dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, studies of the incidence of cancer in childhood in Africa are even more difficult than those of adults. Because of the relative rarity of cancer in this age group, population-based studies must involve rather large populations (or long time periods) in order that sufficient cases can be assembled to permit calculation of valid rates. This is the major reason that there are so few population-based studies. Pooled results, for the period 2001-2010, from six of the registries appearing in this monograph, from the International Incidence of Childhood Cancer, Volume III, have recently been published  [3] , and these data, plus those from more restricted periods, have appeared online (http://iicc. iarc. fr/results/registries. php). Older data were published in IICC-2  [4] , and in a section of the monograph Cancer in Africa, also published by IARC  [5].
In this volume, we have therefore obtained data from functioning population-based cancer registries in sub-Saharan Africa, all of which, as members of the African Cancer Registry Network have been evaluated as achieving at least 70% coverage of their target population (i. e. identifying 70% of the actual incident cancers overall). The original objective was to include data from those registries that could provide at least 150 paediatric cancer cases, in a recent period of no longer that 10 years duration. At the time, the study began, 15 cancer registries met this criterion, and agreed to participate. One smaller registry (Eastern Cape, Rep. of South Africa) was also included. Table  4  lists the participants, with the total number of childhood cancers contributed by each one. The editors hoped that the data in this volume will help to highlight (or confirm) some of the interesting features of the epidemiology of paediatric cancer in sub-Saharan Africa and act as a stimulus to further research, as well as drawing attention to the needs of this important, but neglected, group of patients.
Processing of the data A listing of individual anonymous cases with the following variables was extracted for each contributor: Cases with an age at diagnosis (in years) between 0 and 14 were abstracted. All data had been coded according to the ICD-O3  [6]. They were processed by the IARC software packages DEPedits and IARCcrgTools  [7]  for verification. Since almost all contributing registries used the CanReg system, a software program developed at IARC and designed for population-based cancer registries, the data had already been submitted to the same edits as those performed by the IARCcrgTools programs. This simplified and speeded up the data validation process. From the ICD-O3 codes, each case was classified according to the third revision of the International Classification of Childhood Cancer (ICCC-3)  [8].
Summary tables The tables which appear in Chapter The age-standardised rate (ASR) is calculated, using the age-specific rates (r 1 , r 2 , r 3 ) and the world standard population, for the three age groups 0-4, 5-9, and 10-14: ASR = [(r 1 × 12000) + (r 2 × 10000) + (r 3 × 9000)]/31000. The standard errors of the ASR and cumulative rate are calculated as in Boyle & Parkin (1991)  [9]. For comparative purposes, the results for four other populations are shown. They are as follows: • USA (18 cancer registries of the SEER programme) white children The results are for 2003-2007, and the relevant data were extracted from the database of the SEER program (https://seer. cancer. gov/ analysis/incidence. html), and for the United Kingdom and France, from the database of 'Cancer Incidence in Five Continents, Volume X'  [10].
Data quality There are few indicators of data quality that are appropriate to evaluating the completeness and validity of registration of childhood cancers. Civil registration of deaths (by cause) is little developed in sub-Saharan Africa, so that the ratio of deaths in the population to cases registered cannot be used. In 'Cancer Incidence in Five Continents'  [10] , the possibility of underenumeration (and duplicate registration) in this age range is investigated by comparing incidence rates within the childhood age groups with the corresponding values from the previous volume (Volume IX) (Table  5. 3). The rationale is that the incidence rates of cancer (all types combined) within children (i. e. within the age groups 0-4 years, 5-9 years, and 10-14 years) tend to exhibit much less variability than do the incidence rates of cancer in adults.
The percentage of cases for which the diagnosis is based only on information from a death certificate (DCO%) also appears as an indicator of data validity in cancer incidence in five continents. Unfortunately, this has less widespread application as a comparative measure in Africa, since several registries do not have access to death certificates, or decline to register unverified diagnoses of 'cancer', having observed the poor accuracy of cause of death statements. In Table  5     The main sources of information for the registry are as follows: 1. Hospitals that include departments of radiotherapy, pathology, haematology, paediatrics, gynaecology, surgery, internal medicine, etc. 2. Medical treatment clinics 3. Diagnostic laboratories with pathology and haematology services. www. ecancer.
Years presented Data for the five-year period 2011-2013 are presented in the following table. The registry was founded in 1989 and is based in the Clinical Research and Epidemiology Unit of Felix Guyon Hospital and financed by the regional health agency (ARS Réunion -Mayotte). The registry covers the whole population of Reunion, which is one of four French overseas departments. The population was estimated to be around 850,000 in 2014. It is a mixed and cosmopolitan population (European, African, Malagasy, and Indian origin mainly). Since 2005, registration has been extended to paediatric cancer cases (age at diagnosis < 18 years old) diagnosed in Mayotte, a small French island. In 2012, year of the last census, the population of Mayotte was estimated to be around 215,000 inhabitants (with 50% aged less than 18).
Registration is active and it is facilitated by the fact that data requested are largely computerised at a lot of levels. ACCESS® software customised locally is used for data entry and analysis. Case finding is an active process. The registry collects all cancer cases that are diagnosed or referred to Moi Teaching and Referral Hospital (MTRH), the second largest hospital in Kenya, receiving patients from the western part of Kenya and beyond. In addition to MTRH, the registry collects data from all government, private hospitals and cancer centres, as well as the Eldoret Hospice. The sources used include the medical records departments, radiotherapy units, haematology and histopathology laboratories, outpatient clinics, medical wards, imaging units and autopsy reports. The ECR also reaches out to clinics of private physicians to collect cases that may have been missed at hospital level.
The ECR has a link with the civil registration office to access cancer-specific mortality data from death certificates. Cases not already in the registry database are traced back to the hospital where the death occurred; those cases that cannot be traced are registered as 'Death Certificate Only (DCO)' cases. This results in a substantial percentage of cases being registered as DCO  (31, or 11% in 2007-2011) , most of which  (17)  were simply specified as 'cancer'. The Moi Teaching and Referral Hospital's Oncology Division, has a vibrant paediatric oncology unit that is responsible for the treatment and management of paediatrics between the ages 0-14 years. The registry uses CanReg 5 for data management and analysis.
5 Malawi National Cancer Registry The registry, established in 1989, was initially a histopathology-based registry (recording data on cancer cases diagnosed in the pathology laboratory in Queen Elizabeth's Hospital (QEH), Blantyre), but in 1993, it became population based, recording cases from all hospitals and clinics serving the population of Blantyre District (Urban and Rural), however diagnosed. The population of Blantyre District (Blantyre city and some rural areas within it) was slightly more than one million in 2008. The population is widely mixed with various ethnic groups from the whole country, with probably no specific tribe dominating. There is also a significant population of immigrants from neighbouring countries (Tanzania, Zambia, and Mozambique) and small numbers of Zimbabweans, Asians (especially from India), and Europeans.
There are specialised treatment services for paediatric oncology available in the Uganda Cancer Institute. Data management is carried out using CANREG software. The paediatric oncology unit (POU) at Parirenyatwa Hospital is the main referral centre for children with malignancies. There is reasonable access to chemotherapy drugs with major chemotherapy drugs being available regularly and consistently to over 80% of children with cancer. The drugs are a donation by a charitable non -governmental organisation. Children also have access to imaging, laboratory services including flow cytometry for leukaemias. However, there are no facilities for bone marrow transplant. Surgery for children with cancer is performed by a dedicated paediatric surgical unit at Harare Central Hospital. After operations, the patients are sent back to the POU for the rest of the management.
Children who require radiotherapy are referred to the radio-oncology unit, which although caters mainly for adults has facilities also for children. Death certificates of people who die in the greater Harare and the dormitory town of Chitungwiza are scrutinised weekly to record those that have died of malignant disease. Deaths apparently from cancer, which cannot be found in clinical records, are registered as 'Death Certificate Only' (DCO). Some 6% of registrations of cancer in childhood in 2003-2012 were DCO cases. The CANREG system is used for data entry and management. The CanReg4 software is used for data entry and management. The South African Paediatric Registry commenced in 1987 as an initiative of the South African Children's Cancer Study Group (SACCSG). Paediatric oncology units in the country collaborate, and data also come from several oncologists and interested parties in private practice.
There are specialised cancer treatment services in CHU de Point G (medical oncology and the only paediatric oncology unit in Mali) and Hôpital du Mali, which houses a service of radiotherapy. There is a specialist Haematology Clinic in the CHU of Point G. There are four laboratories de pathology. The main one is located in Point G with four qualified pathologists, which does the great bulk of the work in Bamako (and nationally). The laboratory at Hôpital du Mali deals with some of the specimens from that hospital. A few clinicians send specimens to overseas labs. All deaths must be certified at the civil registry in order to receive a burial permit. The registry uses death certificates as a source of information. Deaths apparently from cancer are matched with the registry database. It is difficult to follow back unmatched cases to hospitals: they are therefore registered as DCO cases.
No cause of death is recorded on death certificates in Niger. There is not a specific service of Paediatric Oncology. Children with cancer are referred to medical oncologists, surgical oncologists, or haematology oncologists as appropriate. The registration process is carried out using the CANREG-4 software. Residents are defined as persons who have been living in the relevant area for at least one year. The registry collects cases from 11 local governments within Ibadan and its environs. Apart from these, the registry also receives cases from out of town facilities like the Baptist Medical Centre, Ogbomosho (Ogbomosho North local Government Area), and the Ladoke Akintola University of Technology Teaching Hospital, Oshogbo. Decreasingly lower numbers still come from the Obafemi Awolowo University Teaching Hospital, Ile-Ife (Ife North Local Government area) and many of the state and privately-owned hospitals.
Gambia National Cancer Registry The Gambia National Cancer Registry (GNCR) was initially established in 1986 as part of the Gambia Hepatitis Intervention Study (GHIS) to record data on the pattern of cancer occurrence in The Gambia. This collaborative project, involving IARC, The Gambia government and the UK Medical Research Council, was initially supported by the Government of Italy through its Ministry of Foreign Affairs and the Swedish Medical Research Council. The registry is wholly funded by IARC through the Gambia Hepatitis Intervention Study. According to the 2010 revision of the World Population Prospects, the total population of The Gambia was 1,728,000. The main source of the data is from four Government hospitals, the main ones being the EFSTH in Banjul, the AFPRC hospital in Farafenni on the north bank, Bwiam hospital in the West Coast region, and Bansang Hospital in the eastern region of the country which serves mainly a rural population.
These hospitals are the major referral centres for the various government dispensaries and health centres, which are evenly located around the country. They provide general medical and laboratory services. The MRC clinic in Fajara is also a major referral centre, with three out-reach stations in rural areas; this institution has a broadbased laboratory research facility, and since 2011 has been the national referral centre for all patients with suspected liver disease. Data is also obtained from the Prevention of Liver Fibrosis and Cancer in Africa (PROLIFICA) project database, a 5-year EU-funded study involving The Gambia and 2 other West African countries. A Cuban haematologist has recently set up a clinic for paediatric haematology. Registration of death is incomplete in The Gambia. Copies of certificates mentioning cancer are obtained from the registration office.
'Death Certificate Only' cases (those for which no diagnostic information could be found from any other source) are not included in the database. Case finding is entirely active, by trained register officer. They visit the clinical services where is expected to generate cancer cases, and collect the information onto a standard registration form. They cross check these against the ward and the central medical records, in addition to registers of admissions/discharges. Data entry and management uses the CanReg-5 software.
Retinoblastoma In most developed countries, the cumulative incidence of retinoblastoma is 50-80 per million and accounts for 2. 5-4% of all childhood cancers  [4]. It is predominantly a tumour of early childhood, though the proportion of cases occurring among infants aged under one year is rather less than for neuroblastoma. In the United States, incidence among blacks (67 per million) is somewhat higher than among whites (62 per million) (Table  8. 5). The data from Africa (Table  8. 5) suggest that, in many of the populations of sub-Saharan Africa, rates are rather higher than in Europe and the United States, with several countries having rates greater than 100 per million. High relative frequencies of retinoblastomas have been reported in several clinical or pathology series from Africa (although the latter are often restricted to solid tumours).
Soft-tissue sarcomas In Africa, incidence rates of rhabdomyosarcoma (Table  8. 9. 1, Figure  8. 9. 1a), although based on small numbers, show considerable variability, with relatively high rates in some centres, notably in East Africa. Data from the period preceding the AIDS epidemic in sub-Saharan Africa give an indication of the incidence of childhood Kaposi sarcoma attributable to the endemic form of the disease. In Kampala (Uganda), the cumulative rate (per million) in 1960-1971 was 42. 7, while in Bulawayo (Zimbabwe) in 1963-1977, it was 29. 6 per 10 6. With the advent of the epidemic of HIV-AIDS, very large increases in the incidence of KS were noted among children in East and Central Africa. In Kampala during 1993-1997, the rate was 771 per million, in Blantyre (Malawi)  (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) , it was 181 and among African residents of Harare (Zimbabwe) during 1990-1997, it was 147 per million  [5].
supported financially through The  (INCTR) Challenge Fund , a registered UK charity (The Challenge Fund in turn receives donations designated to support cancer registry activities in low-and middle-income countries 1. For this project, in particular, the support of the  South African Medical Research Council , via a Funding Agreement (March 2015) is gratefully acknowledged. Within  IARC ,  Mr Sebastien Antoni  of the Section of  Cancer Surveillance  provided support in the organisation of the data from the cancer registries in the African Network of Cancer Registries database, from which the tables were prepared. We would particularly like to thank  Dr Ian Magrath ,  President of the International Network of Cancer treatment and Research (INCTR) , and  Dr Lorenzo Leoncini ,  Department of Medical Biotechnology, University.
The data collected in the hospital-based registry are: demographic information (name, age, sex, and address); tumour type, localisation, and stage; histological type; treatment and outcome; and yearly follow-up results. Data should include the date and cause of death, if the patient died. There are a few satellite units in Limpopo (Northern part of the country) and Eastern Cape (East London and Port Elizabeth) Until April 2011, the Registry was the main source of statistical data for epidemiological studies in this field. An alternative source of cancer information was the National Cancer Registry, a pathology-based registry of the National Health Laboratory Service, established in 1986. Data for a five-year period 2008-2012 are presented in the following table. ecancer 2017, 11:755 Years presented Map of the catchment area served by the cancer registry.
All data were re-gridded onto a regular 1°Â 1°grid using linear nearest-neighbour interpolation. In the case of depth-resolved dissolved oxygen concentrations, monthly output is not a standard CMIP5 output. Data were therefore sourced from the model progenitors directly; however, output could not be obtained for HadGEM2-CC, HadGEM2-ES or IPSL-CM5B-LR models and so only nine models were used for the monthly oxygen analysis. Preindustrial control run output was detrended, if necessary, using a linear regression prior to further analysis. For each year of model output, the annual maxima (for SST) and minima (pH, thermocline oxygen and PP) were extracted. Supplementary Note 3 and Supplementary Fig. 10  contain a comparison of model output with observations. Regression analysis. A generalized least-squares model with a first-order autoregressive error term (AR(1)) was applied to time series of the annual maxima (SST) or minima (other variables) using R package 'nlme'  72  : Y t ¼ m þ ot þ N t ; ð1Þ where Y t is the annual extreme in the variable, m is a constant term (the intercept), t is the linear trend function (here time in years), o is the magnitude of the trend (the slope) and N t is the unexplained portion of the data, which is modelled as an AR(1) process.
Approximately 5-30% of pixels fail one of the tests and, for most models and variables, these pixels are predominantly in the Arctic. Assuming a normal distribution for trends in annual extrema is reasonable given the low percentage (5-30%) of pixels where the assumption is not met. Where the assumptions are not met, the ToE criterion will not necessarily indicate that the trend exceeds 95% of the noise values, and may instead be lower. The ToE is calculated for each individual model and the median year for each pixel is then used as the ensemble mean. The inter-model difference in ToE is expressed as ± 1 s. d. The pace of climate change is described here by the difference (in years) between the ToE and the start of the climate change signal. All analyses were performed in Matlab (Mathworks).
Acknowledgements We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Supplementary Table 1 of this paper) for producing and making available their model output. This work was supported by  Natural Environment Research Council National Capability  funding to S. A. H. Contributions of T. I. and R. S. were supported by  EU   H2020  project  CRESCENDO  (grant  641816 ). Contribution of J. L. S. was supported by  BP  under the  Princeton Carbon Mitigation Initiative. Contribution of JT was supported by  Bjerknes Centre (BIGCHANGE)  and  RCN  (no. 239965 ).
RT-PCR analysis Total RNA was extracted from each sample with an RNeasy mini-kit (Qiagen), with a final elution in 30μl of RNase free distilled water. RNA integrity, purity and concentration (RIN values) were quantified using a BioAnalyser (Agilent) and only extractions with a RIN value of > = 8 were retained for further analysis. First strand cDNA syntheses were conducted with 1-3μg RNA and 1μl of 150ng/μl N6 primer in a total volume of 20μl using Superscript RT II (Invitrogen) following the manufacturer's instructions. We searched for transcripts of our genes of interest in quail and chicken on Genbank. Where possible, we used existing primers documented in other studies. Alternatively spliced transcripts and/or coding regions were amplified using published primers and primers designed from available quail and chicken gene sequences from Genbank for ASIP, AGRP, POMC, PCSK1 and PCSK2 (Table A in S1 File).
Several novel ASIP transcripts were found, and these were fully sequenced using new internal primers (Table A in S1 File). For POMC, we used published chicken alternatively spliced transcripts  [5]  and unpublished quail transcripts from Genbank (accession number AB620012. 1, AB620012. 1, AB620013. 1). All of the isoforms expressed in this study contained a unique non-coding exon and primers were designed to be isoform specific. Where available quail coding sequences were relatively short in comparison to the chicken, we designed primers using chicken transcripts to ensure increased sequence coverage. Reverse transcription polymerase chain reaction (RT-PCR) was carried out on cDNA derived from the ventral and dorsal surface of each embryo (as described above) to examine which transcripts of our genes of interest were expressed in each stage of development.
RT-PCR was performed in 25μl total reactions containing 1. 0 unit BIOTAQ polymerase (Bioline) 1. 5mM MgCl 2 , 0. 2μl 50mM dNTP, 1μl 10μM each primer and 1μl of first strand synthesis product. PCR reactions were performed in a DNA Engine (MJ Research, Watertown, MA), with the following cycling parameters: Heated lid 110˚C, 94˚C for 2 mins. ; 40 x 94˚C for 30 secs. , 55-64˚C for 30-60 secs. , 72˚C for 1 min; 72˚C for 5 mins. As described previously, primers were designed to be isoform specific resulting in a single band in our RT-PCR experiments. Sequencing was performed with Sanger sequencing on both strands using the PCR primers. New sequences were assembled and edited using the SeqMan software (DNASTAR Inc. , Madison WI, USA). Alignments were performed in Mega 6. 06  [36]. We used the housekeeping gene β-actin as a positive control for all RT-PCR reactions.
Acknowledgments We would like to thank all members of the  Baker lab  for their extensive technical support and the  Bronner lab  for the gift of the SOX10 probe as well as  Marie Manceau  and  Barry Denholm  for comments on this manuscript and  Nicolas Haupaix  for additional testing of our ASIP and AGRP probe. This research was supported by a Cambridge Commonwealth, European and  International scholarship  to  T-LG  (www. cambridgetrust. org/about/cambridge-overseastrust),and  Murray Edwards College, Cambridge  to NIM (http://www. murrayedwards. cam. ac. uk).