dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset preprocessing Before performing our study, the dataset went through steps of anonymization and sanitization to minimize the privacy risks associated with the collected data and remove unwanted records. Dataset anonymization. We focused on attributes corresponding to identifiers (Stable device addresses, device names, etc. ) and on temporal data (timestamps). The following transformations were applied: • Device addresses: the Stable BD_ADDR (Public and Random Static) have been pseudonymized through keyed-hashing 2  of the 24-bit lowest part (NIC), thus leaving the 24-bit highest part (OUI) unmodified. • Names of persons: fields potentially containing names were sanitized by searching and removing substrings that were matching the pattern of a name ("*. 's. *|*. 's. *", "*. de. *") or strings from a dictionary 3  of names.
Among the 1300 distinct device names of our dataset, we identified and redacted 142 names related to physical persons from 4287 advertisement records accounting for 0. 05% of the dataset. • Timestamps: the temporal information has been transformed from absolute (date and time) to relative (time elapsed since the beginning of the collection campaign). Nevertheless, in order to be able to conduct the analysis of the distribution of addresses (Section 5. 2), we kept the list of raw Random Static addresses that were stripped from any other information. Dataset sanitization. BLE beacons are static devices deployed in the physical space to enable localization services. Thus, they are of little interest in the context of this study as they are not associated to an individual. The records associated to beacons have been filtered out the dataset based on regular expressions used to identify the main beacons standards  4.
Adoption of the LE Privacy feature As described in Section 2. 2, device addresses can be of 4 types: Public, Random Static (Stable category), Random Non-resolvable and Random Resolvable (Private category). Table  1  shows the distribution of BD_ADDR and records among the device address types in our dataset. In term of volume, we observed that Private addresses (Random Resolvable and Random Non-resolvable) account for about 60% of the advertisement packets found in our dataset. This shows that the privacy-preserving mechanism of BLE is widely adopted by the industry. This also highlights that a number of devices are still using Stable addresses. In fact, our dataset includes more than 5500 Stable device addresses that can be trivially used to track the corresponding users  [7, 8, 11]. Moreover, we observed that 80% of those Stable identifiers are of type Public, thus revealing the globally unique MAC address assigned by the manufacturer to the device.
AD types distribution Advertising data can contain a variety of AD types. In our dataset, we found that advertising payloads contain 2 distinct AD structures in average. The distribution of those AD types is highly concentrated as the six most common types (presented in Table  2 ) that are included in more than 80% of the advertisement packets in our dataset. Indeed, we computed that 6. 3M advertisement records include at least one of those six types. Among those most common values, we can observe types used to identify (Complete Local Name) or to describe the device and its capabilities (Appearance and Flags). Other types are dedicated to the advertisement of services (Complete List of 16-bit Service Class UUIDs) and transmission of data (Manufacturer Specific Data and Service Data-16-bit UUID).
Uniformity of random addresses distribution According to the Bluetooth Core Specification [24, Vol 2, Part H, sec. 2], random addresses should be generated using a FIPS compliant PRNG and thus should exhibit some characteristics expected from those PRNGs. In particular, it is expected that the output of those PRNGs follows a uniform distribution. We used the random addresses found within our dataset and tested whether this set of random values followed a uniform distribution 6. For each random address found within our dataset, we extracted the part that has been generated with a PRNG 7 and submitted those values to the Kolmogorov-Smirnov (KS) statistical test  [5]. The KS test is used to test whether one distribution of values differs substantially from theoretical expectations. It outputs the distance (the statistic) between the empirical distribution and the reference distribution as well as a significance score (the p-value) that gives the probability of obtaining this distance if the values were drawn from the reference distribution  [13].
2. 3]. Using a BLE-enabled laptop running Microsoft Windows 10 left idled, we found that the lifetime of the BD_ADDR is around 16 minutes while the Device Hash lasts for approximately 60 minutes (see Figure  9 ). First, we can observe that Random Non-resolvable addresses used to advertise Microsoft CDP data last longer than the Bluetooth Core Specification recommended duration of 15 minutes. Then, the switch to a new Device Hash is not synchronized with the change of BD_ADDR. In fact, the Microsoft CDP advertising data undermine the address randomization scheme through the  exposure of an identifier whose lifetime overlaps the one of the BD_ADDR. From the dataset, we computed that more than 89% of Random Non-resolvable addresses follow the format of the data shown in Figure  8. Leveraging the Device Type field found within the Microsoft CDP data (see Figure  8 ), we found that 96% of such Random Nonresolvable addresses are broadcasted from Microsoft Windows 10 computers (desktops and laptops).
For instance, this is the case of the company IDs 9 that are included in the Manufacturer Specific Data AD structure. Like company IDs, members UUIDs 10 are 16-bit identifiers assigned by the Bluetooth SIG. Such members UUIDs can be included in the Service Data-16-bit UUID and Complete List of 16-bit Service Class UUIDs AD structures. The presence of those indicators suggests that the device features a service that has been designed by the corresponding company. Indeed, for the sake of compatibility, it is possible that a service is implemented by other companies than the one that has designed it. However, we found that, in practice, those identifiers are generally included by devices of the associated manufacturer. In addition, some companies can design their own 16-bit UUIDs. This information is not publicly available but can be obtained analyzing the advertisement traffic generated by the devices.
Similarly, the 128-bit service UUIDs can be generated by manufacturers and can be leveraged as well. For instance, the adab09ad-6e7d-4601-bda2-bffaa68956ba custom 128-bit service UUID broadcasted by Fitbit fitness trackers is associated with the Fitbit manufacturer. From our dataset, we computed that more than 78% of Stable and 95% of Private addresses are associated with at least one company identifier (company ID or members UUID) in the advertising payload, indicating that the manufacturer could be identified. 9 https://www. bluetooth. com/specifications/assigned-numbers/company-identifiers 10 https://www. bluetooth. com/specifications/assigned-numbers/16-bit-uuids-formembers.
Device type Public BD_ADDR:. As previously observed, the manufacturer of a device can be derived from its Public BD_ADDR. Nevertheless, when this manufacturer is focused on a single class of products, it becomes possible to infer the type of the device from the Public address. For instance, the OUI associated with Nintendo and OculusVR respectively reveal a video game platform and a virtual reality headset. Service UUIDs. Service UUIDs are processed by Central to discover services offered by nearby Peripheral before establishing a connection. Some service UUIDs assigned by the Bluetooth SIG reveal particular characteristics of a device that can lead to the inference of its type. For instance, looking at the online Bluetooth SIG database  11  of 16-bit service UUIDs, we can infer that devices which broadcast the Running Speed and Cadence service UUID (0x1814) are likely fitness trackers while those that advertise the Insulin Delivery service UUID (0x183a) are healthcare devices.
Appearance and Class of Device. The Bluetooth Core Specification defines two AD types, Appearance 15  and Class of Device  16  , that are designed to carry information about the nature of a device and more specifically its external appearance. For instance, the Appearance code 0x00c1 disclose a sport watch, while the Class of Device code 0x022808 indicates a toy vehicle. More worrisome, certain codes indicate specific medical devices: Appearance codes such as 0x0d00 and 0x0d48 respectively betray a glucose monitor and an insulin pen, while Class of Device codes such as 0x02292c and 0x022930 respectively denote a medication monitor system and a knee prosthesis. In our dataset, we found that more than 5. 3% of Public and 13. 3% of Random Static addresses include either an Appearance or a Class of Device AD structure in their advertising data.
Identity of the owner The Complete Local Name and Shortened Local Name AD structures carry a textual description of the device that can be often customized by users. As a result, a number of those device names include the identity of the owner under the form of a full name, a firstname, a lastname or a nickname. For instance, we found that Bose headsets and Apple smartphones broadcast such device names that include the name of the owner: LE-Bose Alice and Bob's iPhone. During our dataset anonymization process (see Section 3. 3), we identified and subsequently anonymized a total of 10. 9% of the 1300 distinct device names advertised from 0. 4% of the addresses of the dataset.
Finally, we go beyond tracking concerns showing how advertising data can leak private information. Martin et al. showed  [17]  that the MAC address of 802. 11 devices can reveal the type of the device in addition of its manufacturer. Our study shows that the BLE advertising payload can be used to this aim. In their blog posts  [14, 15] , Lester and Stone pointed out that some elements contained in BLE advertisement packets can be leveraged to identify the nature of the device. We complement those observations presenting a comprehensive analysis of the AD structures that can expose the model and the type of a device. Several tools for parsing the content of BLE advertising data have been produced by the community. In particular, the advlib  [12]  JavaScript library and the RaMBLE  [14]  Android mobile application are capable of decomposing and interpreting a number of AD structures.
Implementation and Instances Our algorithms were implemented in C language and executed on a 3. 20-GHz Intel(R) Core(TM) i5-3470 CPU computer with 8 GB of RAM, running under Ubuntu. A set of HHC instances was designed by Bredström and Rönnqvist  [17]. These instances include most of the particular features of the VRPTW-SP studied here: time windows, synchronization, and preferences. They were specifically extended to fit the additional problem specifications related to the different types of services offered to and requested by customers. The benchmark was grouped into three different categories according to the number of customers and vehicles, which were equal to 18 clients with 4 vehicles in the first group (G1), 45 clients with 10 vehicles in the second group (G2), and 73 clients with 16 vehicles in the third one (G3).
, 2016). Since their result was obtained using a TGTC consensus as ARFbs definition, we repeated our analysis with TGTC (Supplemental Figure  2A ). We still validated our result suggesting O'Malley et al. likely confused ER and IR. The MP graph (Figure  2B ) suggests a periodicity of overrepresented distances every 10 bp, a hypothesis we confirmed by extending the distance window, revealing this trend for MP but not for ARF2 (Supplemental Figure  2B ). Modelling of DR5 and IR13 protein/DNA complexes structures based on ARF1 crystallographic data (PDB entry 4LDX) clearly illustrates that these configurations are incompatible with the dimerization mode described for ER7 and could involve a different dimerization interface (Figure  2D ).
PWM models reveal preferred ARFbs configurations in auxin regulated genes We next tested the PWM models on in vivo data. ChIP-seq data are available for ARF6 and ARF3  (Oh et al. , 2014; Simonini et al. , 2017). However, no obvious ARFbs could be identified in any of these datasets. Testing ARFbs monomeric or dimeric models yielded a very poor AUROC value (0. 61 for ARF6 and 0. 58 for ARF3) suggesting that these data might not be adequate to evaluate our model. We also used the auxin responsive genes datasets derived from a meta-analysis of 22 microarray data (see Methods). We defined 4 groups of regions of either auxin induced or repressed genes with high or very high confidence (very-high confidence: 153 up regulated genes, 36 down regulated; high confidence: 741 up regulated, 515 down regulated, Supplemental File).
Bio-informatic analyses The TAIR10 version of Arabidopsis genome was used throughout the analyses. The DAP-seq peaks were downloaded from http://neomorph. salk. edu/PlantCistromeDB. We sorted the peaks (200 bp) extracted from narrowPeaks file accordingly to their Qvalue. An ARF2-bound region was considered to overlap with an MP-bound region if the overlap exceeded 100 bp. We used the Bedtools suite to assess the overlaps and retrieve genome sequences. The PWM were generated using MEME Suite 4. 12. 0  (Bailey et al. , 2009)  on the 600 top peaks of ARF2 and MP according to the Q-value. ROC-AUC analysis: performing a ROC analysis requires a background set of unbound genomic regions. This set was built with a Python script that takes a bed file of bound genomic regions as input and randomly selects in the Arabidopsis genome regions of same size, similar GC content and with similar origin (intron, exon or intergenic).
A region was scored positive when containing at least one site. For the analysis of auxin regulated promoters, we used 1500 bp upstream of the first exon of each gene. All DAP-seq regions overlapping with the promoters were then selected for analyses. The major scripts used are available on github: https://github. com/Bioinfo-LPCV-RDF. The frequency matrices used to infer PWM can be downloaded on https://github. com/Bioinfo-LPCV-RDF/Scores.
Selection of auxin regulated genes We selected auxin regulated genes over twenty-two publicly available gene expression profiling datasets from the GEO database (Supplemental File 1). The datasets were generated on seedlings or roots of A. thaliana with different auxin concentrations and times of exposure to auxin (explored in  Zemlyanskaya et al. , 2016). Differentially expressed genes were defined as those expressed at least 1. 5 times higher (lower) after auxin treatment comparing to control, with FDR adjusted p-value < 0. 05 (Welch t-test with Benjamini-Hochberg correction). We compiled four groups of auxin-regulated genes: induced or repressed genes with high or very high confidence (Supplemental File 1). High confidence genes: 741 auxin up-regulated and 515 down-regulated genes significantly (more than 1.
FUNDING This work was supported by the  Agence Nationale de la Recherche  [ ANR-12-BSV6-0005  Auxiflo to RD, TV, FP], a PhD fellowships from the  University Grenoble Alpes  [RAM], the  Grenoble Alliance for Cell and Structural Biology  [ ANR-10-LABX-49-01  to FP, RD, AS],  Russian State Budget  [ 0324-2018-0019  to VM] and  Russian Foundation for Basic Research  [ 18-04-01130  to VM].
Study Area The study area is the "Tirone Alto Vesuvio Nature Reserve" within the Vesuvio National Park (Figure  1 ), which covers a total area of 8482 hectares and suffers a strong anthropic pressure: almost 400000 inhabitants live in the 13 municipalities around the protected area. The Park, is characterized by a large plantation forest: while the Northern slope is covered by tall deciduous trees, the Southern side presents a large plantation dominated by P. pinaster Aiton and P. pinea L, with the presence of Pinus halepensis Mill, Pinus nigra Arnold  (Picariello et al. , 2000)  and scattered patches of typical Mediterranean macchia vegetation. The study area shows the typical Mediterranean climate, with hot and dry summers and rainy winters with mild temperatures. In the last 40 years the average temperatures have gradually increased from 14 to 16 • C and the rainfall has been very erratic: some years  (1980, 1984, 1996, 2005, 2009, 2010)  have been characterized by heavy rains, others have been extremely dry  (1977, 1999, 2003, 2012, 2016, 2017)  (The Royal Netherlands Meteorological Institute, KNMI database Climate Explorer 1  ,  Trouet and Van Oldenborgh, 2013  and Supplementary Figure  S1 , data acquired on January 2018).
Sampling and Processing of Tree Cores In December 2017, for each study site, 15 dominant trees (trees with crowns extending above the general level of the main canopy and receiving full light from above and partly from the sides-  Adams et al. , 1994)  were sampled with a 5 mm increment borer (Haglöfs, Långsele, Sweden), and from each individual two cores were collected, at 1. 3 m above ground, at east and west direction. The total of 120 collected cores were fixed on specific wooden supports and, subsequently, subjected to a sanding process, to facilitate tree-ring identification. The measurement of the treering growth was carried out through the LINTAB system: a stereo-microscope connected to a computer, in which a specific software called TSAP-Win is installed and allows to elaborate a series of representative curves of each individual plant growth trend.
After measuring each sample, first a visual comparison, then a statistical synchronization of the curves, known as crossdating, was performed using the Gleichaeufigkeit index (GLK), which evaluates the correlation between the different series  (Eckstein and Bauch, 1969). Successively, the data were statistically analyzed using the COFECHA software, which allowed checking the quality of crossdating, providing indications on the number of years to add or subtract to the chronologies. Finally, the elementary chronologies have been standardized using ARSTAN  (Cook and Holmes, 1986). Standard chronologies were detrended using the smoothing spline function, with a 40-year step for the chronologies of MSS site and a 10-year step for those relating to the HSS, LSS, and CS sites. The mean chronology was calculated through an arithmetic mean, while the stabilize variance was determined through the Keith Briffa rbar-weighted method.
Climate Analysis Climate data of minimum, maximum, and mean monthly temperature and total monthly precipitation for the period 1975-2017 were downloaded from the CRU TS3. 23 gridded dataset at 0. 5 • resolution data  (Harris et al. , 2014)  since  Zalloni et al. (2018)  demonstrated for the same area the high significant correlation with local meteo stations. A Pearson's linear correlation function analysis (P < 0. 05), performed using Excel, was implemented between climate data and treering width and isotope data. Temperature and precipitation data were seasonally grouped from December of the previous year to February of the next year, in order to cover all the season which could influence tree-ring growth in Mediterranean species  (Cherubini et al. , 2003; Balzano et al. , 2018). Those relationships help clarify the role of climate, namely temperature and precipitation, on past tree growth and to understand how much it influenced tree growth in CS site, not affected by fire.
S1  and S2 ). The substrate-free structure showed no difference when superimposed to the PRPP-Mg 2ϩbound structure, with an RMSD (root mean square deviation) of 0. 20 Å (Fig. 2B ). However, part of the flexible loop (amino acids 103-106) was missing in both subunits. After PRPP hydrolysis, a phosphate ion, identified by a clear electron density (Fig. 2C  and Note S1), remains in the active site and interacts with the conserved Ala-131-TGGT-PRPP-binding motif (Figs. 2C  and S1 ). A network of water molecules replaced the ribose and pyrophosphate moieties and interacts with the conserved Arg-67, Asp-127, Asp-128, Ala-131 and Gly-133 residues (Fig. 2C ). Five of these water molecules (called a, b, c, d, e) were located in close vicinity of the six oxygen atoms coordinating the magnesium ion in the PRPP-Mg 2ϩ -hAPRT structures (PDB IDs: 6FCH, 6FCI, and 6FD4) (Fig.
3A ). Nonreactivity of hAPRT toward Hx could relate to Hx chemical properties or unbinding of Hx to the active site. In both cases, Hx may not be able to induce the structural modifications of hAPRT necessary to form the active conformation, as observed in our ADE-PRPP-Mg 2ϩ -hAPRT structure where the hood and the flexible loop undergo major changes upon ADE binding  (6). To understand the nonreactivity of Hx, we determined the structure of hAPRT crystallized in the presence of PRPP, magnesium ion and hypoxanthine at 1. 90 Å resolution (Table  2 ). We observed, as for ADE, that both the hood and the flexible loop regions cap the active site where Hx is bound (Fig. 3B ). The structure of the Hx-PRPP-Mg 2ϩ -hAPRT complex was identical to that of the ADE-PRPP-Mg 2ϩ -hAPRT complex (PDB ID: 6FCI) with a RMSD of 0.
6 Å resolution. The structures were then compared with the natural AMP-hAPRT complex (PDB ID: 6FCL). The electron densities in the active sites were readily attributable to IMP and GMP (Fig. 4, A  and B ). Both structures were similar overall and superimposable to the substrate-free, PRPPbound hAPRT or AMP-hAPRT structures with an RMSD between 0. 17 and 0. 31 Å. The conformation of IMP was, however, different from that of AMP (Fig. 4A  and Fig. S3 ). Even though the interactions with the phosphate and the sugar moieties were conserved, the base was differently positioned. Specifically, with AMP, the N1 atom faces the main chain NH group of Arg-27 and presents its lone electron pair to form a hydrogen bond of 3. 0 Å (Fig. S3 ). In the IMP complex, the N1 atom is at a hydrogen bond distance of 2.
Molecular basis of hAPRT catalysis in forward and reverse directions The forward reaction catalyzed by hAPRT can occur via either an S N 1 or an S N 2 displacement mechanism  (12, 13, 15). According to an S N 1-type reaction mechanism, an oxocarbenium intermediate must exist and be located on the C1Ј-O4Ј atoms of PRPP. It is a short-lived, solvent-sensitive intermediate that can be stabilized by an electron donor moiety  (16). In the ADE-PRPP-hAPRT structure (PDB ID: 6FCI), we noticed that the hydroxyl group of Tyr-105 is the only electron donor group in close vicinity to the C1Ј-O4Ј atoms of PRPP (6). This hydroxyl group, which is 3. 4 Å and 3. 8 Å away from the N9 and C1Ј atoms, respectively, also contributes to their isolation from solvent molecules (Fig. 5A ). Moreover, it is hydrogen-bonded to the PRPP pyrophosphate moiety (Fig.
6 Å. These short distances support an S N 2-type reaction  (10). To discriminate between these two possible reaction paths, we applied a quantum mechanics (QM) and molecular mechanics (MM) protocol. We first placed the ADE-PRPP-Mg 2ϩ -hAPRT structure (PDB ID: 6FCI) in a minimum on its potential energy surface. To be exhaustive, we investigated four possible scenarios to identify the lowest energy path linking the reactants (ADE and PRPP) and the expected products (AMP and PP i ) in the forward reaction (Fig. 5B ). We first assumed that the mechanism proceeded via an S N 1 pathway by dissociating the C1Ј-O1Ј bond of PRPP to form a stable intermediate state made of three distinct species: the purine base, a phosphoribosyl oxocarbenium derivative, and a pyrophosphate. The nucleophilic attack by the purine N9 atom position on the C1Ј position of the phosphoribosyl carbocation is the second step of this mechanism.