dataesr/scientific-paragraphs-categorization huggingface dataset:
| Interspecific variation and phylogenetic heritability of yolk THs For the first time, we assessed the interspecific variation in mater- with a CV = 112. 92% (Gil et al. , 2007) ]. Even after taking into account key life-history traits (i. e. migratory status, developmental mode and body mass) and the influence of captivity, we still detected moderate to strong phylogenetic heritability on both yolk THs. This indicates that besides life histories, a substantial proportion of variation in maternal THs is accounted for by the phylogeny itself. Our assessment on raw yolk TH data also.
Brommer for his kind and helpful statistical advice in the beginning stage of data analysis and Jorma Nurmi for excellent nest-finding skills and effort in the field. Mass spectrometry analyses were performed at the Turku Proteomics Facility, University of Turku and Åbo Akademi University. The facility is supported by Biocenter Finland. This study was funded by a grant from the Academy of Finland to SR (grant no. 286278). BYH was initially supported by the same grant to SR and later by the Ella and Georg Ehrnrooth Foundation and the Academy of Finland (grant no. 332716). RAV was supported by the research grant CONICYT -PIA -CCTE AFB170008 -IEB and Cape Horn International Center (CHIC-FB210018), Chile. CO N FLI C T O F I NTE R E S TThe authors claim no conflict of interest. AUTH O R S ' CO NTR I B UTI O N SS.
They were usually people with a good understanding of the innovation and the local partnership dynamics. Their role was to broker and network for key partners to work together and make scaling happen. The Scaling Readiness monitors were mainly responsible for applying the Scaling Readiness tools to collect and analyze data with the objective to influence decisionmaking and strategy development with the broader scaling project team. They were usually research assistants with good data collection and analysis skills. Two different scaling consultants were recruited to backstop the scaling champions and Scaling Readiness monitors and to ensure cross-project learning. During the two years of implementation, the scaling champions, Scaling Readiness monitors, and scaling consultant worked closely together with the Scaling Fund project teams.
Scaling Readiness Step 2 in the Cassava Flash Dryer Case Study Based on the innovation packages defined for the flash dryer scaling work in Colombia, DR Congo, and Nigeria, the project team assessed the innovation readiness and innovation use. The first step to determine the innovation readiness and innovation use level was to collect background information via a short desktop study. The Scaling Readiness monitors gathered information about the available evidence on the readiness and use of the innovation package core and complementary innovations from academic and technical databases and repositories. To complement the desktop study, the project team also collected new data. For the innovations categorized as core innovation subcomponents, small-scale processors of cassava flour and starch and flash dryer manufacturers from Colombia, DR Congo, and Nigeria were visited at each location to collect technical information for their innovation readiness (y-axis) and innovation use (x-axis) specific to space, time, and goals  (Sartas et al.
2020a)  used in the analysis. For innovations categorized as services and institutional arrangements (Table  3. 2), information was collected through surveys with different value chain actors, such as bankers, cassava producers, processors, and representatives of government organizations, among others. These surveys were administered during stakeholder meetings and forums in each location. The information collected via the desktop review, field measurements, and survey results were processed by the Scaling Readiness monitor to determine the innovation readiness and innovation use level for each of the innovations in the innovation package for the three country contexts. A Microsoft Excel template was used to plot the Scaling Readiness graph. The template enabled selection of innovation readiness and innovation use levels from a drop-down list and automatically generated the graph (see  Figs.
In this regard, one of the lessons learned from the flash dryer case was that it is not enough to carry out this analysis only at the beginning of the project, but that periodic diagnoses must also be carried out, since the context is dynamic and changing, and emerging bottlenecks can arise. Collecting information to design strategies and monitor scaling progress was a challenge in the case study project. Scaling Readiness collects this information through electronic surveys, but it was found that most of the project partners were unresponsive. Some were very busy or did not believe they had the capacity to complete an electronic survey. The challenge will be to develop mechanisms that capture as much information as possible while being user-friendly for the project partners. Collecting information during project meetings or as part of workshops seemed to be more promising in terms of response rates and data quality.
The time and responsibilities of senior staff are often fragmented, meaning they need to juggle to a broad variety of science, management, and leadership demands and expectations. Furthermore, scientists are often not on the ground in the context where scaling is desired. The environments in which the cassava flash dryer and other RTB Scaling Fund projects operate are very dynamic and require ongoing navigation and re-strategizing, which requires operational knowledge. One of the opportunities we see here is to decentralize management and decision-making in scaling projects so that on-the-ground scaling champions can act in a flexible manner based on the analysis and data provided by Scaling Readiness monitors. We have seen that Scaling Fund projects where such a decentralized model for decision making was applied seemed more successful in capitalizing on emerging opportunities and navigating change.
Acknowledgments This research was largely undertaken as part of, and funded by, the  CGIAR Research Program  on Roots, Tubers and  Bananas  (RTB) and supported by  CGIAR Trust Fund  contributors. The authors thank the following individuals who provided input and feedback on earlier drafts of this chapter:  Tom Remington ,  Jill Lenne , and  Christopher Butler. We greatly appreciate  Zandra Vasquez 's assistance with references and formatting and  Claudio Proietti  for his support in effectively managing the RTB Scaling Fund.
, 2013). Yw is determined by distribution and water supply during the growing season. This case usually occurs in rainfed systems, in which water supply from inseason rainfall and stored soil water is not enough to meet crop water needs  (Rattalino Edreira et al. , 2017). Yp and Yw models rely on the climate, soil, and management data to assume the influence of genotype, environment, and management practices on crop growth and yield  (Rotter et al. , 2015). The yield gap of maize cropping systems in Indonesia represents 56% and 58% of Yp and Yw in irrigated and rainfed fields. The yield gap modeling is based on the local climate, soil, crop management, and farmers' maize yield data. At the national level, the average farmers' maize yield just ss 44% of 13 44% of 13 represents. 6 ton/ha for the irrigated sites and 42% of 12.
This study was carried out to assess variation in the selected farmers' fields. Yield gaps were investigated by figuring out data on maize grain yield, crop management practices, crop management, applied inputs, and production site adversities. Data were collected from multiple personal interviews with farmers in the course of over two agricultural seasons in the selected areas. Protocol based on  Grassini et al. , (2015)  related to Global Yield Gap Atlas was used. Supporting data sources in the form of climates (rainfall, temperature, humidity, and solar radiation) were collected during the last 18 years  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017)  from Semarang weather station, Indonesian Agency for Meteorology, Climatology, and Geophysics, and NASA POWER service.
Five villages were selected according to the crop, while 20 farmers were selected for interview within each village. Hence, the total number of surveyed fields was 100 for the entire Central Java Province. The interview method used was a face-to-face interview. The interviewer was provided with returned studies handed out by agronomists, agricultural extension educators, crop consultants, technicians, and researchers with guidelines to collect the data. The collected data also covered field location in the form of a face-to-face interview. The interviewer was provided with returned studies handed out by agronomists, agricultural extension educators, crop consultants, technicians, and researchers with guidelines to collect the data. The collected data also covered field location in the form of e pictures of the field taken in every corner of the farmers' fields for high accuracy location.
The survey design was determined based on the selection criteria for provinces, districts, villages, and farmers (Table  1 ). The study also aimed to identify opportunities to close the existing yield gaps through management practices. The farmers are represented in the range of farmers in the local area. The farmers' maize fields data were selected not from the experimental sites, trial plots, and variety-testing plots. Farmer maize field is defined as a plot of land planted with maize managed with equal practices (e. g. , planting/ transplanting date, fertilizer amount and timing, variety choice, plant density) and harvested at the same time. The collected data were avoided from lands planted with more than two varieties, or fields with portions planted at very different dates (more than a four-day difference).
RESULTS AND DISCUSSION The Climates and Soils Orders of the Study Site The study areas were selected to point out the causes of yield gaps in the farmers' maize fields in Grobogan, Central Java. This region has a major crop sequence of Rice-Rice-Maize (60%). The production data were also collected to identify the largest crop area within Central Java Province. The fields that match the spatial locations of our study sites were selected for the project. Crop-specific harvested areas, lowland rainfed maize in Central Java, selected weather stations, and study areas around them were used to estimate the level of the yield gaps of maize in major producing areas. Secondary data were used as the major references of weather data. The data on daily rainfall, minimum and maximum temperature, humidity, and solar radiation during 2000-2017 were collected and used to release weather data with quality control based on the relationship between adjacent weather stations and selected weather stations.
The Level of the Yield Gaps of Maize The data on lowland areas in the regency level were obtained for rice and maize. The harvested area reported for each site has a function to estimate the average yield of the farmers based on regencies that overlap with the location of the buffers. The information obtained from official statistics, local extension agents, and agronomists were useful to assess grain yields and areas of maize for rainfed lowland. In the study area, an overview of the context of the cropping system was obtained by taking 2010 and 2015 data from RWS Semarang in the lowland landscape. There are two types of water regimes, namely manual and rainfed irrigation. This region's most common cropping sequence is rice-rice-maize, with an average grain yield of 7. 15, 5. 31, and 6. 22 ton/ha, respectively (Table  3 ).
The Causes of Yield Gaps in Farmers' Maize Fields The survey data obtained from the maize farmers were provided for management practices and to inform the average maize yield for each of their fields in each year. The proposed management practices for each RWS (Reference Weather Stations) include the dominant crop sequences, ecosystems, water regime, total harvested area, maize management (sowing and transplanting date, actual and optimal maize population density and spacing, variety, and tillage system), applied inputs (nutrient fertilizer, lime, irrigation, manure, and pesticides), and extent of abiotic and biotic stresses (flood, diseases, insect). The results showed the maize varieties used by farmers during the 2017-2018 period. There are six hybrid maize varieties commonly used in lowland areas (Figure  6 ).
The basic cross-correlation analysis of yield gaps describes the causes of the yield gaps at the field scale. The indicators used include data crop management, soil constraints, and biotics. The aftermath of various factors on the growth of maize and other results of crop population, nutrition, and water limitations were evaluated separately. Table  5  indicates there may be a large space for maize yield The farmers should shift from traditional to modern varieties suited to solve farming problems and improve market demand for yield grain. The maize production dominated by smallholder farming systems shows management practices in Central Java using N sources such as NPK and urea with an N rate of application (Table  5 ). The study of  Leitner et al. , (2020)  explains that increasing soil fertility and closing the yield gap by 75% could be through increased N fertilizer.
ACKNOWLEDGEMENTS The authors are grateful for the financial support through  Global Yield Gap Atlas Indonesia Project  from  Indonesian Agency for Agricultural Research and Development. Gratitude is expressed to  Prof. Patricio Grassini  ( University of Nebraska-Lincoln, USA ) and  Prof. Fahmuddin Agus  ( Center for Agricultural Land Resources Research and Development ) for insightful comments on this research. The authors also thank  Yuni Kamal  and  Warsito  ( Assessment Institute for Agricultural Technology of Central Java ) and Agricultural Extension from  Grobogan Regency  for excellent support in the fields.
Bt3a and Bt3b were the reverse complement of Bt2b and Bt1a primers from  Glass and Donaldson (1995) , respectively (File S2). Two separate PCR amplifications were carried out in a final volume of 50 µl containing: 5 µl of Fermentas Taq polymerase buffer, 0. 5 µM each primer, 2 mM each dNTP, 25 mM MgCl 2 , 1. 25 U of Taq DNA polymerase, and 2 µl of template DNA solution at a concentration between 2 and 80 ng/µl. The PCR cycles consisted of an initial denaturation step at 95°C for 3 min, followed by 35 PCR cycles with a denaturation step at 94°C for 30 s, a primer annealing step at 58°C for 30 s and an elongation step at 72°C for 90 s. A final elongation step was performed at 72°C for 10 min. Sanger sequencing of the PCR products was performed by Genewiz (South Plainfield, NJ, USA). Sequencing analyses were undertaken using Bio-Edit software.
β-tubulin gene sequencing Sequencing of the β-tubulin gene with two primer pairs produced a 1392 bp-long sequence for all the isolates analysed. A BlastX analysis of this fragment gave the highest score with the Diaporthe helianthi β-tubulin protein (identity 99. 62%) with an overlap at amino acids 22 to 398 of the protein. The analyses of the DNA sequences revealed a mutation, resulting in the substitution of glutamic acid with lysine at codon 198 (E198K) in all the isolates sequenced from the 16-0219 orchard. The substitution was caused by a change of guanine to alanine in the nucleotide triplet (GAGAAG) (File S4). The β-tubulin gene sequences of a wild and a mutant isolate were deposited in GenBank (accession numbers MG772818 and MG996732 for the sensitive and resistant isolates, respectively).
Acknowledgements We would like to thank all the field workers who participated in sample collection. This project was partly funded and supported by the  French Agency for Food, Environmental and Occupational Health and Safety (ANSES)  via the tax on sales of plant protection products. Proceeds from this tax are allocated to ANSES to finance a "phytopharmacovigilance" (PPV) system for monitoring the adverse effects of plant protection products, pursuant to the French Act on the Future of Agriculture of 13 October 2014.
Acknowledgements. We thank the participants in the palaeontological excavations in Peñacerrada I,  Arroyo de la Pascueta ,  San Just ,  El Soplao  and  Ariño. We are grateful for the collaboration with the SAMCA Group and the  Fundaci on Conjunto Paleontol ogico de Teruel-Din  opolis during our work in the Ariño outcrop. We thank the  Consejer ıa de Cultura ,  Turismo y Deporte del Gobierno de Cantabria (Spain)  and the  Direcci on General de Patrimonio Cultural del Gobierno de Arag on (Spain)  for giving us permission to excavate in the amber-bearing outcrops. We also thank the  Museo de Ciencias Naturales de Alava , the  Colecci on Institucional del Laboratorio de la Cueva El Soplao , and the  Museo Aragon es de Paleontolog ıa  for loaning their specimens. This work is part of the collaboration agreement between the  Universitat de Barcelona  and the public company El Soplao S.
tataricus. This observation suggests that the plant takes up and accumulates the fungal astins, most likely after their secretion by C. asteris, and is probably able to modify them. To understand astin production and regulation, we studied the interaction between the plant A. tataricus and the fungal endophyte C. asteris in more detail. The endophyte lives within the host plant and, thus, it was hypothesized that fungal astin biosynthesis in culture † Corresponding author: J. Ludwig-M€ uller; Jutta. Ludwig-Mueller@tu-dresden. de Funding: Financial support was provided by the European Union (European Regional Development Fund, ERA-IB Project Astinprod ERA-IB-15-039) and the Free State of Saxony (S€ achsische Aufbaubank grants 100271404 and 100271410). L. Jahn, T. Schafhauser, and J. Ludwig-M€ uller were funded by the S€ achsische Aufbaubank.
T. Schafhauser was supported by the Institutional Strategy of the University of T€ ubingen (Project ZUK63). W. Wohlleben was funded by the Bundesministerium f€ ur Bildung und Forschung (grant 0315934). W. J. H. van Berkel was supported by the Netherlands Organisation for Scientific Research  (Project ACTS 053. 80. 713). *The e-Xtra logo stands for "electronic extra" and indicates that supplementary figures and supplementary tables are published online. Conflict of interest: P. Jacques is a co-founder of Lipofabrik and Lipofabrik Belgium and a member of the scientific advisory board of both companies. can be stimulated and altered by the action of different plant hormones. Phytohormones such as auxins and cytokinins but also stress hormones such as salicylic acid play an important role in the crosstalk between the host plant and the endophyte  (Ali et al.
Biological materials. The fungus C. asteris originated from our own culture (deposited under the accession number DSM 100826 at DSMZ, Braunschweig, Germany), isolated from Aster tataricus in 2013  (Jahn et al. 2017 ) and maintained on MEAlow medium. The Arabidopsis thaliana ecotype Columbia (Col-0) used in the Petri dish assays was obtained from Nottingham Arabidopsis Stock Centre (NASC, Loughborough, U. K. ). Mutants of A. thaliana were obtained from different sources: sid2-1 and npr1-1 from Regina Mencia (Universidad Nacional del Litoral, Santa Fe, Argentina), NahG from Sabine Rosahl (Institute of Plant Biochemistry Halle, Germany), and jar1-1 as well as etr1-1 from NASC (Nottingham, UK). Seeds of sunflower (Helianthus annuus) and tomato (Solanum lycopersicum) were purchased from Hornbach (Bornheim, Germany).
S. A. ). The system was controlled by AB Sciex Analyst software (version 1. 5. 1). Due to a lack of internal standards for astins, only an estimation of the astin amount by area units was possible. A standard curve (0. 1 to 10 ng/ml) for astin C was done to link the arbitrary unit to an astin C concentration. Growth of A. thaliana on different astin concentrations. A. thaliana was cultivated in Petri dishes containing MS medium (MS including vitamins at 4. 4 g/liter, sucrose at 10 g/liter, and phytoagar at 10 g/liter; pH 5. 8 ± 0. 1)  (Murashige and Skoog 1962) , including different concentrations of astins A, C, (both dichlorinated), or G (nonchlorinated). Seven plants were sown per Petri dish (10 Petri dishes per treatment). The growth of plants was documented weekly as in the cocultivation system (root length and growth stage).
Mobile Genetic Elements (MGEs) are integrated in bacterial genomes and key elements that drive prokaryote genome evolution. Among them are Integrative and Conjugative Elements (ICEs) and Integrative Mobilizable Elements (IMEs) which are important for bacterial fitness since they frequently carry genes participating in important bacterial adaptation phenotypes such as antibiotic resistance, virulence or specialized metabolic pathways. Although ICEs and IMEs are widespread, they are as yet almost never annotated in public bacterial genomes. To address the need of dedicated strategies for the annotation of these elements, we developed ICEscreen, a tool that introduces two new features to detect ICEs and IMEs in Firmicute genomes. First, ICEscreen uses an efficient strategy to detect Signature Proteins of ICEs and IMEs based on a database dedicated to Firmicutes and composed of manually curated proteins and Hidden Markov Models (HMM) profiles.
ICEfinder uses several tools to detect proteins encoded by the integration and transfer modules and attempts to delineate some of the detected and well-known elements by looking for direct repetitions corresponding to the 3 ends of tRNA genes targeted by various ICEs and IMEs. The second initiative was led by the team of E. Rocha at the Pasteur Institute, France, who developed Conjscan  (20) , a module of the MacSyFinder software (  21 ) that identifies conjugative modules on plasmids and chromosomes by looking for the genes encoding their protein components and then checking that the composition and genetic organization of the system is consistent with that expected from a conjugative system. A complementary method based on comparative genomics is provided to delineate elements by synteny when at least four closely related genomes of the same species are available  (22).
In this article we present the result of our work, a new tool named ICEscreen. We describe the strategy implemented in ICEscreen and demonstrate its value for the scientific community through a benchmark study based both on the detection of 11 published and experimentally supported elements as well as on a dataset named FirmiData  (25). The latter includes 246 ICEs and IMEs manually annotated in 40 Firmicute genomes, among which 104 are located in complex composite structures.
Construction of the ICEscreen Signature Protein database Four types of SPs were used in order to detect ICEs and IMEs: (i) integrases which are required for the excision and integration of the element, (ii) relaxases which are proteins catalyzing a site-specific nick of DNA, (iii) the coupling proteins which initiate the transfer of the element through the conjugation pore and (iv) VirB4 which plays an essential role in the functionality of the conjugation pore. VirB4 and the coupling proteins are the most conserved conjugation proteins and therefore the easiest to detect even in elements with a distant conjugation module. The ICEscreen SP database was built in two distinct parts: (i) a protein database of curated SPs of ICEs and IMEs identified in Firmicutes, mainly in Streptococci  (11, 12)  and (ii) an HMM profile bank designed to detect distant homologs of SPs in all other non-Streptococcus genera of Firmicutes.