dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We gratefully acknowledge the DESY and CERN managements for their support and hospitality, and their accelerator staff for the reliable and efficient beam operation. The authors would like to thank the  RIMST (Zelenograd) group  for their help and sensors manufacturing. This work was supported by the  European Commission  under the  FP7 Research Infrastructures project AIDA , grant agreement no. 262025 ; by the  Bundesministerium für Bildung und Forschung, Germany ; by the  DFG cluster of excellence 'Origin and Structure of the Universe' of Germany ; by the  Helmholtz-Nachwuchsgruppen  grant  VH-NG-206 ; by the  BMBF , grant no. 05HS6VH1 ; by the  Alexander von Humboldt Foundation  (including Research Award IV, RUS1066839 GSA); by the  Russian Ministry of Education and Science contracts 4465.
Data variables Data variables included: age, sex, previous ART exposure, ART start date, other medication (at start and during ART), co-morbidities (at start and during ART), results of hepatitis B and C testing, height, weight, WHO clinical stage, CD4 cell count, CrCl (at start and during ART along with the date) and patient outcomes. These data were collected from an electronic FUCHIA database(Follow Up and Care for HIV Infection and AIDS)(2010 Epicentre, http:// www. epicentre. msf. org) and from patient medical files. The electronic database and patient medical files do not include patient names but patient numbers.
Acknowledgments This research was conducted through the  Structured Operational Research and Training Initiative (SORT IT ), a global partnership led by the  Special Programme for Research and Training in Tropical Diseases at the World Health Organization (WHO/TDR ). The model is based on a course developed jointly by the  International Union Against Tuberculosis and Lung Disease  ( The Union ) and (MSF). The specific SORT IT programme which resulted in this publication was jointly developed and implemented by: The  Union, South-East Asia Regional Office, New Delhi, India ;  The Centre for Operational Research, The Union, Paris, France ; the  Operational Research Unit (LUXOR) ,  Medécins Sans Frontières, Brussels Operational Center , Luxembourg; and  Burnet Institute, Melbourne, Australia.
baseline assessments Trained psychologists collected data with a standardised questionnaire during a face to face interview at the participants' home. Information included sociodemographic status, lifestyle, medical history, drug use, and assessment of cognitive and functional status. Clinical examination included measurements of blood pressure with a digital tensiometer (OMRON M4). Anthropometric measures included height and weight. Fasting bloods samples were taken and markers (such as cholesterol, glucose) measured at a single laboratory.
Magnetic resonance imaging Brain MRI scanning was undertaken on average of 4. 2 months (SD 3. 0 months) after the baseline examination. Scanning was completed with a 1. 5-Tesla Magnetom (Siemens, Erlangen, Germany). Usual MRI exclusions were applied. The scanning sequence and data extraction methods have been described in detail previously. 27 28 In brief, raw data were converted to the ACR-NEMA standard format and then transformed for analysis and storage at the Department of Neurofunctional Imaging, Caen. 25  This centre developed fully automatic image processing software for tissue segmentation and to detect and quantify white matter lesions. 27 29 utomated imaging processing was also used to study brain volume (white matter, grey matter, and ventricles). Total intracranial volume by summing grey matter, white matter, and cerebral spinal fluid volumes were computed with voxel based morphometry techniques.
All analyses were repeated with Alzheimer's disease as the outcome (sensitivity analysis). Although the sample size is large for a brain imaging study, it is relatively small for risk model testing. Therefore, instead of splitting the sample into derivation and testing datasets we ran the analyses on the entire sample. To correct for optimism bias in the C statistic value (that is, over-fitting to a specific sample), we undertook internal validation using 100 bootstrap samples. To use all the data from the 1721 participants and test whether missingness (assumed to be missing at random) influenced the results, we carried out multivariate imputation by chained equations (using the mi procedure in Stata) that included all 13 conventional predictors and the outcome variable. We created 10 imputed datasets and fitted each model separately on each.
Demographics Of the 1721 participants with complete MRI data at baseline, 119 (6. 9%) progressed to dementia over the 10 years of follow-up. The mean time to onset of dementia was 6. 7 years (SD 2. 0 years; range 1. 7-10. 5 years). People without dementia were followed for an average of 7. 4 years (SD 2. 3 years; range 0. 6-10. 6 years). Table  1  shows baseline demographic characteristics of the cohort stratified by dementia status at follow-up. The groups with and without dementia differed significantly with respect to age, education, physical function, cognitive function and apolipoprotein e4 allele status but had similar distributions of sex, smoking, alcohol, history of cardiovascular disease, diabetes, and systolic blood pressure. simple model for dementia risk with conventional risk factors When all 13 conventional risk variables were included in the model (M1), significant predictors of 10 year incident dementia included age, physical function, cognition (mini-mental state examination, Benton visual retention test, and digit span), and apolipoprotein e4 allele status (table 2).
Results from the multiple imputation analysis, however, indicated consistency in findings in the reduced (complete case) and pooled (imputed) datasets. Additional studies with larger samples are needed to determine whether small positive changes in discrimination performance and reclassification of risk would show significant results. Small significant differences in prediction, however, might not translate to improvement for the clinical setting. Finally, there is currently no consensus model for predicting risk of future dementia, and whether MRI enhances prediction in other dementia risk models requires testing. Lastly, there are discussions about the set up and utilisation of predictive models  57 58  ; further evaluation of this, however, is outside the scope of this study.
falciparum cases. As both cohorts are subject to similar meteorological conditions, we were able to analyze the relevance of different immunological mechanisms compared with climatic forcing in malaria transmission. Transmission was first modeled by using similarly unique datasets of entomological inoculation rate. A stochastic nonlinear human-mosquito model that includes rainfall and temperature covariates, drug treatment periods, and population variability is capable of simulating the complete dynamics of reported malaria cases for both villages. We found that under moderate transmission intensity climate is crucial; however, under high endemicity the development of clinical immunity buffers any effect of climate. Our models open the possibility of forecasting malaria from climate in endemic regions but only after accounting for the interaction between climate and immunity.
Continued exposure to the parasite is seemingly required to maintain such premunition  (21). Complete protection from further infections is rarely, if ever, achieved. In such high-transmission regions, the relationship between local climate and disease is difficult to disentangle. In this study, two unique long-term cohort datasets from villages separated by 5 km but with markedly different malaria transmission intensity (Fig. 1 , Upper) enable us to showcase the relative roles of internal and external factors in malaria epidemiology, assess the potential degree of predictability emanating from climatic variability, and generate estimates of key parameters in determining malaria population dynamics. To this end, we use a recently developed inference methodology for nonlinear stochastic dynamical systems, successfully applied to epidemic dynamics  (3, 16)  but never applied to endemic settings.
4e À 3). All these associations suggest that climate variability plays an important role in malaria dynamics and indicate that climate covariates should be included in the malaria models. Transmission variability was modeled in four different ways: (i) only by means of the EIR, (ii) with a seasonal flexible function [seasonal splines (Sp)], and (iii) with a combination of a seasonal flexible function and climatic covariate anomalies in two alternative ways [seasonal splines plus linear combination of temperature and rainfall (SpTR)] and SpROT (same as before plus rainfall over temperature) as explained in SI Text. Likelihoods of these transmission models for each of the drug periods are shown in Table  1  together with second-order Akaike information criterion (AIC c ) values; AIC c is a likelihood-based criterion that penalizes for higher number of parameters as well as for size of dataset  (28).
For instance, if clinical immunity decreases (e. g. , after bed net policies or insecticide campaigns), the distribution of recorded malaria cases throughout the year would change accordingly, making climate and immunity intertwined drivers of variation in incidence of malaria. Recent multimodel approaches addressed the potential expansion of malaria to currently uninfected areas by performing predictions mainly based on climate  (13). This study shows that such extrapolation is valid only along the fringes where the disease is unstable but has to be carefully addressed for endemic places where intrinsic factors such as immunity, reinfection, and asymptomatics should be taken into account to reproduce the observed temporal patterns. Our approach opens the possibility to forecast also in malaria endemic regions and could be useful for other datasets from very different epidemiological dynamics as well as for other vector-borne diseases.
Materials and Methods This program is supported by three different institutions: the Institut Pasteur (Dakar, Senegal), the Institut de Recherche pour le Développement (Marseille, France), and the Senegalese Ministry of Health and Prevention. An agreement between these institutions defines all research activities conducted in this program. The longitudinal surveys were approved by the Ministry of Health of Senegal and the assembled population of the two villages (as explained in SI Text). Written informed consent was obtained from all participants. We studied two extensive long-term epidemiological datasets of daily P. falciparum confirmed malaria cases, probably among the best malaria records worldwide, recorded for  19 y (1990-2008)  in Dielmo and for  16 y (1993-2008)  in Ndiop (see SI Text for more details)  (37).
Mosquitoes of the A. gambiae s. l. species complex are the main vectors in both Ndiop and Dielmo. However, notably, A. funestus is also present in Dielmo, largely because of the stream that provides a suitable larval habitat. The intensity of malaria transmission was monitored during the whole study period; night-time collections of mosquitoes landing on volunteers were carried out monthly and the sporozoite rate was determined. It was thus possible to estimate the EIR, i. e. , the number of infective bites per person per night, for every month of the whole period (see ref. (37). Rainfall time series from Dielmo (13,685662N, 16,38463W) and Ndiop (13,724620N, 16,409324W) come from a meteorological ground-based manually operated station in each village. Temperature was extracted from National Oceanic and Atmospheric Administration National Climate Data Center Global Hydrology and Climatology Network v2  (42)  averaged from the four nearest villages (Cap Skirring, Kaolack, Diourbel, and Ziguinchor).
ACKNOWLEDGMENTS. We thank the villagers of Dielmo and Ndiop for their participation and sustained collaboration in this project, and the field workers ( Abdoulaye Badiane ,  Dominique Manga ,  Fatou Bintou Badji ,  Gaoussou Diakhaby ,  Marie-Louise Senghor , and  Roger Ehemba ) for their contribution to the project and in generating and maintaining the malaria database. K. L. thanks  Gustavo Berman  and  Hugo Ferrari  for kindly allowing the intensive use of the computational resources at  Centro Atómico Bariloche  for part of this work. X. R. and K. L. were funded by the  European Union   FP7 Quantifying Weather  and  Climate Impacts on Health in Developing Countries. X. R. acknowledges the project  EUPORIAS  (Grant  308291 ). K. L. is currently a researcher at the  National Scientific and Technical Research Council of Argentina.
C. Neutron diffraction High-resolution neutron powder diffraction (NPD) experiments were performed at the Institut Laue-Langevin (ILL) in Grenoble, France. NPD data at room temperature were collected in the D2B diffractometer for samples Sr2% and Sr5%. A wavelength of ∼ 1. 59 Å was used, with an angular span of 150 • and a step of 0. 05 •. The collimated beam on the sample was of 32 mm × 12 mm. A NaCaAlF standard was used to calibrate the neutrons wavelength, yielding the value λ = 1. 5943±0. 0001 Å. Measurements were performed in vanadium cylinders of 6 mm diameter and 8 cm height filled with sample, with a collection time of 4 h. Data were processed with the LAMP software 17 and the diffractograms thus obtained were refined with the full-pattern analysis Rietveld method, using the program FULLPROF  18.
The extraction of the EXAFS signal and the structural refinements were performed using the ESTRA-FITEXA software package  22. Minimizations were conducted in the wave vector space (k) to avoid possible artifacts introduced by Fourier filtering. The number of free parameters in the fits never exceeded the number of independent points (N ind = (2∆k • ∆R)/π + 1). The amplitude and phase photoelectron functions were calculated ab-initio using the FEFF8 code  19. The crystallographic clusters centered on the absorption specie (Sr) used for FEFF calculations were obtained from the structure refined from neutron diffraction data, as described in the following.
The most relevant bond-lengths are summarized in Tables  II  and III  for samples Sr2% and Sr5%, together with the results of bond-valence-sum (BVS) calculations performed using the KDist software  23   The interatomic distances and BVS reported in Tables II and III also confirm that the Sr-substitution does not disrupt the scheme of charge order between Co 2+ at Co2 and Co3 sites, and the hybridized Co 3+ /Co 2+ (3d 7 L) at sites Co1 and Co4 proposed for the parent compound  7. The effects of the Sr substitution over the lattice parameters at room temperature are presented in Fig. 2 , as well as on the volume of the cell normalized per unit formula. An anisotropic distortion is observed with the increase of Srsubstitution, while the c parameter remains unchanged (within experimental uncertainty).
ACKNOWLEDGMENTS This work is part of a research project supported by  Agencia Nacional de Promoción Científica y Tecnológica (Argentina) , under grant  PICT-2011-0752 , by  CONICET  under grant  PIP 490 2012-2014  and by  Universidad Nacional de Cuyo  under SeCTyP grant  06-C456. We acknowledge Soleil synchrotron for the beamtime allocation and the  MinCyT-Soleil collaboration  project for the finantial support. We also acknowledge  ILL  and its staff for the beamtime allocation and technical assistance as well as the  MinCyT of Argentina  for funding the trip to perform the experiments. We thank  A. Zelaya  who helped in the preparation of the samples,  V. Tognoli  and  R. Benavides  at  Centro Atómico Bariloche and Ana Arauzo at Universidad de Zaragoza  for their valuable technical support.
Information generated from economic evaluation is increasingly being used to inform health resource allocation decisions globally, including in low-and middle-income countries. However, a crucial consideration for users of the information at a policy level, e. g. funding agencies, is whether the studies are comparable, provide sufficient detail to inform policy decision making, and incorporate inputs from data sources that are reliable and relevant to the context. This review was conducted to inform a methodological standardisation workstream at the Bill and Melinda Gates Foundation (BMGF) and assesses BMGF-funded costper-DALY economic evaluations in four programme areas (malaria, tuberculosis, HIV/AIDS and vaccines) in terms of variation in methodology, use of evidence, and quality of reporting.
Since 2004, BMGF has provided cumulative funding in excess of US$200 million for cost-effectiveness analysis and related activities around the world. To maximise the benefit of economic evaluation information to health policy decisions, it is essential that the studies are comparable both within and across health problems as well as properly performed and reported to effectively assist the health investment decisions that could subsequently have a large impact on the health of target populations. Limited methodological quality is reported to be a significant barrier to the effective use of economic evaluation information  [5] [6] [7]. This is of particular concern in low-and middle-income countries (LMICs) where research capacity in this field and reliable data sources are insufficient  [7, 8] , and there are few methodological guidelines for performing locally-relevant economic evaluation  [9].
Methods Scope The review included published economic evaluations undertaken in LMICs from 2000 onwards in four focus programme areas for BMGF  [10]  (vaccine, tuberculosis, HIV/AIDS, and malaria). The initial review identified all types of economic evaluation (cost-minimization; cost-effectiveness analysis; cost-utility analysis; cost-benefit analysis  [1] ) to provide an indicator of the proportion of economic evaluations that are supported by BMGF. The in-depth analyses included variation in methodology, quality of reporting, and quality of evidence used and was limited to economic evaluations that used the cost per disability adjusted life year (DALY)averted outcome measure and were funded by BMGF. The review scope was limited to costper-DALY studies as i) BMGF sought consistency with the outcome measure of existing programmes that it funds such as the Global Burden of Disease initiative; ii) a cost-utility-study focus was required as the intention of the reference case was to improve the ability of economic evaluation to inform resource allocation decisions in terms of both allocative and technical efficiency, iii) a single study type facilitated meaningful inference from within the time and resources available for the analysis.
Search Strategy The search aimed initially to identify all economic evaluations in LMIC settings relating to the four programme areas to explore the number of existing studies during the time that BMGF had been established and examine the proportion of studies funded by BMGF. These were then narrowed down further to include only BGMF-funded studies that used cost-per DALY as their most aggregated measure of outcome for the in-depth analysis. To identify these economic evaluations, systematic reviews of this type of studies were firstly retrieved. Since BMGF was established in 2000, the period for the search of published systematic reviews in MEDLINE and Centre for Reviews and Dissemination databases (CRD) were limited to 2000 to May 2013. Individual economic evaluations were then identified manually through the citations in the relevant systematic reviews.
Although the majority of studies were funded by non-BMGF organisations, BMGF was most often cited as the funding body compared to any other individual organization, except in area of TB, of which only one study was supported by the foundation. Disease/programme-  specific measures such as infection averted were the most common aggregated outcome measures, reported in 39% of studies. Death averted or life year saved were the outcome measured in 11% of studies, while DALY averted and QALY were the most aggregated outcome measures for 38% and 11% of studies respectively. Only two studies (1%) used monetary benefit (costbenefit analyses) as an outcome measure. (Fig  2 ). ICERs derived from each BMGF-funded cost-per-DALY study illustrate that almost all of the interventions in the areas of malaria, TB and HIV/AIDS represented good value for money (as defined by the World Health Organization) as the reported ICERs were below a ceiling threshold equal to Gross National Income per capita (1035 US dollars for low-income countries using World Bank classifications) [16] (Fig  3 ).
Manner of reporting and variation in methodology of cost-per-DALY studies funded by the BMGF The percentage of cost-per-DALY studies funded by the BMGF adhering to a set of reporting requirements is shown in Fig 4. Generalisability/transferability and equity considerations were the attributes most often neglected in the studies, followed by affordability, price date, method of cost adjustment for time difference between price date in source for cost data and price date in the study and method of currency conversion. Further analyses suggested that there was significant heterogeneity in methodology used in the cost-per-DALY studies funded by the BMGF. Most studies (12 out of 20 studies) were conducted using a societal perspective, followed by healthcare provider (5 studies  [18] [19] [20] [21] [22] ) and health system (1 study  [23] ) viewpoints, and two studies did not clearly state the perspective used.
However, it is noteworthy that the latest recommendation from the Global Burden of Disease research program  [43]  has changed, omitting age-weighting and discounting. • Only a few (2 out of 20) economic evaluations employed probabilistic sensitivity analysis even though it is widely recommended in resource-rich settings as the most comprehensive method of dealing with various forms of uncertainty in economic evaluation  [44]. • The findings of this review are consistent with previous reviews which found better quality of cost and resource data were used for economic evaluations in resource-limited settings compared to baseline clinical data and clinical effect sizes  [12]. This may be due to the lack of reliable administrative databases or existing costing studies prompting researchers to conduct primary cost studies.
Acknowledgments The  Health Intervention and Technology Assessment Program (HITAP)  is funded by the  Thailand Research Fund  under the senior research scholar on Health Technology Assessment ( RTA5580010 ), the  National Health Security Office , the  Health System Research Institute  and the  Bureau of Health Policy and Strategy, Ministry of Public Health. The findings, interpretations and conclusions expressed in this article do not necessarily reflect the views of the above funding agencies. HITAP's international unit was established with support from the  Thai Health-Global Link Initiative Project (TGLIP) , the  international Decision Support Initiative  (funded by the  Bill & Melinda Gates Foundation  and the  Department for International Development, UK) , and the  Rockefeller Foundation  to provide technical assistance on health intervention and technology assessment for governments of low-and middle-income countries.
Estimates from RCTs 5 Estimates from previously published economic analyses: 6 Expert opinion 9 Not clearly stated Costs 1 Cost calculations based on reliable databases or data sources conducted for specific study: same jurisdiction 2 Recently published cost calculations based on reliable databases or data course: same jurisdiction 3 Data source not known: same jurisdiction 4 Using charge (price) rather than cost when societal perspective was adopted 5 Recently published cost calculations based on reliable databases or data sources: different jurisdiction 6 Data source not known: different jurisdiction 9 Not clearly stated RCT = randomised control trial. 1 Single RCT with direct comparison between comparator therapies, measuring final outcomes 2+ Meta-analysis of RCTs with direct comparison between comparator therapies, measuring surrogate outcomes.