dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of FAAP20-UBZ with other UBDs To further investigate whether the UBZ-Ub interface resembles other UBD-Ub interface, we compared the Ub-interacting α-helix of FAAP20-UBZ with Ub-binding single α-helix motifs such as UIM (Ub-interacting motif) and MIU (motif interacting with Ub, also known as IUIM [inverted UIM]) (Fig. 6 ). Three-dimensional structures of the UIM domain of Vps27 (Vps27-UIM) (PDB code: 1Q0W) and the MIU domain of Rabex5 (Rabex5-MIU) (PDB code: 2C7M and 2FIF) have been solved in complex with Ub  [37] [38] [39]. Rabex5-MIU and FAA-P20-UBZ are orientated in the same direction, relative to the bound Ub. Remarkably, critical residues involved in Ub binding are strictly identical between Rabex5-MIU and FAAP20-UBZ. Thus, the α-helix-mediated Ub-interaction of FAAP20-UBZ is closely related to that of MIU.
Acknowledgments We thank the beam-line staffs at  NW12A ,  BL-1A ,  BL-5A  and  BL17A of Photon Factory (Tsukuba, Japan)  and  BL32XU  and  BL41XU  of  SPring8 (Hyogo, Japan ) for technical help during data collection. This work was partly carried out at the  Joint Usage/Research Center (RIRBM) ,  Hiroshima University.
Background. Childhood cancer is an emerging problem in Africa. Its extent is hazy because data are scarce, but it should be addressed. This is the first report from the South African Children's Tumour Registry (SACTR), which covers the whole of South Africa (SA). It provides minimal estimates of cancer incidence and discusses the challenges of cancer surveillance and control in a child population in a middleincome country. Only about 2% of the African population is covered by cancer registries producing comparable incidence data. Objective. To present and interpret incidence patterns and trends of childhood cancer over a 21-year period. The results should raise awareness of the problem of childhood cancer in an African population and provide sensible data for taking this problem in hand. Methods.
The South African Children's Tumour Registry During 1987 -2007, the nine POUs (Fig. 1 ) treated children with cancer and provid-ed data to the SACTR. More recently, the SACTR data sources have included two new satellite units in the Eastern Cape Province and one in KwaZulu-Natal. Each POU sends its data to the central SACTR office as electronic files or as physical records by registered post. In addition to the POU sites, data also come from several oncologists treating children with cancer in private healthcare institutions and from some private paediatricians. In the central registry, the relevant information is recorded into the electronic database of a customised CanReg 4 computer program, adapted for childhood cancer by the International Agency for Research on Cancer (IARC). Data are validated in collaboration with the data originators.
Duplicate registration is prevented by manual matching of new cases. Relapses are matched with earlier registration of the same patient and flagged. Since 2002, information on tumour site, morphology and behaviour has been coded according to the International Classification of Diseases for Oncology, 3rd edition (ICD-O-3). [6] The cases originally coded to the ICD-O-2  [7]  were recoded to the ICD-O-3 in agreement with the original medical records. The registry respects confidentiality principles at all times. Identifying information is used for data input, quality control and follow-up. In all reports only grouped data are presented.
Cancer cases The SACTR records all malignant tumours, as well as non-malignant central nervous system (CNS) tumours, occurring in children aged under 15 years of age who are resident in SA. Children are considered resident if their parents have been resident for >12 months or if they themselves have lived in SA for at least 6 months, according to the identity documents examined by designated administrative personnel. Records of all patients registered during the period 1987 -2007 included identification number, province of residence, gender, age in years, ethnic group, dates of birth and diagnosis, tumour site, histological classification, behaviour and most valid basis of diagnosis. The extracted data were verified in collaboration with the IARC and converted to the International Classification of Childhood Cancer, 3rd edition (ICCC-3).
Population data Population data were obtained from the United Nations  [9]  and from Statistics South Africa,  [1]  although these sources did not provide figures for all cross-categories of gender, age, ethnic group and province in each calendar year. We were able to extract 1 201 data cells within the study period and a further 122 from the preceding or succeeding years. We have estimated the missing population figures by linear interpolation or extrapolation of available data, using the relevant gender, age, ethnic and province categories. The derived population estimates are based on the assumptions of linear population growth within the population subcategories over the periods with unavailable data. The estimated population data used for calculating the incidence rates in this study are shown in Table  1.
Analysis of incidence rates Incidence rates are expressed as an average annual number of cases per million personyears. [10] The age-standardised average annual incidence rate (ASR) for the age range 0 -14 years is the weighted average of the age-specific incidence rates using the weights of the world standard population for the age groups 0 -4, 5 -9 and 10 -14 years. The parameters of the incidence trends were estimated using software for analysis of trends using Jointpoint Regression Program 3. 5. 4 of 2012 developed at the National Cancer Institute, Bethesda, USA. These analyses were based on the crude rate, assuming Poisson variance of counts. The joinpoints were identified using the Hudson method and their optimal num ber was determined by the permutation test.
[10] Our rates for the Asian/Indian ethnic group (ASR 79 per million) are at the lower end of the ASR range observed in India and the developing countries of South-East Asia in the 1980s or early 1990s (74 -110 per million). [10] As black African children constitute two-thirds of the total dataset, their very low registration rates impact heavily on the national incidence estimates. The low incidence rates may be partly explained by underdiagnosing, misdiagnosing or under-reporting. The diagnosis of many tumours, in particular those of the CNS, may be delayed as the appropriate technology required to confirm the diagnosis is not always available. [18] In many rural areas there is also cultural pressure that traditional healers should be consulted before modern medical services are accessed.
[1] able  4  shows the impact of exclusion of the provinces with lowest rates. There are a number of smaller centres and individual practices that are able to diagnose and treat children with cancer but do not necessarily send data to the SACTR. Although the SACCSG encourages reporting to the SACTR, the registration process remains challenging. As a consequence, the rates reported here should be considered minimal estimates of the true status. Deaths are certified in SA, and there is a central collection of death certificates. The SACTR did not attempt to link these records with the registry database, as the quality of their information has been assessed as low. [19] In countries where death data are available to cancer registries, these may help to achieve higher registration completeness, provided the causes of death are correctly assessed and coded.
Since 2011, POUs have submitted the same dataset to both the NCR and the SACTR every 3 months. The databases of both cancer registries should therefore be equally complete and include the same registered tumours. However, from recently published report  [20]  based on data from the NCR for 2000 -2006 it became evident that further efforts are required to unify the registration procedures and strengthen collaboration between the two cancer registries. Although the overall rate they reported (45. 7 per million) was very similar to ours (45. 2/million), there are differences in the incidence rates of several diagnostic groups. These differences may be caused by the use of additional data sources by the NCR, which would explain the higher incidence rates of lymphomas, epithelial neoplasms, soft-tissue sarcomas and bone tumours.
Acknowledgments. The following colleagues played a prominent role in establishing and supporting the SACTR:  Ruellyn Cockcroft , founder of the SACCSG tumour registry in 1987 -H F Verwoerd Hospital  1987 -1990 ,  Ga-Rankuwa Hospital   1990 -1996 ;  Paddy Hartley and Cyril Karabus -Red Cross War Memorial Children's Hospital ;  Richard Cohn -Johannesburg  (until 1997); Lourens de Jager -H F Verwoerd Hospital 1993 -2000, and his private patients since 2006;  East London -Sydney Smith  and  Mark Painter ;  Tygerberg Hospital -Peter Hesseling  and  Glyn Wessels ; Polokwane as satellite till 2009, thereafter independent -Oloko Wedi; Durban -Joan Naidoo until 1993 and  R Thejpal  from  1993 ;  Bloemfontein -Sydney Smith until 1990 , and all consultants and medical officers in the department from 1987 to 2007; Rina Nortje -secretary, SACTR.
• Learning analytics for Assessment and Adaptive Learning: assessment is a kind of feedback for the learner and the Mooc team about the learning process. There is a special area of data analytics, named Learning Analytics which uses specific heuristics to analyze the learning traces (productions, logs, chronicles,. ) in order to discover some indicators useful to understand the learning process and able to guide adaptation (see section 1. 2. 4 for more details). Adaptation can be made by the learner himself/herself or can be exploited by an adaptation process, considering these indicators as parts of the learner profile. • Engagement Diagnosis and Remediation: this is probably one of the key of the success of a Mooc. How to recognize very early in the course what to do to keep a high level of commitment of learners in the Mooc? How to remediate very quickly when the commitment is lower? How to involve the learner himself to gather the conditions of his/her commitment?.
CNRS Pilot in Lyon: COAT In Lyon, we tried to develop tools in order to be able to observe living Moocs. Moreover, these Moocs have been designed to give the learners the control of the observation and the ability to collaborate to the analysis process. A description of this approach is available on slideshare  12. We applied a Design Oriented Research method which is described in Figure  1. A variety of assistance modules have been developed as well and are the target of near future scientific and technical contributions. The general tools for observing and analyzing interaction traces is TRACE-ME  13  and SAMOTRACE-ME  14. These tools used the general framework kTBS  15. Figure  2  gives a general idea of the learner experience of his/her own trace. 2. About the use of Webwork, SageCell and IPython notebook for teaching mathematics and computational science, Marc Buffat.
With this experience, we start in 2014 a project of a MOOC on scientific programming with our colleagues in the mathematics department of Lyon 1, University Joseph Fourier,Paris Sud and INRIA. The goal of this MOOC InProS  [10]  "Introduction à la programmation scientifique" is to teach a methodology for scientific programming, based on problem solving using Python and IPython notebook and on-line exercises using SageCell and Webwork.
Python, Sage and SageCell Historically in the mechanical engineering department, we used the numerical computing environment Matlab to teach numerical methods, because of its ease of use for beginners and the large amount of available toolboxes. However, Matlab is not a real programming language and students tended to learn only how to write Matlab script using black-box libraries functions instead of learning scientific programing. In recent years, we start to use "Scientific Python" to teach scientific computing. Indeed Python is a beautiful programming language, with great readibility that makes it a good choice as a first programming language. With the scientific libraries (Nympy, Scipy and Matplotlib), it is a great alternative to Matlab to teach numerical methods. To allow our students to use Python without installing the "Scientific Python stack" on their computer, we use a Sage web-server (http://sage-meca.
Ipython notebook The IPython Notebook  [11]  is a web-based interactive computational environment where you can combine Python code execution, text, mathematics using L A T E X, plots and rich media into a single document. It is already used as a medium of instruction for courses in numerical methods or scientific computing as seen in the recent SciPy and PyconFR conferences  [12, 13]. In the MOOC InProS, we will used IPython Notebook as the main course material. Our main objective is to replace PowerPoint like presentation by a more dynamical presentation using live coding and demonstration. Furthermore, in order to have more engaging video, we combine screencast of the notebook using handwritten font and Khan academy style tutorial (i. e. using hand annotation with a digital tablet) (see Figure  4 ).
Introduction Most EPFL Professors heard the first time about MOOCs in August 2012: Daphne Koller, Professor at the Stanford Artificial Intelligence Lab and co-founder of coursera, gave a talk to present MOOCs to the yearly meeting between EPFL Professors and the Direction. After the meeting, our President Patrick Aebischer, whois extremely enthousiastic about MOOCs, asked us: "Who would like to make a MOOC?" I thought to myself that my Numerical Analysis course to 400 non-mathematician Bachelor students would be a good candidate. However, I also thought that transforming it into a MOOC would be an enormous work. Thus, I did not raise my hand. You cannot always do what you want, the Direction asked me to make a MOOC, and I will describe hereafter how I did it. The first EPFL MOOC was given by Martin Odersky, the designer of the Scala programming language, in September 2012.
The old Numerical Analysis course This Numerical Analysis course, in its classic version, corresponds to the teaching book  [15]. The course is tought at EPFL at Bachelor level to first years students in physics, second year students in mechanics, third year students in computer science. The course corresponds to a 14 weeks semester, a chapter being presented each week. Numerical tools are presented the first 7 weeks: interpolation, difference formula, quadrature, linear systems, nonlinear systems. The remaining 7 weeks correspond to solving ordinary differential equations and partial differential equations arising from physics and engineering. Each weak, the ex-cathedra course is 2 hours on the blackboard, the exercice session 1 hour in a computer lab. During the exercice session, the students fill an incomplete Matlab/octave file and run numerical experiments to confirm the theoretical predictions.
Acknowledgements Marco Picasso would like to thank the staff of the  MOOCs Factory Lab at EPFL ,  Patrick Jermann  for technical support and  Jérémy Rieder  for cutting the videos. The scientific staff is also acknowledged,  Gilles Steiner  post-doc,  Viljami Laurmaa PhD  student and  Giacomo Rosilho de Souza  master student, all in the Section of mathematics.
Population-based controls were identified among Englishspeaking male residents of CT and MA between the ages of 18-55 at the time of the interview, using random digit dialing. Controls were frequency-matched to cases sampling on the basis of age categories, and individuals with a previous history of cancer with the exception of non-melanoma skin cancer were excluded as potential controls. The study was approved by the Institutional Review Boards of Yale University, the Connecticut Department of Public Health Human Investigation Committee, the Harvard School of Public Health Human Subject Committee, the Massachusetts Department of Public Health, Dana Farber Cancer Institute, and the 28 participating hospitals in Connecticut. Data collection. All subjects included in the study completed an in-person and standardised, structured questionnaire implemented by trained study interviewers.
These metrics included age at first use, number of MBS products used, and duration of use. Polytomous logistic regression models and then hierarchical coefficients tests were used to evaluate the associations between the use of MBS and the risk of TGCC by subtypes (seminoma and non-seminoma, each vs controls). Po0. 05 was the criterion of statistical significance, and all statistical tests were two sided. Statistical analyses were conducted using Stata Version 10. 0 software (Stata, College Station, TX, USA). 1 , the cases were slightly younger and more likely to be white than the controls. The prevalence of cryptorchidism and injury to the testes or groin was higher in cases than that in controls. However, years of education, prevalence of tobacco smoking, prevalence of alcohol drinking, and height were similar in cases and controls (Table  1 ).
ACKNOWLEDGEMENTS The cooperation of 28 Connecticut hospitals, including  Charlotte Hungerford Hospital, Bridgeport Hospital, Danbury Hospital, Hartford Hospital, Middlesex Hospital, New Britain General Hospital, Bradley Memorial Hospital, Yale/New Haven Hospital, St. Francis Hospital and Medical Center, St. Mary's Hospital, Hospital of St. Raphael, St. Vincent's Medical Center, Stamford Hospital, William W. Backus Hospital, Windham Hospital ,  Eastern Connecticut Health Network, Griffin Hospital, Bristol Hospital, Johnson Memorial Hospital, Day Kimball Hospital, Greenwich Hospital, Lawrence and Memorial Hospital, Milford Hospital, New Milford Hospital, Norwalk Hospital, MidState Medical Center, John Dempsey Hospital and Waterbury Hospital , in allowing patient access, is gratefully acknowledged.
Results The search results for category A (B) are presented in Table  3 (4  and 5 ). Figures  4  and 5  show the comparison of data and the predicted backgrounds as a function of S T , τ lepton p T , and jet multiplicity parameters. The dashed curves show the expectation for LQ signals. For the comparison of expected and observed number of events in Tables  3 4 5 and Figs. 4 5 , Z-scores are used. These are computed taking into account the total uncertainty in the mean number of expected events. A unit Z-score, |Z| = 1, refers to a two-tailed 1-standard deviation quantile (∼68%) of the normal distribution. For each selection, the observed number of events is found to be in an overall agreement with the SM-only hypothesis and the distributions reveal no statistically significant deviations from the SM expectations.
Input and output The main objective of the simulation program is to establish a rapid but global diagnostic of air conditioners and of the building envelope. The program also helps to determine the size of the system needed by considering the premise's thermal loads and evaluating the optimum cooling power required. This program is based on the EnergyPlus simulation code  [17] , which provides the simulation core. A graphical interface allows the rapid entry of input variables as well as the display of the generated output. Since the simulation program is pitched at auditing premises with a small air conditioning unit such as a split system type, the building studied is considered as a single thermal zone. The software architecture is as follows: A user interface for entering parameters to define the building and the system characteristics (Fig  1 ).
The calculation of the electrical power introduces also the coefficients K1 and K2, i. e. calculated req cool req elec Eurovent sys SEER K K Q Q EER K EER 2 1 , , 2 calculated annual cool annual elec SEER K K Q Q 2 1 , , (3) (4) (5) The annual electrical energy is calculated from the annual cooling energy delivered in the thermal zone and the SEER calculated value determined by EnergyPlus using the formula  (5). The size or capacity of the system is evaluated by comparing the actual cooling capacity installed with the required cooling capacity assessed by the software. A difference lower than 30% between these two values provides access to the label A. The classification proposed in table 3 has been elaborated empirically for this project considering the available power range of RAC on the market.
The K2 study Participants included patients who were above 18 years of age and diagnosed with kidney cancer between 2007 and 2012 in one of 6 participating centres in Czech Republic, 1 center in Romania, and 1 center in Russia. We gave participants a standardized face-to-face short lifestyle questionnaire covering socio-demographic characteristics, anthropometric measures, medical history, family history, and tobacco and alcohol use. Clinical and pathological data were abstracted from medical charts and pathological reports. A majority of participants underwent nephrectomy and the tumor was histologically confirmed. Follow-up for outcome (relapse, vital status, and cause of death where relevant) was performed every 6 to 12 months after diagnosis, using passive follow-up methods where possible (with confirmation of vital status through active follow-up methods in case of uncertainties), and active follow-up methods when no linkage to databases was possible.
Statistical analysis We used Cox proportional hazards models with time since diagnosis (recruitment) as the time scale to estimate hazard ratios (HR) and 95% confidence intervals (CI) for all-cause mortality by categories of circulating vitamin B6. Categories were defined by splitting B6 concentrations at quartiles of its distribution within the randomly selected subcohort. HRs for cause-specific mortality were calculated in a competing risks model using the data augmentation method of Lunn and McNeil  [11]. To account for the case-cohort design  [9]  we used Barlow's method to weight the likelihood and computed robust variance estimates, slightly adjusted to account for the fact that we included all cases with stage IV disease (i. e. , all stage IV cases received a weight of 1). All models included stage, age at diagnosis, and sex as covariates, with the baseline hazard stratified by country of recruitment.
Acknowledgments A grant from the  World Cancer Research Fund (UK)  funded the biochemical analyses for this study. This work was also supported by a grant from the  NCI  ( U01-CA155309 ), and the  Ministry of Health Czech Republic-DRO  (MMCI,  00209805 ). The work carried out by DCM was undertaken during the tenure of an  IARC-Australia Postdoctoral Fellowship  funded by  Cancer Council Australia. The study sponsors had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. The authors are entirely independent from the funders. All authors had full access to all of the statistical reports and tables and take responsibility for the integrity of the data and the accuracy of the data analysis.
A grant from the  World Cancer Research Fund (UK , http://www. wcrf. org/)n°2010/254 funded the biochemical analyses for this study. This work was also supported by a grant from the  NCI  ( U01-CA155309 , http://www. cancer. gov/),and the  Ministry of Health Czech Republic -DRO (MMCI ,  00209805 , http://www. mzcr. cz/En/). The work carried out by DCM was undertaken during the tenure of an  IARC-Australia Postdoctoral Fellowship  funded by  Cancer Council Australia  (http://training. iarc. fr/en/fellowships/iarcaustralia_postdoc. php). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Methodology During 2010-2012, a total of 168 Mycobacterium tuberculosis Complex (MTBC) isolates were collected by cluster sampling from 10 different Moroccan cities, and centralized by the National Reference Laboratory of Tuberculosis over the study period. All isolates were genotyped using spoligotyping, and a subset of 75 was genotyped using 24-locus based MIRU-VNTR typing, followed by first line drug susceptibility testing. Corresponding strain lineages were predicted using MIRU-VNTRplus database.