dataesr/scientific-paragraphs-categorization huggingface dataset:
Search results A flowchart of the identification of relevant studies is shown in Fig. 1. A total of 2985 articles were obtained from searching four databases, 738 were duplicates and 2225 articles that did not meet the selection criteria were excluded. The remaining 22 articles were obtained for full-text review  [10, 11, [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46]. Of these, eleven articles contained insufficient information and were excluded from the review  [10, 11, [38] [39] [40] [41] [42] [43] [44] [45] [46]. The remaining eleven studies were considered for the qualitative analysis in this review  [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] ; of these, seven were included in the meta-analysis  [28, 29, 31-33, 36, 37].
Four studies were cross-sectional and prevalence data reported in these studies were used to estimate the summary odds of P. falciparum infection in children infected with STH but  uninfected with intestinal helminths. The remaining three studies were longitudinal in nature  [31, 33, 37]. Longitudinal studies by Abanyie et al. [31]  and Kirwan et al. [37]  did not report incidence data, hence prevalence data reported in these studies during the baseline survey  [31]  and at the end of the follow-up survey  [37]  were used when estimating the summary odds of P. falciparum infection in children infected with STH. In the longitudinal study by Roussilhon et al. [33] , the odds ratio for the occurrence of at least one episode of P. falciparum malaria during the follow-up was used when estimating the summary odds ratio.
Classifying, describing and understanding the natural environment is an important element of studies of human, animal and ecosystem health, and baseline ecological data are commonly lacking in remote environments of the world. Human African trypanosomiasis is an important constraint on human well-being in sub-Saharan Africa, and spillover transmission occurs from the reservoir community of wild mammals. Here we use robust and repeatable methodology to generate baseline datasets on vegetation and mammal density to investigate the ecology of warthogs (Phacochoerus africanus) in the remote Luambe National Park in Zambia, in order to further our understanding of their interactions with tsetse (Glossina spp. ) vectors of trypanosomiasis. Fuzzy set theory is used to produce an accurate landcover classification, and distance sampling techniques are applied to obtain species and habitat level density estimates for the most abundant wild mammals.
INTRODUCTION Understanding the structure of natural ecosystems forms the basis for understanding the processes within those ecosystems, including the transmission of infectious and vector-borne diseases. Remotely sensed datasets and geographical information systems (GIS) have been widely used to further our understanding of these systems. This technology has not only helped in the study of the global drivers of ecological change, but is also invaluable for understanding the biotic and abiotic factors influencing ecosystems at much smaller scales. GIS technology has become an integral component of many conservation programmes and the development of trans-disciplinary approaches such as Conservation Medicine, One Health and EcoHealth have highlighted its utility. However, recent moves to adopt ecosystem-based approaches within conservation and development programmes have highlighted frequent deficiencies in baseline ecological data, particularly in developing countries  (Rapport et al.
(2012)  identified host distribution and abundance as having a significant influence on the survival of tsetse and therefore on trypanosomiasis epidemiology. Many other factors are also important including daily activity patterns of hosts and seasonal migration behaviour. Tsetse distribution and abundance is largely driven by climatic factors, host abundance and vegetation  (Robinson et al. 1997). A better understanding of the distribution and characteristics of both mammal and plant communities is therefore likely to improve our management of HAT. Here we generate accurate high-resolution datasets of vegetation and large mammal density in the remote, rela-tively inaccessible LNP within the Luangwa Valley, in order to investigate the ecology of warthogs and to further our understanding of their interactions with Glossina spp.
Landcover Classification Landsat 7 ETM+ Data Landsat 7 ETM+ data with a spatial resolution of 30 m were selected for this study. The most recent L1G Landsat satellite image covering the study area was downloaded from the Global Land Cover Facility maintained by the University of Maryland (image acquisition date 04/10/2001, path 170, row 069, cloud cover 0%) (NASA Landsat Programme 2004). Proprietary satellite image processing software Erdas Imagine 8. 4 (Leica Geosystems AG, Atlanta, USA) was used for all image processing and classification procedures.
Classification Scheme The classification scheme selected is, to a degree, dictated by the objectives of the study in question  (Congalton 1991). In this case, an important objective was to produce a dataset suitable for use as a GIS base layer for the design and spatial analysis of warthog and tsetse surveys planned for the park. Therefore, the classification level needed to distinguish between woodland classes, for example, rather than to simply classify to the broad physiognomic vegetation unit level (i. e. woodland). 1 The classification of vege-tation at the physiognomic level by  White (1983)  was first used to understand the vegetation units represented in the park. These physiognomic units were then further divided into individual land cover classes (Table  1 ). Particular reference was made to the previous detailed studies of the vegetation by  Astle et al.
Ground-Truthing Ground-truthing was conducted to confirm the classification scheme and collect reference training and test data in August and September, 2005. Evaluation of vegetation class relied on qualitative observations of vegetation physiognomy and predominant tree or shrub species present. Tree and plant species were identified with assistance from standard field guides covering the southern African region  (Van Wyk and Van Wyk 1997; Coates Palgrave 2003a)  and the Luangwa Valley  (Smith 1995; Coates Palgrave 2003b). Spatial area was used to create a sample frame for collection of reference data, with polygons of homogenous vegetation as the sample unit. Twenty or more sample units were collected for all classes using a hand-held global positioning system (GPS), except for acacia woodland (sixteen) and hill scrub miombo woodland (nine) whose limited distribution precluded the collection of more data.
polygon data. A subset of 25% of the points in each class were randomly selected and withheld from the training data, to be used as the test data. The training data were then used to create spectral signatures for each vegetation class. As the reflectance and emittance properties vary for different vegetation types, a spectral response pattern referred to as a spectral signature may be produced for each class from the training data  (Lillesand et al. 2004 ). The image was classified using a maximum likelihood classifier with the additional activation of the fuzzy classification function  (Pouncey et al. 1999). The feature space non-parametric decision rule was applied first and pixels in areas of overlap using this algorithm were then classified using the maximum likelihood parametric decision rule.
Transect Design The newly created land cover dataset was used to design a mammal density survey using distance sampling methods. The primary objective was to estimate the abundance of warthog and other mammalian hosts of trypanosomes and the secondary objective to map warthog burrow distribution. A ground transect survey was used rather than aerial survey techniques to enable more accurate estimation of the density of smaller species which are important hosts for trypanosomiasis. The study was conducted during the dry season from August to October, 2006, in the north of the park (the location for a concurrent tsetse survey). A single random starting point was generated to create a systematic grid of 40 parallel transects perpendicular to the Luangwa River. The length of each transect was 4.
Land Cover Classification Ten land cover classes were identified during the groundtruthing study (Table  1 ). The hill scrub miombo class was very small and could not be accurately mapped so was removed from the final classification (see discussion). The overall accuracy of the classification was 71. 2% (95% CI 65. 3-76. 7%). The image produced by the fuzzy classifier was significantly more accurate than that produced using a standard maximum likelihood classifier (McNemar's chisquared = 4. 6875, df = 1, P value = 0. 030). The error matrix is presented with the conditional kappa for the classified data rather than the reference data (Table  2 ). The area of the park as calculated from the classified image was 331 km 2 (33119 hectares) and the total perimeter length was 142 km (142102 m). The complete dataset for the final classified image (Fig.
Indeed, in his detailed floristic study of North Luangwa National Park, Smith (1998) grouped his vegetation categories into mosaics of vegetation types that could not be mapped separately at his chosen resolution. Although attempts were made to collect homogenous reference data in this study, it will have contained some heterogeneity and represented, in effect, 'fuzzy' ground-truth data. An important feature of fuzzy classifiers is that homogenous reference data are not needed. Most of the classes within the classification scheme performed well, with the exception of the hill scrub miombo class. A small area of this class was identified during the ground survey on the hills in the south eastern section of the park, but only at an altitude of 660 m or greater. The highest point in LNP, based on the 1:250,000 topographical maps  (Surveyor-General 1972) , is 680 m meaning this class was only present over a very restricted area.
Warthog Burrow Distribution Mapping of the warthog burrows over the classified dataset allowed the spatial pattern and habitat preference for burrow location to be examined. The clear pattern revealed in Figure  3  may be explained by the drainage of the soils, the ease of excavation and the provision of cover from predation. Combretum woodland and thicket are generally found on more sandy soils with better drainage in the rains and easier excavation. In contrast, mopane woodland and mopane scrub generally occur on clay soils, prone to seasonal flooding  (Smith 1998 ) and difficult to excavate in the dry season. Warthog have been reported to thrive in areas of wooded grassland bounding suitable floodplain grassland  (Rodgers 1984) , a situation which occurs especially towards the south of the transect study area in LNP.
Habitat Densities The use of the classified dataset also allowed the aggregate density of wild mammals to be examined by vegetation class. Not surprisingly, the highest densities were recorded in grassland with nutritious herbage providing for large densities of puku in particular. Although lower densities of large mammals were recorded in the riverine woodland and aquatic grassland classes, these habitats are likely to be very important ecologically, especially in the dry season as a source of forage and water. They may support a wide diversity of other species not included in the survey such as birds, amphibians and invertebrates. Acacia woodland forms only a very small component of the vegetation in LNP and animals were rarely observed in the dense stands of Acacia kirkii, but were more commonly seen in more open acacia woodland near the Luangwa River.
CONCLUSION This study provides a reliable framework for ecological monitoring of vegetation composition and wild mammal densities in remote, relatively inaccessible environments. Information generated can be used as a baseline for further study into wildlife disease systems. The use of classification algorithms based on fuzzy set theory enables accurate classification of vegetation classes despite the presence of natural mosaics and mixed pixels. The datasets created are ideal for use as a GIS base layer for the design, implementation and analysis of ecological and epidemiological studies. The distance sampling technique utilising a ground survey allows for reliable estimation of densities of smaller mammal species and important hosts of trypanosomes such as warthogs. The large survey effort required to estimate species density accurately in areas with relatively low wild mammal densities may limit the usefulness of this technique for health research in some environments.
ACKNOWLEDGMENTS We gratefully acknowledge support from the  Zambian Wildlife Authority  and the  Department of Veterinary Services, Government of Zambia. Funding was provided by the  Department for International Development Animal Health Programme (DFID-AHP)  and the  Royal Zoological Society of Scotland. NEA and SCW are funded with support from the  Ecosystem Services for Poverty Alleviation Programme (ESPA) ,  NERC  Project No. NE-J001570-1. EMF is funded by the  Wellcome Trust  ( 085308 ). Prof. S. Buckland  provided advice regarding the design of the transect survey and  Kepson Chansa  assisted with the field survey. James Milansi ,  Wilbroad Chansa ,  Victor Siamudaala  and  Adrian Carr  provided support and advice during the fieldwork.
All annotated vector maps for Level 0, Level 1 and Level 2 (pYB vectors) are available through the JBEI ICE database. Stacked constructs assembled for this study are further described in Supplementary Table  3. jStack assembly process. For Level 1 assembly, Level 0 parts are digested with their respective Type IIS restriction enzyme (New England Biolabs). Although Level 1 constructs can be assembled via one pot Golden Gate assembly  4  , we find that gel extractions (Zymo Research) of restriction enzyme-digested DNA parts allows for mixing and matching of DNA fragments of Level 0 parts that utilize different Type IIS restriction enzymes (for example, BsaI or BsmBI). Moreover, as only a small amount of the gel extracted product is necessary for the subsequent ligation of the Level 1 cassette, we routinely freeze the remaining amount for future use, as we have experienced robust ligation efficiencies with pre-digested DNA parts that have been stored up to half a year at À 20 °C.
1 Â 50 mm, 1. 8 mm) and with MS and MS/MS data collected using a QExactive Orbitrap mass spectrometer (Thermo Scientific, San Jose, CA, USA). Chromatography used a flow rate of 0. 4 ml min À 1 , first equilibrating the column with 100% buffer A (LC-MS water with 0. 1% formic acid) for 1. 5 min, then diluting over 7 min to 0% buffer A with buffer B (100% acetonitrile with 0. 1% formic acid). Full MS spectra was collected at 70,000 resolution from m/z 70-1,050 and MS/MS fragmentation data collected at 17,500 resolution using 10, 20 and 30 eV collision energies (averaged together). Violacein was identified in samples based on accurate mass m/z, and comparing retention time and fragmentation spectra to a purchased standard (violacein from Janthinobacterium lividum, V9389, Sigma). Identification of violacein biosynthesis pathway intermediates and shunt products was based on accurate mass m/z.
Ubiquitin (GLy20g27950) was used as the endogenous control with primers: F: 5 0 -ACCCT TCACCTTGTCCTCCGTC-3 0 , and R: 5 0 -GACACATTGAGTTCAACACAAAC CG-3 0. Expression of transgenes from the synthetic gene cluster was assayed for neomycin phosphotransferase II (primers: F: 5 0 -GGAGAGGCTATTCGGCTA TG-3 0 , and R: 5 0 -GCCAACGCTATGTCCTGATA-3 0 ); At5g45060 (primers: F: 5 0 -ATGTTGCAAAACCACTGGCC-3 0 , and R: 5 0 -TCCAACCGCTTCATGACC AA-3 0 ); At5g45050 (primers: F: 5 0 -GTCTCTCATACGTGTATCTTCCAATGG-3 0 , and R: 5 0 -AAACATTTGGATCAGTGCGG-3 0 ); and At3g51560 (primers: F: 5 0 -AT TGCATTCCCCAAAGTCAA-3 0 , and R: 5 0 -TTGCGACCTATGGATTGGAT-3 0 ). Data availability. The authors declare that the data supporting the findings of this study are available within the article and its Supplementary Information Files are available from the corresponding author upon request.
All vectors and resources described are publicly available and can be found through the Inventory of Composable Elements (ICE) at https://acs-registry. jbei. org/. Once logged in, the plasmids and strains are listed under the JBEI Public Registry tab. Finally, the sequences of pYB1301 (KX817179), pYB2301 (KX817180) and pYB3301 (KX817181) were deposited to the NCBI nucleotide database.
Acknowledgements We thank  James Kirby  and  Ee-Been Goh  for technical expertise and helpful comments on bisabolene experiments. This work was part of the  DOE Early Career Award  and the  DOE Joint BioEnergy Institute  (http://www. jbei. org)supported by the  U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research ; and  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility  through contract  DE-AC02-05CH11231  between  Lawrence Berkeley National Laboratory  and the  U. S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for  United States Government purposes.
Africa is often seen as a continent of mass migration and displacement caused by poverty, violent conflict and environmental stress. Yet such perceptions are based on stereotypes rather than theoretically informed empirical research. Drawing on the migration and visa databases from the Determinants of International Migration (DEMIG project) and the Global Bilateral Migration Database (GBMD), this paper explores the evolution and drivers of migration within, towards and from Africa in the post-colonial period. Contradicting common ideas of Africa as a 'continent on the move', the analysis shows that intra-African migration intensities have gone down. This may be related to state formation and the related imposition of barriers towards free movement in the wake of decolonisation as well as the concomitant rise of nationalism and inter-state tensions.
On the other hand, conventional accounts of African migration tend to ignore the role of African states in shaping migration. This reflects the more general Eurocentric (destination-country) focus of migration research. In order to fill these research gaps and gain a better understanding of the nature and causes of African migration, this paper analyses the evolution of migrations within, towards and from Africa in the post-colonial era, and explores the main factors explaining changes in the volume and the direction of these migrations. It will do so by drawing on new longitudinal databases containing data on migration stocks and flows, which have significantly extended the capacity to perform such analyses. Before embarking upon the empirical analysis, however, we will further explore the theoretical arguments that compel us to critically rethink role of development and states in migration processes.
Data African migration research is haunted by the lack of reliable official data and the absence of appropriate sampling frameworks in the form of census or survey data. Although these problems are far from resolved, recently, the availability of new migration databases has significantly expanded the scope to conduct analyses on migration from, to and within Africa 2. The Global Bilateral Migration Database (GBMD), which was released by the World Bank, contains bilateral migration population ('stock') data for 226 countries, major territories and dependencies for each decade from 1960 to 2000  (Özden, Parsons, Schiff & Walmsley, 2011). This database is based on census data and population register records (when census data were not available). While the release of this database has drastically increased the potential to assess long-term migration trends, it has some limitations.
For instance, immigration is likely to be underestimated for countries defining migration on the basis of 'citizenship' rather than 'birth' because of naturalisation. Moreover, irregular migration is generally not taken into account and if data was missing some values have been estimated. Because the 1970 data turned out to be inconsistent, we decided only use 20 year intervals  (ie, 1960, 1980 and 2000)  to study the evolution of African migration. The recently completed DEMIG C2C ('country-to-country') migration flow database (which has been part of the Determinants of International Migration project) has drastically expanded our ability to perform detailed analysis of recent patterns and trends of African migration to and from Europe, North America and Oceania. DEMIG C2C database covers bilateral migration flows for 34 reporting countries from and to a broad range of origin countries over the 1946-2011 period (see  Vezzoli, Villares-Varela, & De Haas, 2014).
This database also has its limitations. In general, migration flow data is generally less reliable than stock data and its coverage is patchier. For instance, flow data are not available for some important destination countries such as the United Kingdom. When flow data are based on population registers they are not always comparable because the registration criteria (such as duration of stay) can vary considerably across countries. This paper will use these new data sources to analyses the evolution of migration patterns within, towards and from Africa in the 1960-2010 period. The analysis of the GBMD stock data will give a global and long-term perspective of the evolution of these migrations. The analysis of the DEMIG C2C flow data will give better insights into the recent evolution of African migration to Europe, North America and Oceania, as well as patterns of diversification in terms of destination and origin countries.
To assess the level of development of African countries, world development indicators available on the World Bank website will be used (GDP, mortality rate of children under five years old, fertility rate and proportion of rural population). To estimate the restrictiveness of immigration policies of African and non-African states, the paper will draw on the DEMIG VISA database, which is a global panel of bilateral travel visa requirements covering the 1973-2013 period (De Haas & Villares-Varela, 2014 forthcoming).
General migration trends Figure  6  shows that although African migrants are still overwhelmingly located in African countries, the proportion of Africans living in (North) America and, particularly, Europe has increased. As we will see, because these are stock data, this underestimates the actual rate of increase of migration flows. The figures in Additional file 2 break these data down on the regional level and show that this increase in extra-continental has been general, although with strong regional variations. After Africa, Europe is the second continent of destination for migrants from West, East, Southern and Central Africa. Migrants from Southern Africa, however, also migrate to America and Oceania in significant numbers. Finally, the main destinations of North African migrants are Europe and Asia (Gulf countries), followed by Africa.