dataesr/scientific-paragraphs-categorization huggingface dataset:
Subjects This observational, single-center, prospective study was designed in accordance with the tenets of the Declaration of Helsinki. Data collection and analysis was approved by the Ethics Committee of the Swiss Federal Department of Health (CER-VD no. 19/15). All patients signed an informed consent. Consecutive patients presenting at Jules-Gonin Eye Hospital (Lausanne, Switzerland) with a first episode of acute, unilateral, and treatment-naive CSCR from January 1, 2014 to October 31, 2015 were included. A CSCR episode was defined as the association of visual symptoms (vision impairment, metamorphopsia, micropsia, dyschromatopsia or central scotoma) in the presence of subretinal fluid on spectral-domain optical coherence tomography (SD-OCT) with a leaking site on fluorescein angiography (FA) and choroidal vascular hyperpermeability on indocyanine green angiography (ICGA).
Fluorescein Expansion Ratio The intensity of leakage at the previously identified leakage sites on FA was estimated by quantifying the relative expansion of hyperfluorescence from early phase (40-60 seconds) to midphase (2-2. 5 minutes). Angiograms were exported as TIFF files and were processed on Matlab using a semiautomated custom algorithm adapted from the method described by Pryds et al. 19  The leakage site was indicated manually, and the borders of the hyperfluorescent area were automatically detected using the grayscale intensity threshold of 0. 75 • Imax, where Imax is the maximal fluorescence intensity at the leakage site. The ratio between hyperfluorescent areas at midphase and early phase was calculated to provide the fluorescein leakage ratio.
Subretinal Fluid Volume The volume of subretinal fluid was calculated at each timepoint using a custom-built algorithm on Matlab (version 2015b; Mathworks, Natick, MA). Briefly, the 97 SD-OCT scans corresponding to the macular volume were exported as PNG files, and the borders of the serous retinal detachment were segmented on each scan using an intensity-based method. After visual verification of the segmentation, the volume was obtained by trapezoidal integration, and a heat map of the subretinal detachment was generated. The kinetics of SRF resorption was then analyzed in each patient by comparing SRF volumes at each timepoint.
Statistical Analyses Survival analyses were performed using the R software (version 3. 1. 3; R Foundation for Statistical Computing, R Core Team, 2015, Vienna, Austria. http://www. R-project. org/). The Kaplan-Meier method with log-rank tests was used for univariate analyses, and the Cox proportional hazard method for the multivariate analysis, with the "survival" 20 package. Results were expressed in terms of hazard ratio and adjusted hazard ratio, respectively. Parameters resulting in a P value #0. 2 in the univariate analysis were entered in the multivariate model, followed by stepwise regression with the "MASS" 21 package. Survival curves were generated with the "ggplot"  22  and "survminer" packages. For each investigated parameter, a dichotomizing value was searched that defined 2 groups with a significant difference in episode resolution rate, under the condition that the smallest group was formed by $11 patients (onethird of the study population).
INTRODUCTION Active immunotherapy of Alzheimer's disease (AD) has been under study for its ability to elicit anti-amyloid-beta (Aβ) antibodies, induce Aβ clearance, and reduce Aβ accumulation in the brain  [1]. Active immunotherapy also has the potential to be cost-effective for long-term therapy  [2, 3]. Development of the first therapeutic vaccine for AD, AN1792, a full-length Aβ peptide (Aβ 1-42 ), was discontinued because of meningoencephalitis, attributed to T-cell activation  [4] [5] [6]. Clinical benefit was suggested, however, in anti-body responders to AN1792  [5]. ACC-001 (vanutide cridificar), a next-generation therapeutic vaccine consisting of Nterminal (Aβ 1-7 ) peptides conjugated to a carrier protein, was designed to minimize potentially harmful T-cell responses  [6]. This paper focuses on the safety of ACC-001 + QS-21 adjuvant in 3 long-term extensions of phase 2a studies of ACC-001 + QS-21 in subjects with mild to moderate AD conducted in the United States (parent study Clinicaltrials.
Study Design and Analysis The phase 2a, multicenter, long-term extension studies assessing safety, tolerability, and immunogenicity of ACC-001 + QS-21 adjuvant in subjects with mild to moderate AD were either third-party unblinded (EU/US studies) or open-label (Japan study). The studies were registered on Clinicaltrials. gov (EU ID: NCT00955409; US ID: NCT00960531; Japan ID: NCT01238991) and were conducted in compliance with ethical principles originating in or derived from the Declaration of Helsinki. Informed consent was obtained from all subjects or legal representatives and caregivers prior to enrollment. Results of the EU/US studies were analyzed together as planned in the study statistical analysis plans, and results from the Japan study were analyzed separately in a similar way. Descriptive summary statistics are provided for all endpoints in the analysis.
ACKNOWLEDGEMENTS M. Hüll was PI (European study branch); he collected and interpreted data, drafted and revised the manuscript, and read and approved the final manuscript. C. Sadowsky  performed research/study, collected and analyzed data, and drafted and revised the manuscript. H. Arai  designed and performed the study, and drafted and revised the manuscript. G. Le Prince Leterme  designed the protocol, interpreted data, generated final study reports, drafted and revised the manuscript, and approved the final manuscript. A. Holstein  designed the protocol, interpreted data, generated final study reports, drafted and revised the manuscript, and approved the final manuscript. K. Booth  participated in study coordination and conduct, served as medical lead for the 1007/8 studies, interpreted data, drafted and revised the manuscript, and read and approved the final manuscript.
Study design (Figure 1) This study was an observational, open-label, cohortstudy conducted in accordance with the principles of the Helsinki Declaration. We prospectively and systematically recorded data from the patients and analyzed them retrospectively. During a first phase, called adaptation period, the injector (DR) used a follow-the-pain approach in order to determine the optimal injection scheme for each individual. The possible injection sites were the corrugator, temporalis, and trapezius muscles. Patients were systematically asked about the usual topography and time course of migraine attacks, and the existence of pain or stiffness of the cervical muscles. Examination searched for muscle tenderness and the existence of myofascial trigger points (TrPs). The decision to inject a single muscle relied on data from questioning and examination.
ACKNOWLEDGMENTS A. B. K. acknowledges discussions with  E. A. Jagla ,  A. Rosso  and  L. Foini. This work was partly supported by the  Argentinian  projects  PIP11220120100250CO  ( CON-ICET ), and  UNCuyo  Grants No. 06/C490  and  06/C017. This work was also partly supported by the  Labex NanoSaclay   ANR-10-LABX-0035. S. B. , N. C. , J. C. and V. J. acknowledge support by the  France-Argentina  project  ECOS-Sud  No. A12E03. R. D. P. thanks the  Mexican council CONACyT  for the PhD fellowship No. :  449563.
Objective To explore how the results from the 2014 dual antiplatelet therapy (DAPT) trial were disseminated to the scientific community and online media. Design A a systematic review of scholarly and public attention surrounding the DAPT study. settings Data were collected from the ISI Web of Knowledge, Google Scholar, PubMed Commons, EurekAlert, the DAPT study website (www. daptstudy. org) and the New England Journal of Medicine website (for scholarly attention) and Altmetric Explorer, Snap Bird, YouTube (for public attention) citing DAPT study results appearing from 16 November 2014 to 10 June 2015. Participants No participants were involved in this study. Main outcome measure Proportion of contents highlighting the increased risk of mortality and critical to the author's interpretation of the results.
Public attention We searched Altmetric Explorer  [12] [13] [14] [15]  to identify all online attention (news, blogs, Twitter, Facebook, Google+, Mendeley, CiteULike) given to the DAPT study. Each identified social media source was then systematically evaluated to determine whether other posts were not captured by Altmetric Explorer. In addition, each original tweet was reviewed to find retweets, replies and favourites. Since Altmetric. com captures only tweets attached to the Digital Object Identifier (DOI) of the original DAPT article, we also used snapbird. org, a search engine that can search an individual Twitter account by using the NEJM's Twitter account and the search terms 'DAPT' and 'dual antiplatelet therapy'. We also searched EurekAlert! (a free online database for science press releases, www.
25 In addition, two independent researchers assessed each source by using a standardised data extraction form and disagreements were resolved by consensus. However, our study has some limitations. First, this study focused on only a specific trial publication and results are not generalisable to other studies. However, the article we focused on was among the top five of all research outputs and within the 99th percentile of articles on Altmetric. Second, the data extraction involved some subjectivity; however, we tried to address this by using a standardised data extraction form and independent assessment as well as consensus among two researchers. Third, despite our best efforts, we cannot ensure that our search strategy was all-encompassing because of the breadth of social media. Finally, we did not explore the balance between efficacy and safety outcomes with DAPT treatment.
Acknowledgements We thank  Elise Diard  for help in creating figure 2. We acknowledge the support from  Altmetric  for free access to 'Altmetric Explorer'. We acknowledge the assistance in English language proofreading by  Laura Smales  ( BioMedEditing, Toronto, Canada ). Isabelle Boutron  and  Philippe Ravaud  submitted a letter to the NEJM following the publication of the DAPT study to highlight the inadequate reporting in the abstract conclusions, but the letter was rejected. contributors Study conception, design, selection of contents and data extraction: MS, RH. Study conception and design: MS, RH, IB. Selection of contents, data extraction: MS, RH. Analysis of data and interpretation of results: RH, PR, IB. Contributed to the writing of the manuscript: MS, RH, PR, IB. All authors read and approved the final manuscript.
There is a lack of prescribed databases and approaches in place for performing comparable Life Cycle Assessments (LCAs) of smartphones and other electronic devices in a coherent manner. Hence there is a need within certain eco-rating initiatives for simplified, yet still precise enough, approaches that are expert independent. Here, five independently published Full LCAs (FLCA) of smartphones-and a metal content declaration of a tablet-are analyzed and compared with the simplified LCA method (Open Eco Rating LCA, OLCA) used by the open eco rating (OER) sustainability assessment. OLCA is described in detail. The comparisons use the same characterization factors that are used for climate change and abiotic resource depletion (ARD) midpoint impact categories. The tablet is only analyzed for the ARD indicator (ARDI).
In this context, the French Environment and Energy Management Agency (ADEME) set up a web platform, Base IMPACTS ®  [29] , being the official database of the French program for the environmental labeling of consumer goods. For mobile phones GWPI and ARDI were selected as they were deemed to be the most relevant, reliable and implementable by companies. OLCA uses the midpoint indicators for CC and ARD as prescribed by the International Life Data System 2011 Midpoint+ version 1. 08 (ILCD)  [30]. The similarities between OLCA and the SENSE tool for the food industry are obvious as SENSE is "a web-based tool which allows the environmental impact calculation for a food product in a simplified way"  [31]. OLCA is similarly intended to give a very good indication of the FLCA GWPI and ARDI scores of mobile phones.
Objectives This study researches to which degree those FLCA approaches with higher resolution and flexibility-using state-of-the-art LCA tools, ISO and ETSI standardized frameworks, primary LCI data and state-of-the art secondary LCI databases such as Ecoinvent and GaBi-differ from a rather simplified approach based on product metrics OLCA. The difference is studied for five smartphones on the GWPI and for a tablet and one of the smartphones on the ARDI. The tablet has been included to understand development potentials for the OLCA. It is expected that FLCA will score significantly higher than OLCA, especially on the ARDI of the tablet and one of the smartphones. The overall question of this research is: How well do GWPI and ARDI results calculated with OLCA, based on metrics for products-here specifically for smartphones and a tablet-coincide with published GWPI and ARDI results based on FLCA for the same products? An overall objective is also to show many details on how the OLCA model can be applied and developed further.
Based on the data given in  [13]  it is not possible to identify the masses of other metals used such as tin and copper. This leads to an OLCA ARDI score of 1. 22 g Sb-eq. , i. e. , 40% lower than for the FLCA of 2 g Sb-eq. [13]. Compared to FLCA it is clear that the present OLCA has room for improvement of the ARDI precision. If cobalt and lithium were to be included in the OLCA ARDI, the score for Phone A would become 1. 68 g eq. -i. e. , 16% lower than for the FLCA. Phone B uses a different indicator for ARD, CML2001 instead of than ILCD. Moreover the report  [14]  is not complete enough to derive the material contents, preventing a similar exercise as for the tablet in Section 2. 3. Therefore the FLCA ARDI cannot be compared to OLCA ARDI for Phone B.
Product Metrics and Carbon Intensities Used to Compare FLCA and OLCA Table  4  shows product metrics used to compare OLCA to FLCA. Uncertainty factors (UF) corresponding to one standard deviation in a lognormal distribution are given next to each value where applicable. UFs are based on the uncertainty range for the LCI databases used. Si area for Phone B is likely underestimated, however identifying irregularities in the FLCA reports of Phones A-E is not the target for this research. Table  5  shows CO 2 -eq. intensities used in FLCA and OLCA. Phone B comprises different system boundaries for screen and camera production than OLCA.
Rigid and flexible PCB production in Supplementary Materials). For the ICs and other PCBA parts, in order to establish a ratio between the precision obtained and the effort needed, a study was carried out for ten printed PCBAs of mobile phones, ranging from a simple feature phone to a high-end smartphone. For that study ten mobile phone electronics sub-assemblies were disassembled and for each one the following items were assessed: -Total surface of silicon dies (Si area ) in ICs. Stacked dies (e. g. , in NAND memory) and wafer level chip scale package-WL-CSP also known as flip chips-were also taken into account, -Total mass of IC and total IC packaging surface. The different types of packaging-e. g. , ball grid array, dual/quad flat no-lead, small outline package-were also considered thanks to specific models available in the databases of the LCA tool Environmental Impact Made Easy (EIME©)  [35].
Previous use of EIME has been reported by Ma et al. [41]   Within this paper these components and processes will be noted as PCBA parts. All these data were then imported to Version 4-database Version 11. 0 of EIME© LCA tool  [35]. The PEP ecopassport ® -PCR 2. 1 method  [42]  was used to obtain environmental impact category results for CC, ARD, water depletion, primary energy depletion and ozone depletion. The environmental impact indicator results (e. g. , kg CO 2 -eq. for the GWPI for each mobile phone) and key parameters, such as Si area , total IC mass and total IC packaging surface, were then combined in a statistical analysis software  [43]  to obtain the correlation factors and regression formulae. For GWPI the Si area in ICs was determined to have the highest R 2 factor (see Figure  1 ).
9991). Moreover, the Si area is rather straight-forward to obtain from mobile phone manufacturers. For 2010 conditions for the PCBA parts GWPI calculation, Equation (9) was derived. I, Siarea + I, PCBA, oth = PCBA GWP (kg CO2 eq. ) = 0. 0137 × Siarea (mm) + 0. 3368  (9)  In 2015 this method was updated thanks to a major life cycle inventory update in EIME©. Indeed, in 2010 the only wafer model available in the EIME© database was "Wafer, from silicon; before dies slicing; at plant; FR©". That model uses a French electricity mix (≈149 g CO 2 -eq. /kWh) for manufacturing of silicon dies, whereas greenhouse gas (GHG) electricity emissions in China are ≈1111 g CO 2 -eq. /kWh  [44]. In the 2015 EIME© database three wafer models were available (France (FR), China (CN) and Europe (RER), thus allowing an update of Equation  (9).
Data on global wafer capacity for seven countries or geographic areas were gathered  [45]  (Table  6 ). For each country, region or geographical area the grid electricity mix GHG models available were compared with the EIME© wafer country models. For some countries or geographical areas, such as China or Europe, a direct match was possible as the corresponding models were available. For others, like Chinese Taipei, a mix consisting of several wafer models was created in order to close the gap (column "Wafer model selected in EIME© databases" in Table  5 ). The result is Equation (10) representing a 2015 global average for GHG emissions emitted by manufacturing of silicon wafers and remaining PCBA parts. I, Siarea + I, PCBA, oth = PCBA GWP (kg CO2 eq. ) = 0. 0202 × Siarea mm 2 + 0. 142 (10).
The devices were disassembled in Orange's laboratory followed by a metal-alloy identification step using an X-ray Fluorescence (XRF) analyzer. Data were also provided by camera manufacturers, especially for back-side illuminated (BSI) complementary metal oxide semiconductor (CMOS) sensors, which are designed with two bonded wafers (one for pixel section and another one for logic circuit). Sample teardown reports available from commercial consultants  [46]  were also used to identify the different parts and their respective functions-e. g. , voice coil motors (VCM)-used for the autofocus feature. Each camera's LCI model was modeled in the EIME© 04_2014 database according to its identified materials and manufacturing processes. In order to identify the critical parts for the GWPI the different parts of the camera were grouped into five blocks (electronics, autofocus, lens, housing and flash) (see Figure  3 ).
identify the different parts and their respective functions-e. g. , voice coil motors (VCM)-used for the autofocus feature. Each camera's LCI model was modeled in the EIME© 04_2014 database according to its identified materials and manufacturing processes. In order to identify the critical parts for the GWPI the different parts of the camera were grouped into five blocks (electronics, autofocus, lens, housing and flash) (see Figure  3 ). For front cameras (models n°1-°2) and simple rear cameras (models n°3-°6), the total GWPI results are quite similar (ranging from ≈0. 355 kg CO2-eq. for model n°2 to ≈0. 431 kg CO2-eq. for model n°6). These models share similar technology with a BSI CMOS sensor designed with a single wafer, featuring two separate sections (pixel and logic circuit). Camera models n°5 and °6 were the only ones to include an autofocus feature.
repartition is shown in Figure  4. For all the models the conclusion is that the CMOS sensor is the key driver for CO 2 -eq. emissions. The CMOS sensors account for at least 85. 8% of the module's footprint. Model n • 9 includes several ICs such as an OIS Driver, a MEMS OIS Gyroscope, an Autofocus VCM Driver and a Serial Flash Memory. For all the models the conclusion is that the CMOS sensor is the key driver for CO2-eq. emissions. The CMOS sensors account for at least 85. 8% of the module's footprint. Model n°9 includes several ICs such as an OIS Driver, a MEMS OIS Gyroscope, an Autofocus VCM Driver and a Serial Flash Memory. Eco-rating systems in general-and OER with OLCA in particular-aim to obtain the best CO2eq. precision per mobile phone for the network operators with minimum data collection effort for the phone manufacturers.
Eco-rating systems in general-and OER with OLCA in particular-aim to obtain the best CO 2 -eq. precision per mobile phone for the network operators with minimum data collection effort for the phone manufacturers. Therefore, some simplifications were carried out for the implementation of the camera model: -For front cameras (models n • 1 and n • 2) and simple rear cameras (models n • 3-• 6 and all featuring a resolution ≤5 Megapixels) a single model was created based on the GWPI scores (average value ~400 g CO 2 -eq. ) of camera models n • 1-• 6. Indeed, for these camera models the CMOS sensor sizes are of the same order of magnitude (about 24. 5 mm 2 +/-10%), -For camera models with a compact digital camera design-e. g. , featuring a large optical zoom-a single model was created (~3000 g CO 2 -eq.
), -For the remaining camera models (n • 7-• 9)-i. e. , those with a resolution >5 Megapixels and not designed as the one for compact digital cameras-a complementary assessment of 40 mobile phones was carried out. It demonstrated that, unlike cameras with resolution ≤5 Megapixels, the size of the CMOS sensors of these more complex cameras varied a lot (see Figure  5 ). For all the models the conclusion is that the CMOS sensor is the key driver for CO2-eq. emissions. The CMOS sensors account for at least 85. 8% of the module's footprint. Model n°9 includes several ICs such as an OIS Driver, a MEMS OIS Gyroscope, an Autofocus VCM Driver and a Serial Flash Memory. Eco-rating systems in general-and OER with OLCA in particular-aim to obtain the best CO2eq. precision per mobile phone for the network operators with minimum data collection effort for the phone manufacturers.
Figures 7-11 show the key results of the present paper. The differences for Screen and IC production balance each other for Phones A and B (Figures  8  and 9 ). The lack of knowledge of the Si ,area of Phone E could be an explanation for the large difference with OLCA (Figure  10 ). Figure  11  shows the difference between FLCA and OLCA for ARDI evaluation of Phone A and the tablet. It seems important to update OLCA with a question for cobalt content. Consequently, for these more complicated camera models, an average GWPI score based on models n°7-°9 would not capture all possible configurations, and the sensor surface parameter had to be introduced into the Life Cycle Inventory (LCI) model. For this purpose the CMOS sensor's size-expressed as a fraction of an inch-was used to simplify the data collection for the phone manufacturers (see Figure  6 ).
Discussion This research discusses the precision of a simplified approach-OLCA-for estimation of the carbon and antimony footprints of smartphones and tablets. On one hand individual practitioners want and need freedom to produce detailed and advanced FLCAs based on the ISO and ETSI LCA standards  [34] , but on the other hand these FLCAs should not be compared by consumers. The main reasons are that not all assumptions are transparent and might differ, and that "apples are not compared to apples" in a strict sense. While comparability is the ultimate aim of the PEF FLCA method, it will require very high data quality. Here instead OLCA is suggested for smartphones which will lead to comparability with enough data quality even before any PEF legislation has been enacted. The reason for this insight is that all assumptions are controlled within OLCA and that the FLCA and OLCA scores for GWPI and ARDI do not differ beyond recognition.