dataesr/scientific-paragraphs-categorization huggingface dataset:
| Statistical analyses For each registry, the proportion of cases registered based only on a death certificate-death certificate only (DCO)-and of morphologically verified (MV) cases were estimated. These proportions are used as indicators of the data quality for each registry. 14  estimated age-specific (5-year age groups) and crude incidence rates per 100 000 women by registry for the entire period and by 5-year periods. Age standardisation was performed by the direct method, using the World Standard population. 15 We also estimated the cumulative risk for each 5-year period. The cumulative risk expresses the overall risk of developing breast cancer before age 75, in the absence of competing risks of death. 16 mporal trends in the age-standardised incidence rate were examined using joinpoint regression,  38  with a maximum of three joinpoints and with the changes in the trend expressed as annual percent change (APC).
7 though the results in this article provide us with important insights to the changing breast cancer incidence rates across different African countries, the estimates from these registries are not perfect, as maintaining completeness and constancy in registration activities within Africa depends on several socioeconomic factors as well as political stability in these countries. Challenges in recorded data from the 2007 to 2009 period in Zimbabwe were documented in an earlier publication. 2 To minimise registration artefacts, we included only those registries with the most consistent registration activities, as evidenced by relatively constant numbers of registrations per year, as well as indicators such as MV% and DCO%, and excluded several long-established registries for which such consistency was uncertain.
Five of those included (Harare, Uganda, Blantyre, Seychelles and Nairobi) have appeared in the one of last four volumes of CI5. The proportion of morphologically verified cases varies across registry area, ranging from 58. 2% in Kampala Uganda to 97% in Seychelles. This could reflect differences in access to pathologic diagnostic services in different countries. Given the long time series available, and previously reported time trends of breast cancer,  4  we included data from The Gambia. The fluctuations in the incidence rates observed in the Gambia are likely due to a deficit of cases in age groups 55 to 59 and 65 to 69 years as well as the uncertainty concerning age in older women, as many more women are registered aged exactly 60 and 70 (Figure  S1 ). In addition, the registry is known to suffer incompleteness of case findingparticularly in older subjects  50  -and this appears to be an increasing problem,  26  which probably accounts for the aberrant results from this registry.
ACKNOWLEDGEMENTS We would like to acknowledge the contribution of the cancer registry staff -including the unpaid interns and volunteers -who collected the data presented here, often under difficult circumstances, and then coded and entered the data into the local databases from which the information for this article was obtained. We express our gratitude to the reviewers for their helpful feedback. W. Y. J. -F. is a  Commonwealth Scholar , funded by the  UK Government. International Agency for Research on Cancer and American Cancer Society  provided financial support for the extra data collection activities. The Commonwealth Scholarship, funded by the  UK Government , is funding W. Yvonne Joko-Fru 's PhD study at the  University of Oxford.
A Tynytag device (Gemini Data Loggers, UK) monitored the RH for all image acquisitions with a negligible error for the whole setup of 0. 3 % RH. RHs of 15, 45, 65, and 95 % were used for the different experiments. Topography AFM images were acquired using PeakForce tapping mode in air  [38] [39] [40] [41]. This operational mode benefits of the accurate control of tip-sample interaction forces measured by the bending of the AFM cantilever, preventing lateral forces due to intermittent contact with the sample. Continuous force-distance curves were executed at a constant frequency of 1 kHz enabling acquisition rates from 0. 7-0. 9 Hz. Silicon ScanAsyst air triangular levers with a nominal spring constant of 0. 4 N/m (Bruker Probes, USA) and ultrasharp tips (a nominal tip radius of curvature of 2 nm) were used for topography measurements.
Representative AFM images of 5 µm x 5 µm from each lignocellulosic film were analyzed by using the root mean square roughness parameter (Rq). The Rq parameter was measured for at least three representative AFM images that came from different areas of each film, and at least three different samples for each film were studied. The resolution of all AFM images were of 512 x 512 data points All raw images were rendered using Nanoscope analysis 1. 8, with exception to quantitative determination of the coverage of CNCs on AFM levers which has been performed with Gwyddion  [42]  software. The reduced YM was determined for all the films using the Peak Force Quantitative Nanomechanics (PFQNM) mode of the AFM. This mode displays a force-distance curve at each image pixel, and the treatment of the curves allows the measurement of a reduced YM for each pixel.
Acknowledgements This work was funded by the  Grand Est Region  and the  European FEDER Program  [grant number  D201550245 ] within  COFILI  project and a part of  ANR INTOS2  [grant number  ANR-18-CE93-0007 ]. The authors also thank the  Nano ' Mat  platform funded by the  Grand EST Region , the  DDRT Grand Est  and the  European FEDER program.
Participants The present study included 986 older men and women, aged 65-79 years, recruited within the frame of the NU-AGE project (Clinicaltrials. gov, NCT01754012) at baseline (April 2012-January 2014). A detailed description of the recruitment process and study design has been described elsewhere  [14, 15]. Participants fulfilling the frailty criteria  [16] , with a disability or overt disease at screening, were excluded. Local ethical approval was provided by the Independent Ethics Committee of the Sant'Orsola-Malpighi Hospital Bologna (Italy-03/2011/U/Sper), the National Research Ethics Committee East of England (UK-12/EE/0109), the Wageningen University Medical Ethics Committee (Netherlands-11/41 NU-AGE), and the Bioethics Committee of the Polish National Food and Nutrition Institute (Poland).
All models were adjusted by age, recruiting centre, smoking habit, fibre intake (g/day), the prevalence of MetS, and adherence to PA guidelines. Given the likely influence of protein intake on SRS, isocaloric substitution modelling of SFAs by other fat subtypes (MUFAs and PUFAs) was further stratified based on a protein intake of 1. 1 g/BW. All assumptions behind regression analyses including normality, linearity, homoscedasticity, and multicollinearity were checked. Based on our sample size, a priori power calculation revealed that small-to-moderate effect sizes were detectable in SRS with a power of >80% and alpha set to 0. 05. All analyses were conducted using SPSS, version 26.
Acknowledgments: We would like to thank all principal investigators and collaborators. We also express our gratitude to the participants and staff involved in data collection and management in NU-AGE research centres. We would like to thank the  EU   HORIZON 2020 Research and Innovation Programme (European Joint Programming Initiative "A healthy diet for a healthy life" "JPI HDHL " and the  ERA-NET co-fund HDHL-INTIMIC)  for supporting three authors (A. S. , A. N. and F. K. ).
Browser fingerprinting consists in collecting attributes from a web browser to build a browser fingerprint. In this work, we assess the adequacy of browser fingerprints as an authentication factor, on a dataset of 4, 145, 408 fingerprints composed of 216 attributes. It was collected throughout 6 months from a population of general browsers. We identify, formalize, and assess the properties for browser fingerprints to be usable and practical as an authentication factor. We notably evaluate their distinctiveness, their stability through time, their collection time, and their size in memory. We show that considering a large surface of 216 fingerprinting attributes leads to an unicity rate of 81% on a population of 1, 989, 365 browsers. Moreover, browser fingerprints are known to evolve, but we observe that between consecutive fingerprints, more than 90% of the attributes remain unchanged after nearly 6 months.
The seminal Panopticlick study  [3]  highlights the possibility to build a browser fingerprint by collecting attributes from a web browser. In addition to being widely used for web tracking purposes  [4]  (raising legal and ethical issues), browser fingerprints are used as an authentication factor in real-life. Browser fingerprints are indeed a good candidate as an authentication factor thanks to their distinctive power, their frictionless deployment (e. g. , no additional software), and their usability (no secret to remember, no additional object to possess, and no supplementary action to carry out). As a result, companies like MicroFocus 1  or SecureAuth 2  include browser fingerprints within their authentication mechanisms (see Figure  1  for an example of such mechanism). Related works.
Our contributions. We conduct the first large-scale data-centric empirical study of the fundamental properties of browser fingerprints when used as an additional authentication factor. We base our findings on an in-depth analysis of a real-life fingerprint dataset collected over 6 months, that contains 4, 145, 408 fingerprints composed of 216 attributes. We formalize, and assess on our dataset, the proper-ties necessary for paving the way to elaborate browser fingerprinting authentication mechanisms. These properties are usually used to evaluate biometric characteristics  [8]. We stress that we do not make any assumption on the inner working of the authentication mechanism, and consequently on the adversarial strategy. Our properties aim at characterizing the adequacy and the practicability of browser fingerprints, independently of their use within future authentication mechanisms.
In particular, we measure the size of browser anonymity sets through time, and show that 81% of our fingerprints are unique. Moreover, we measure the proportion of identical attributes between two observations of the fingerprint of a browser, and show that 90% of the attributes remain unchanged after nearly 6 months. Finally, we measure the collection time and the size of fingerprints. We show that on average, they weigh a dozen of kilobytes, and are collected in a few seconds. The rest of the paper is organized as follows. Section 2 presents and formalizes the properties evaluated in our analysis. Section 3 describes the analyzed dataset. Section 4 presents the experimental results. Finally, Section 5 synthesizes the results and concludes.
Distinctiveness To satisfy the distinctiveness property, the browser fingerprints should distinguish two different browsers. The distinctiveness depends on the used attributes, and on the fingerprinted browser population. The two extreme cases are every browser sharing the same fingerprint, which makes them indistinguishable from each other, and no two browsers sharing the same fingerprint, making every browser distinguishable. Our dataset entries are composed of a fingerprint, the source browser, and the time of collection in the form of a Unix timestamp in milliseconds. We denote B the domain of the unique identifiers, and T the timestamp domain. The fingerprint dataset is denoted D, and is formalized as: D = {( f , b,t) | f ∈ F, b ∈ B,t ∈ T } (1) We use the size of the browser anonymity sets to quantify the distinctiveness, as the browsers that belong to the same anonymity set are indistinguishable.
We denote S( f , D) the function that returns the browsers that provided the fingerprint f in the dataset D. It is formalized as: S( f , D) = {b ∈ B | ∀(g, b,t) ∈ D, f = g} (2) We denote A(ε, D) the function that provides the fingerprints that have an anonymity set of size ε (i. e. , that are shared by ε browsers) in the dataset D. It is formalized as: A(ε, D) = { f ∈ F | card(S( f , D)) = ε} (3) We measure the anonymity set sizes on the fingerprints currently in use by each browser, and not on their whole history. It is performed by simulating datasets composed of the last fingerprint seen for each browser at a given time. Let E τ (D) be the simulated dataset originating from D that represents the state of the fingerprints after τ days. With t τ the last timestamp of this day, we have: E τ (D) = {( f i , b j ,t k ) ∈ D | ∀( f p , b q ,t r ) ∈ D, b j = b q ,t r ≤ t k ≤ t τ } (4).
We consider the set Ω of the n used attributes. We denote f [ω] the value taken by the attribute ω for the fingerprint f. Let sim( f , g) be a simple similarity function between the fingerprints f and g, which is formalized as: sim( f , g) = 1 n ∑ ω∈Ω δ ( f [ω], g[ω]) (6) We define the function meansim(∆ , D) that provides the mean similarity of the consecutive fingerprints, for a given time range ∆ and a dataset D. It is formalized as: meansim(∆ , D) = ∑ ( f ,g)∈C(∆ ,D) sim( f , g) card(C(∆ , D)) (7).
Fingerprint Dataset To study the properties of browser fingerprints on a real-world browser population, we launched a fingerprint collection experiment. It was performed in collaboration with the authors of  [5] , and an industrial partner that controls one of the top 15 French websites according to Alexa 5. The authors of  [5]  held the 17 attributes of their previous work  [7]  and focused on web tracking, whereas we held 216 attributes and focused on web authentication.
Fingerprint Collection We designed a fingerprinting probe that collects 200 JavaScript properties and 16 HTTP header fields. We integrated the probe to two general audience web pages of our industrial partner, which subjects are political news and weather forecast. The probe collected fingerprints from December 7, 2016, to June 7, 2017. Only the visitors that consented to cookies were fingerprinted, in compliance with the European directives 2002/58/CE and 2009/136/CE in effect at the time. To differentiate browsers, we assigned them a unique identifier (UID) as a 6-months cookie. Similarly to  [3, 7] , we coped with cookie deletion by storing a one-way hash of the IP address, computed by a secure cryptographic hash function. Previous datasets were collected through dedicated websites, and are biased towards privacy-aware and technically-skilled persons  [3, 7].
Dataset Filtering and Preprocessing Given the experimental aspect of fingerprints and the scale of our collection, the raw dataset contained erroneous or irrelevant samples. We remove 70, 460 entries entries that have a wrong format (e. g. , empty or truncated data), that are duplicated, or that come from a robot. Cookies are an unreliable identification method, hence we perform a resynchronization similar to  [3]. We consider the entries that have the same (fingerprint, IP address hash) pair to come from the same browser, and assign them the same UID. Similarly to  [3] , we do not synchronize the interleaved UIDs, that are the pairs having the UID values b 1 , b 2 , then b 1 again. We replace 181, 676 UIDs with 116, 708 replacement UIDs using this method. To avoid counting multiple entries of identical fingerprints coming from the same browser, the usual way is to ignore them during collection  [3, 7].
Our probe collects the fingerprint on each visit, and to stay consistent with common methodologies we deduplicate the fingerprints afterward. For each browser, we hold the first entry that has a given fingerprint, and ignore the consecutive entries if they have this fingerprint. For example, if a browser b has the entries {( f 1 , b,t 1 ), ( f 2 , b,t 2 ), ( f 2 , b,t 3 ), ( f 1 , b,t 4 )}, we hold {( f 1 , b,t 1 ), ( f 2 , b,t 2 ), ( f 1 , b,t 4 )}. The deduplication constitutes the biggest cut in our dataset, with 2, 420, 217 entries filtered out. We extract 46 additional attributes from 9 original attributes, which are of two types. The first type consists in extracted attributes composed of parts of their original attribute, like the screen resolution that is split into the values of the width and the height.
Work Dataset The work dataset obtained after the preprocessing step contains 5, 714, 738 entries (comprising identical fingerprints for a given browser if they are interleaved), with 4, 145, 408 fingerprints (no identical fingerprint counted for the same browser), and composed of 3, 578, 196 distinct fingerprints. The fingerprints are composed of 216 original attributes and 46 extracted ones, for a total of 262 attributes. They come from 1, 989, 365 browsers, 27. 53% of which have multiple fingerprints. Table 1 presents a comparison between the dataset of Panopticlick  [3] , AmIUnique  [7] , Hiding in the Crowd  [5] , and this study.
Empirical Evaluation of Browser Fingerprints Properties In this section, we evaluate the browser fingerprints of our dataset according to the properties presented in Section 2. We show that our fingerprints offer a satisfying distinctiveness, as 81% of them are only shared by one browser. Moreover, our fingerprints are stable, as more than 90% of the attributes are expected to stay identical between two observations, even if they are separated by nearly 6 months. Our fingerprints of mobile browsers are less distinctive than the fingerprints of desktop browsers, with a unicity rate of 42% against 84%. Finally, our fingerprints do not hinder the performance as, on average, they weigh a dozen of kilobytes and take a few seconds to collect.
Distinctiveness We call unicity rate the proportion of the fingerprints that are shared by a single browser. Our fingerprints offer a satisfying distinctiveness, as they have a stable unicity rate of approximately 81% on the long run, and more than 94% of the fingerprints are shared by 8 browsers or less. However, the fingerprints of the mobile browsers are more uniform than the fingerprints of the desktop browsers, with a unicity rate of approximately 42% against 84%, on the long run. Figure  2  presents the size of the anonymity sets (AS) alongside the frequency of browser arrival for the daily-partitioned datasets. New browsers are encountered continually, but starting from the 60th day, the arrival frequency stabilizes around 5, 000 new browsers per day. Before this stabilization, we have a variable arrival frequency with some major spikes that seem to correspond to events that happened in France.
For example, the spike on the 38th day corresponds to a live political debate on TV, and the spike on the 43rd day correlates with the announcement of a cold snap. Figure  3  presents the unicity rate through the partitioned datasets for the overall, the mobile, and the desktop groups. The unicity rate is stable for the desktop browsers, with a slight increase of 1. 04 points from the 60th day to the 183th, from 84. 99% to 86. 03%. The unicity rate of the mobile browsers is lower, and it has a little decrease of 0. 29 points on the same period, from 42. 42% to 42. 13%. Fig. 3  Proportion of unique fingerprints for the overall, the mobile, and the desktop groups, through the partitioned datasets obtained after each day.
Time Resource Consumption Our probe takes several seconds to collect the attributes that compose our fingerprints. The median collection time of our fingerprints is of 2. 92 seconds. Mobile browsers take more time to provide the collected attributes, with a median collection time of 4. 44 seconds, against 2. 64 seconds for the desktop browsers. This is less than the median loading time of web pages 7  , which is of 6. 6 seconds for the desktop browsers, and of 19. 6 seconds for the mobile browsers, at the date of March 1, 2020. Figure  5  displays the cumulative distribution of the collection time of our fingerprints. We measure the collection time by the time difference between the starting of the script and the fingerprint sending. Some values take from several hours to days. They can come from a web page that is put in background or accessed after a long time.
Memory Resource Consumption Our script consumes a dozen of kilobytes per fingerprint, a size that is easily handled by the current storage and bandwidth capacities. Half of our fingerprints weigh less than 7, 550 bytes, and 99% less than 14 kilobytes. The fingerprints of the desktop browsers are heavier, with 95% of the fingerprints weighing less than 12, 082 bytes, against 8, 020 bytes for the fingerprints of mobile browsers. This is due to heavy attributes being lighter on mobile browsers, like the list of plugins that is usually empty. Figure  6  displays the cumulative distribution of the size of our fingerprints in bytes. The average fingerprint size is µ = 7, 692 bytes, and the standard deviation is σ = 2, 294. We remove 1 fingerprint from a desktop browser that is considered an outlier because of its size being greater than µ + 15 • σ.
Synthesis of Results and Conclusion In this study, we evaluate the properties offered by browser fingerprints as an additional web authentication factor, through the analysis of a large-scale real-life fingerprint dataset. We show that browser fingerprints offer a satisfying distinctiveness, as 81% of our fingerprints are only shared by one browser. Moreover, fingerprints are stable. At least 90% of our attributes are expected to stay identical between two observations of the fingerprint of a browser, even if the observations are separated by nearly 6 months. We validate that fingerprints offer a high performance, as they only weigh a dozen of kilobytes, and take a few seconds to collect. We conclude that browser fingerprints provide satisfying properties for an additional web authentication factor, and can strengthen password-based systems without a major loss of usability.
Acknowledgements We want to thank  Benoît Baudry  and  David Gross-Amblard  for their valuable comments, together with  Alexandre Garel  for his work on the experiment. This is a postprint of a contribution published in Innovative Mobile and Internet Services in Ubiquitous Computing, edited by  Leonard Barolli ,  Aneta Poniszewska-Maranda , and  Hyunhee Park , and published by Springer International Publishing. The final authenticated version is available online at: https://doi. org/10. 1007/978-3-030-50399-4_16.
A current view in the DMD field is that a combination of therapies targeting different pathological mechanisms may ultimately be most beneficial for patients  (Guiraud and Davies, 2017; Hoffman, 2019). Histone deacetylase inhibitors (HDACi) are one class of small molecules that has shown promise for DMD in the mdx mouse, in dmd zebrafish, and in DMD clinical trials  (Bettica et al. , 2016; Johnson et al. , 2013; Minetti et al. , 2006; ClinicalTrials. gov NCT02851797, NCT03373968; reviewed in Consalvi et al. , 2014). HDACi are examples of "epigenetic drugs", small molecules that target chromatin modifications and transcriptional regulators. Histone acetylation, which is often linked with open chromatin and active transcription, is one example of an epigenetic modification and is the target of many HDACi  (Kouzarides, 2007).
Furthermore, cloud physical parameters as cloud phase, cloud optical depth, and cloud top temperature are used as pixel-wise classifiers. A classification scheme is set up to search for the DNI variability class with a best agreement between these classifiers and the pre-existing knowledge on the characteristics of the cloud field within each variability class from the reference data base. Up to 55 % of all DNI variability class members are identified in the same class as in the reference data base. And up to 92 % cases are identified correctly if the neighbouring class is counted as success as well -the latter is a common approach in classifying natural structures showing no clear distinction between classes as in our case of temporal variability. Such a DNI variability classification method allows comparisons of different project sites in a statistical and automatic manner e.