveys are: the All Sky Automated Survey (ASAS; Pojmanski 1997), the All Sky Automated Survey for Supernovae (ASAS-SN;  Shappee et al. 2014 ), the Asteroid Terrestrial-impact Last Alert System (ATLAS;  Tonry et al. 2018) , the Catalina Real-Time Transient Survey (CRTS;  Drake et al. 2014) , EROS  (Tisserand et al. 2007) , Gaia  (Gaia Collaboration et al.
Section 2 describes the ZTF light curve data including pre-processing and feature extraction. In Section 3, we introduce the classification scheme adopted for ZTF and describe the ML algorithms used therein. In Section 4, we present the active learning approach to labeled data set assembly and classifier training. The performance of the resulting classifiers are dis-cussed in Section 5. Finally, in Section 6 we discuss this initial study and outline our future work plans.
This broad range in the number of epochs per field is partially due to observability (lower declination fields tend be have fewer epochs), but mostly because of the smaller surveys which tend to accumulate many epochs for small sets of fields. The median number of epochs per field in the secondary grid is much lower (median < 50 for all filters). The low number of epochs makes classification challenging, and we did not include them in this study but will do so in future work. All ZTF images are processed and data products are automatically generated. This includes light curves of all persistent sources in the science images, which are the main data product for this work. Here, we summarise the process, for a full description, see  Masci et al. (2019). First, reference source lists are generated by running a source finding algorithm on reference images.
Data storage and access Efficient data storage and access, given the data set size, represent a substantial problem. We solved it by employing Kowalski 4  , an open-source system used internally at Caltech to store the ZTF alert and light curve data together with external catalogs and access those through a standardized API  (Duev et al. 2019a). We used Kowalski to efficiently feed the feature computation pipeline  (Coughlin et al. 2020a)  with the ZTF light curve data and store the results. Additionally, the (versioned) classifier predictions have been stored in a dedicated database that fed the active learning process described in Section 4.
CLASSIFICATION SCHEME Astronomical ground-based light curve data are usually sparse, unevenly sampled, and heteroskedastic, and ZTF is no exception to this general rule. A variable object classification framework must tackle these challenges. First of all, the input image data used to generate the light curves are affected by a broad range of factors such as the weather, the observability of fields, and the cadences of different sub-surveys within ZTF. In addition, the accuracy of the photometry decreases for fainter objects. The result is that objects belonging to the same class will have different noise levels and appear different to the classifier. The second problem is that for some types of variable objects, a light curve in a single filter is insufficient for correct classification. Frequently, additional observations are needed in a different bandpass, either optical, infrared, radio or high-energy.
In some cases even this is insufficient, and the intrinsic luminosity must be known (e. g. by using the distance from, say, Gaia parallax). Evidently, these data are not always available for all ZTF sources making the external data to be used by the classification framework inhomogeneous and potentially biased towards specific object subsets. Further, there are several challenges specific to particular classes of variable objects. Some types of objects are more abundant than others so that one has to frequently deal with very imbalanced data sets, with class examples ranging from hundreds of thousands down to just a handful. In addition, the source taxonomy 5  adds to the challenge as classes can be overlapping. For example, an accreting white dwarf -red dwarf binary (a cataclysmic variable) can be both outbursting (e.
If the performance on a particular class is deemed insufficient, retraining the classifier with new training data (or employing a different architecture) does not affect the system performance on other classes. Adding new types of variable objects is straightforward and also does not affect other classifiers. As ZTF continuous operations and the temporal baseline and number of epochs increases, new types of variables become detectable, which only requires new classifiers to be added, instead of having to rebuild an entire multi-class or multi-label classifier. Another advantage is more flexibility for the end-user. Depending on the (astrophysical) class and the scientific goal, requirements for completeness 6  and purity 7  can be very different. This trade-off is easier to interpret when using binary classifiers.
Therefore, these classifiers do not use any external data for the classification to avoid biases and enable independent analyses. Our second group of classifiers -"ontological " -is geared towards the categorization of specific types of variable objects based on as much information as is available for a particular object. The result can then be used to easily obtain a large, pure sample of that particular type of variable. Alternately, by also including lower-scoring examples one can use the result as the input for specialized pipelines to discover new sources (e. g. fitting eclipsing binary light curves with binary star models). We note that these classifiers use features from external catalogs in addition to ZTF data and are therefore prone to non-ZTF-specific biases.
As additional information, the location on the Gaia observational HR diagram and a Pan-STARRS image cutout are displayed. Labels can be assigned using a set of range sliders representing the class labels. The slider values are quantized to 0, 0. 25, 0. 5, 0. 75 and 1, to enable a human scanner to indicate how certain they are of their classification, the information that can be used in classifier training. Being part of the ZTF Variable Marshal, all interactions with the interface can be carried out programmatically via API calls. Contrary to a common misconception, data labeling is actually a job for highly-skilled, trained professionals that takes most of the time and is one of the most important parts of the work to build any successful ML system. We started with multiple experts performing classification using different user accounts, but later moved to regular multi-expert classification sprints that used a single user account.
This approach proved to be superior as it effectively averaged input from multiple experts and minimized the number of mistakes while labeling 10. To test our pipeline during development, we selected a subset of the ZTF data. In order to obtain a representative set, we chose ten pairs of ZTF-fields, taking into account the RA and Dec, and the Galactic latitude, see Fig. 4  and Table 4. These fields contain a diverse range of Galactic environments and also span a range of different cadences and total number of epochs. We use the g, r, and i band light curve of these fields, a total of ≈34 million. In order to explore the data and build an initial training sample, we visually inspected two sets of random and non-variable light curves (from a field in the test-set) by simply selecting light curves identified as outliers in the IQR-magnitude phase space.
XGBoost Similar to DNN, XGBoost has a large number of hyperparameters. These can be categorized as general parameters, tree boosting parameters, learning parameters, etc. A thorough hyperparameter tuning is generally not possible, and, indeed, not practical. Various methods are adopted to find near-optimal values for some of the parameters that should be tuned. Some of the critical parameters are: • max depth: this indicates the depth of the tree, with greater depth indicating more complex models, in turn implying models that are more prone to overfitting, • min child weight: this is a parameter that determines when further partitioning of a tree will stop. Larger numbers indicate a more conservative approach, • subsample: this determines the fraction of the data that the boosting algorithm will use at each boosting iteration, • colsample bytree: this is a counterpart to subsample but pertaining to the columns.
In other words, it is the number of features that will get used in each tree, • eta: this is the learning rate and is applied after every boosting step. All of these parameters affect tree boosting. We tuned these parameters, and, since we use all the available data which is very unbalanced, we tuned one more viz. • scale pos weight: this parameter decides if one of the classes needs to be given extra weight while fitting because it has fewer samples. Given the way XGBoost determines the splits using its complex parameters, even for unbalanced classes, one is often fine with leaving this parameter set to one. We started with scale pos weight, giving it four choices viz. [1, CR/2, CR, 2*CR] where CR is the ratio of samples belonging to the two classes. Then we tuned max depth (from 3 to 7 at a spacing of 2) and min child weight (from 1 to 5 at a spacing of 2) simultaneously, sampling the grid at nine points.
These sets contain easy-to-classify objects, and our classifiers should demonstrate excellent performance on them, which is indeed the case (see Fig. 10 ). As can be seen in the figure, most classifiers confidently classify the gold set correctly. There are a few exceptions, for example, the semi-regular variable classifier does not seem to perform as well as the other classifiers (mostly due to a lack of examples). There is also some confusion between some of the binary classes; the difference between EA and EB light curves is subtle, so this confusion is expected. We also compare our classification scores for the periodic variable classes with the results from  Chen et al. (2020). Chen et al. (2020)  used the ZTF data from Data Release 2 to search for periodic variable stars. They classified the variable stars by comparing the periods and parameters describing the light curve shape.
ACKNOWLEDGMENTS We thank the referee for useful and constructive feedback on the manuscript. Based on observations obtained with the  Samuel Oschin Telescope 48-inch  and the 60-inch Telescope at the  Palomar Observatory  as part of the  Zwicky Transient Facility  project. ZTF is supported by the  National Science Foundation  under Grant No. AST-1440341  and a collaboration including  Caltech , IPAC, the  Weizmann Institute for Science , the  Oskar Klein Center at Stockholm University , the  University of Maryland , the  University of Washington (UW) ,  Deutsches Elektronen-Synchrotron and Humboldt University, Los Alamos National Laboratories , the  TANGO Consortium of Taiwan , the  University of Wisconsin at Milwaukee , and  Lawrence Berkeley National Laboratories. Operations are conducted by  Caltech Optical Observatories, IPAC , and  UW.
DAD acknowledges support from the  Heising-Simons Foundation  under Grant No. 12540303. AAM acknowledges support from the  NSF  grant  OAC-1640818. MWC acknowledges support from the  National Science Foundation  with grant number  PHY-2010970. The authors acknowledge support from  Google Cloud. The authors acknowledge the  Minnesota Supercomputing Institute 13 (MSI)  at the  University of Minnesota  for providing resources that contributed to the research results reported within this paper under project " Identification of Variable Objects in the Zwicky Transient Facility. "This research used resources of the  National Energy Research Scientific Computing Center (NERSC) , a  U. S. Department of Energy Office of Science User Facility  operated under Contract No. DE-AC02-05CH11231  under project " Towards a complete catalog of variable sources  to support efficient searches for compact binary mergers and their products.
of phenology, we introduced soil-water availability as a common driver of foliage development and productivity for all of these systems. The new approach was tested by using a sample of sites (calibration sites) that provided NEE from flux tower observations as well as leaf area index data from satellite images (MODIS, MODerate resolution Imaging Spectroradiometer). For assessing the simulation accuracy, we applied the calibrated model to 42 additional sites (validation sites) across West Africa for which measured aboveground biomass data were available. The model showed good performance regarding biomass of crops, grass, or trees, yielding correlation coefficients of 0. 82, 0. 94, and 0. 77 and root-Published by Copernicus Publications on behalf of the European Geosciences Union. mean-square errors of  0.
For this analysis, a literature review was first carried out to find suitable parameters for the physiological processes of typical grass and tree species in Sahelian and Sudanian savanna ecosystems. Furthermore, thresholds of soil-water availability for leaf development and senescence, which were derived from 12 sites with ample data available for several years, were introduced. Finally, the model was evaluated against data from other sites representative of the region, including eddy flux measurements, satellite data, and in situ biomass measurements for both managed and natural ecosystems. The objective of this paper is to test the ability of the LandscapeDNDC model in its current form to simulate C fluxes and stocks for various representative savanna ecosystem types with varying human management activities within the Sahelian and Sudanian regions.
In the Sahelian ecological zone which extends over 1. 3 × 10 6 km 2 , the average annual temperatures vary between 25 and 31 • C and the annual precipitation is between 150 and 600 mm. The length of dry season lasts for 7-9 months annually and the monthly precipitation maximum is in August. The Sudanian zone, which covers approximately 1. 7×10 6 km 2 , is cooler (22-29 • C), wetter (600-1200 mm yr -1 ), and the dry season length is around 4-7 months with monthly maximum precipitation also occurring around August (NASA POWER climate dataset, https: //power. larc. nasa. gov/, last access: 16 June 2021).
Initialization, climate, and deposition As model input data, initial soil and vegetation properties as well as daily climate data are required. Land management needs to be prescribed as boundary conditions. The soil parameters were bulk density (kg m -3 ), pH, soil texture (i. e. , clay, silt, and sand content), organic C and N content (kg kg -1 ), and soil hydrological parameters (i. e. , field capacity, wilting point; in mm m -3 ). These were gathered from available literature sources for each site and complemented by information from the principal investigators (PI) of these sites. In a few cases, soil information was complemented by data from ISRIC-WISE (International Soil Reference and Information Centre -World Inventory of Soil Emission Potentials) soil dataset  (Batjes, 2008). Similarly, vegetation was initialized using data of the amount of grass biomass, tree sizes (average height and breast height diameter), and number of trees per hectare as recorded at the sites.
Input climate data were maximum and minimum temperature ( • C), precipitation (mm), relative humidity (%), solar radiation (W m -2 ), and wind speed (m s -1 ). These data were either obtained directly from measurements at the sites or were sourced from the NASA POWER climate dataset. Except for Kelma, all sites were assumed to have no ground water access down to their maximum rooting depth of 1 m. For the Kelma site, a flooding period was prescribed for 2005-2007, according to published water content data  (Timouk et al. , 2009; de Rosnay et al. , 2009). Management was also prescribed for both agricultural sites and savanna grasslands that were occasionally grazed with cattle. Fire impacts were not considered. In addition to climate, daily input of dry and wet deposition of oxidized and reduced N compounds (nitrate, ammonia, nitric acid, and nitrogen oxide) were provided for the model runs.
These were derived from the field measurements within the IGAC/DEBITS/AFRICA (International Global Atmospheric Chemistry/Deposition of Biogeochemically Important Trace Species/AFRICA) project, in which some of our investigation sites were also included  (Agoufou, Banizoumbou, Djougou, and Katibougou). According to this dataset, the total wet deposition of N was estimated to be around 3. 2 kg, and dry deposition was estimated to be around https://doi. org/10. 5194/gmd-14-3789-2021 Geosci. Model Dev. , 14, 3789-3812, 2021 3. 6 kg N ha -1 yr -1 , with only little variation between Sahelian and Sudanian ecosystems (Galy-Lacaux et al. , 2014).
Carbon flux and soil moisture measurements We collected net ecosystem C exchange (NEE) measurements from 12 flux tower sites, hereinafter called the core sites, in the Sudanian and Sahelian ecological zones. These data were used for calibrating the new phenological routine of the LandscapeDNDC model and to evaluate simulations for crop-dominated, tree-dominated grassland/woodland, and grass-dominated ecosystems. The spatial distribution and characteristic of the core sites show that they are well distributed across the major natural and agricultural areas in the Sudanian and Sa-helian ecological zones covering grassland savannas (from sparse grasslands to shrublands), woodlands (including seasonally flooded, open, and dense forests), and cultivated land types (with major crops including maize, Zea mays; millet, Pennisetum glaucum; sorghum, Sorghum bicolor; peanut, Arachis hypogaea; and cassava, Manihot esculenta (Fig.
1 ; Table  1 ). Five sites are located in the Sahelian ecological zone (Dahra, Senegal; Kelma, Mali; Agoufou, Mali; Wankama-1 and -2, Niger), and seven in the Sudanian ecological zone (Sumbrungu, Ghana; Bontioli, Burkina Faso; Kayoro, Ghana; Nalohou, Benin; Nazinga, Burkina Faso; Niakhar, Senegal; Bellefoungou, Benin). For more information, see Table  1  and the references therein. In addition, soil moisture data measured close to the flux towers were used for evaluation of simulations. These measurements took place mostly within the first 30 cm (CS616 soil moisture probes, Campbell Scientific Inc. , USA), although deeper soil horizons were also considered at the AMMA sites. In few cases, only moisture of the uppermost layers were recorded for comparison with estimates from satellite observations.
Aboveground biomass and leaf area measurements Aboveground herbaceous biomass and crop yield were available from sampled measurements for all core sites except for two of the Sudanian cultivated sites (Kayoro and Nalohou). For the Kayoro site, biomass was assumed to equal that at the Vea site in Ghana, and for the Nalohou site, maize data were taken from the Dassari site in Benin and the Wa site in Ghana, sorghum was taken from the Samanko site in Mali, and cassava was taken from the Ikenne site in Niger, because these sites have a similar climate. In addition, 42 sites representing cropland (11), grassland savanna (  27 ), and grassland ecosystems with significant tree contributions (4) from which biomass data are available were used for validating the revised model (Fig. 1 ). These sites are located in Senegal, Benin, Mali, Ghana, Niger, and Burkina Faso covering overall 70 years of observations within the 1984-2018 period (not continuous for all sites).