dataesr/scientific-paragraphs-categorization huggingface dataset:
Other measurements were performed on stalks mounted (vertically) onto the goniometer of an Oxford Instruments Gemini Xray diffractometer, equipped with a Sapphire 3 CCD detector. The sample to detector distance was 45 mm. Small-Angle X-ray Scattering. For the solutions, experiments were performed on ID02 at the ESRF, Grenoble France. Samples were flowed through a quartz capillary. After the sample was injected in the capillary and reached the X-ray beam, the flow was stopped during the SAXS data acquisition. The sample-detector distance was 2m (data were also obtained at 10 m). Data processing (background subtraction, radial averaging) was performed using the dedicated beamline software or SAXSUtilities (http://www. sztucki. de/SAXSutilities/). For the solid state Tyr 5 -PEO6k-Tyr 5 sample, measurements were performed on BM26B (DUBBLE) at the ESRF (Grenoble, France).
Time-resolved DSC, SAXS, and WAXS measurements were performed. 38, 39 The sample-to-SAXS detector distance was ca. 1476 mm using a wavelength of 1. 033 Å. A Dectris-Pilatus 1 M detector with a resolution of 981 × 1043 pixels and a pixel size of 172 × 172 μm was used to acquire the 2D SAXS scattering patterns. Standard corrections for sample absorption and background subtraction were performed. The data were normalized to the intensity of the incident beam (in order to correct for primary beam intensity fluctuations) and were corrected for absorption and background scattering. Diffraction from silver behenate was used to calibrate the wavevector scale of the scattering curve. WAXS patterns were collected using a Pilatus 300 K-W detector (linear, 254 mm × 33. 5 mm active area) with a pixel size of 172 μm 2.
75 Pa. The angular frequency range was 0. 1-622. 8 rad s -1 , although data are only presented up to 50 rad s -1 due to device inertia at high frequency. The viscosity of the sample was measured performing varying stress experiments in the range 0. 0056 to 16880 s -1. Polarized Optical Microscopy. Images were obtained with an Olympus BX41 polarized microscope by placing the sample between crossed polarizers. Films were prepared by drying 40 μL of 1 wt % peptide. Dry films were placed between a glass slide and a coverslip before capturing the images with a Canon G2 digital camera.
Therefore, the novel monomer that we present in this work can be used effectively for the selective deprotection of tyrosine (Supporting Information, Scheme S1). The secondary structure of the peptide components of the conjugates was examined using a combination of circular dichroism (CD) and FTIR spectroscopy. Figure  4  shows CD spectra. The temperature dependence of the CD data was examined in the range 20-60 °C, and no significant changes in the CD spectra were noted for either Tyr 5 -PEO2k-Tyr 5 or Tyr 5 -PEO6k-Tyr 5 (Figure  4a, b ). For Tyr 5 -PEO6k-Tyr 5 at native pH (pH 5. 69), the spectra (Figure  4a ) show a minimum at 218 nm, which is a signature of β-sheet structure. The maximum at 228 nm is due to the absorbance of the tyrosine residues, similar features having been observed for a Tyr 2 -PEO1.
It was observed that neither Tyr 5 -PEO6k-Tyr 5 (native pH or pH 12) nor Tyr 5 -PEO2k-Tyr 5 form a gel up to 20 wt % (sample Tyr 3 -PAla-Tyr 3 is not soluble at high concentration so gel formation is precluded). Figure  9  shows measurements of    a The fit was performed using the long cylindrical shell form factor using software SASfit. 72   dynamic shear modulus for a viscous 20 wt % solution of Tyr 5 -PEO6k-Tyr 5. Stress sweeps (Figure  9a ) showed approximately linear viscoelastic behavior up to a stress σ = 10 Pa. Frequency sweeps were then performed, and the results at two different stress values are shown in Figure  9b. These reveal frequencydependent moduli G″ > G′ (and low values of both moduli) consistent with a predominantly fluid-like response. This conclusion is supported by the observed terminal scalings of the moduli G′ ∼ ω 2 and G″ ∼ ω 1.
Local realignment, duplicate removal and base quality recalibration were performed using the Genome Analysis Toolkit (GATK, v3. 1. 1) (30). Sequencing data have been deposited in the NCBI Sequence Read Archive under the accession SRP055003. Somatic single nucleotide variants (SNVs) were identified using MuTect (v1. 0)  (31) , Strelka (v2. 0. 15) (32) and VarScan 2 (v2. 3. 7) (33) using a consensus approach, retaining those detected by at least 2 mutation callers. Small insertions and deletions (indels) were detected using Strelka (v2. 0. 15) (32) and VarScan 2 (v2. 3. 7) (33). We filtered out SNVs and indels outside of the target regions, those with mutant allelic fraction (MAF) of <1% and/or those supported by ≤5 reads  (34). We further excluded SNVs and indels for which the tumor MAF was <5 times that of the matched normal MAF, as well as SNVs and indels found at >5% global minor allele frequency of dbSNP (build 137).
For in-frame indels, those defined as "deleterious" by MutationTaster or PROVEAN  (41) , and targeted by loss of heterozygosity (LOH) of the wild-type allele or affected cancer genes included in the cancer gene lists described by Kandoth et al. (127 significantly mutated genes)  (42) , the Cancer Gene Census  (43)  or Lawrence et al. (Cancer5000-S gene set) (44) were considered to be likely pathogenic. In addition, frameshift, splice-site and truncating mutations in the presence of LOH of the wild-type allele or affected genes of at least one of the three cancer gene datasets were also considered likely pathogenic. The cancer cell fraction (CCF) of each mutation was inferred using the number of reads supporting the reference and the alternate alleles and the segmented Log2 ratio from MPS as input for ABSOLUTE (v1.
Pathway analysis Genes included in the targeted sequencing assay were mapped to KEGG pathways to identify pathways recurrently altered. Significantly regulated pathways and networks were determined using the Ingenuity Pathway Analysis (IPA) program as previously described  (49). For the analysis, genes harboring likely pathogenic mutations in MaBCs, and not mutated in FBCs, were mapped to networks available in the Ingenuity database and ranked by a score indicating the likelihood of finding those genes together by chance. Using a 99% confidence level, scores of ≥3 were considered significant.
p-values <0. 05 were considered statistically significant. Univariate survival analysis (disease-free and overall survival) was performed using the Kaplan-Meier method and analyzed using the log-rank test. Disease-free and overall survival were expressed as the number of months from diagnosis to the occurrence of an event (local recurrence/metastasis and disease-related death, respectively). All tests were two-sided. Pvalues <0. 05 were considered statistically significant. The above statistical analyses were performed with the software suites GraphPad Prism 6 (GraphPad Software Inc. ), SPSS 20. 0 (IBM) and R v3. 0. 2.
In addition, all mutations selected for Sanger sequencing were validated (Supplementary Fig. S1B ). The genes most frequently mutated in MaBCs were PIK3CA (20%) and GATA3 (15%, Fig. 1 , Supplementary Figure  S2 , Supplementary Tables  S4  and S5 ). All but one PIK3CA mutation affected the hotspots E542, E545 or H1047 (Supplementary Tables S4). Of the 241 genes sequenced, MutSigCV demonstrated that PIK3CA, GATA3, TP53 and MAP3K1 were significantly mutated in MaBCs (q<0. 1; Supplementary Table  S6 ). Cancer genes belonging to the Cancer Gene Census  (43) , or the 127 genes significantly mutated in cancer  (42)  and the Cancer5000-S pan-TCGA datasets  (44) , including PIK3CA, GATA3, KMT2C and TP53, were also mutated in MaBCs (Supplementary Table  S4 ). All PIK3CA (12/12) and all but two GATA3 (7/9) mutations were found to be clonal by ABSOLUTE analysis.
0% vs 0. 8% and 0. 8%, respectively; Fisher's exact tests p=0. 0121 and p=0. 0296, respectively; Supplementary Table  S5 ). Lower frequencies of mutations in HUWE1 and TP53 were detected when ER-positive/HER2-negative MaBCs were compared to germline BRCA2-mutant ER-positive/HER2-negative FBCs (HUWE1: 0% vs 25%, TP53: 7% vs 38%, Fisher's exact tests p=0. 0135 and p=0. 0348, respectively; Supplementary Table  S5 ). Of note, additional genes identified as significantly mutated genes in FBCs, including RUNX1, AKT1 and NCOR1, were not found to be mutated in this cohort of MaBCs (Supplementary Table  S5 ). Given that none of the MaBCs included in this study were classified as invasive lobular carcinomas, we repeated the analyses comparing the repertoire of somatic mutations in ER-positive/HER2-negative MaBCs with that of ERpositive/HER2-negative FBCs/ post-menopausal FBCs, after the exclusion of all cases classified as "infiltrating lobular carcinoma" or "breast lobular adenocarcinoma" in the TCGA dataset.
A pathway and networks analysis of these genes using the IPA software revealed an enrichment of genes involved in DNA repair mechanisms (network score=17, Fig. 4 ). Of note, the genes enriched for DNA repair mechanisms, such as FANCM and PALB2, were also associated with LOH in the ER-positive/HER2-negative MaBCs harboring mutations in these genes (Supplementary Table  S4 ). Furthermore, using The Drug Gene Interaction Database (50), we investigated if any of these 27 genes exclusively mutated in ER-positive/ HER2-negative MaBCs would be actionable. This analysis revealed that ten genes, including PRKCA and RICTOR, may be actionable by a variety of anti-neoplastic drugs (Supplementary Table  S10 ). In terms of CNAs, ER-positive/HER2-negative MaBCs and FBCs/post-menopausal FBCs of ER-positive/HER2-negative subtype shared many of the recurrent amplifications, gains and losses, including gains of 1q, 8q and 16p, losses of 1p and 16q and amplifications of 8q.
Losses of 16q and 17p were significantly less frequent in ER-positive/HER2-negative MaBCs than in ER-positive/HER2-negative FBCs/ post-menopausal FBCs (Fisher's exact test p<0. 05; Fig. 5  and Supplementary Fig. S6 ). Given the high frequency of 16q losses and 16p gains in lobular carcinomas and that none of the MaBCs included in this study were of invasive lobular carcinoma histologic type, we compared the repertoire of CNAs in MaBCs and FBCs/ post-menopausal FBCs, after the exclusion of all cases classified as "infiltrating lobular carcinoma" or "breast lobular adenocarcinoma" in the TCGA dataset. The differences reported above were confirmed (Supplementary Fig. S7 ).
Our data suggest that caution should be exercised in the extrapolation of biological and therapeutic findings from studies performed in FBCs to MaBCs, and that further studies to define targetable alterations in the latter are warranted. Sequencing data, copy number data and intrinsic subtype information of the FBCs were retrieved from The Cancer Genome Atlas (13). Repertoire of copy number alterations as defined by targeted capture massively parallel sequencing in male breast cancers classified into luminal A-like and luminal B-like subtypes. Samples are distributed along the y-axis; the 232 genes mapping to autosomes included in the targeted capture massively parallel sequencing assay are distributed according to their genomic position on the x-axis. Dark blue: amplification, light blue: copy number gain; white: neutral; light red: copy number loss; dark red: homozygous deletion.
FINANCIAL SUPPORT SP is funded by a  Susan G Komen   Postdoctoral Fellowship Grant  ( PDF14298348 ) and GSM by  CAPES  (# BEX 5714/14-1 ). Research reported in this publication was supported in part by a  Cancer Center Support  Grant of the  National Institutes of Health/National Cancer Institute  (Grant No. P30CA008748 ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Introduction There are more than 30000 fish species inhabiting a wide range of aquatic habitats worldwide  [1] , some of them being very important in the global economy and human diet. Moreover, as key integral members of ecosystems, fish are also becoming increasingly important sentinels of environmental health  [2]. Some fish are also relevant laboratory models for the analysis of different developmental processes in vertebrates. In recent years, as part of the policy to improve fisheries management, the estimation of maturity and sex ratio of fish stocks in European waters has become a fundamental requirement of the European Data Collection Framework (http://ec. europa. eu/fisheries/cfp/fishing_ rules/data_collection/index_en. htm). Due to the overexploitation of fish from ocean sources some extractive fisheries have reached a plateau or are depleted.
gtf3a mRNA fragments were amplified using conventional PCR and employing 0,8 mM degenerate primers: forward 5´-TGGARGCTCATCTKTGCAAACACAC-3´and reverse 5´-GTCYCCDCAGGCTYTCCTTCATG-3´. Primers were designed through alignment (Clus-talw2, http://www. ebi. ac. uk/Tools/msa/clustalw2/) of piscine gtf3a sequences available in Ensembl and GenBank and searching for highly conserved nucleotide regions. Properties of designed primers were checked employing the IDT OligoAnalyzer Tool (https://eu. idtdna. com/calc/analyzer). The amplification was run with commercial Taq DNA Polymerase, recombinant Kit and 100 mM dNTP Mix (Invitrogen) for 35 cyclesin a 2720 Thermal Cycler (Applied Biosystems, Carlsbad, California, USA). PCR procedure was as follows: 94°C for 2 minutes, denaturation at 94°C for 30 seconds, annealing at 58°C (Tm) for 30 seconds, elongation at 72°C for 8 minutes and finally 72°C for 8 seconds.
PCR products were stored at 4°C until they were analysed by electrophoresis in ethidium bromide stained agarose gels (1,5%) and sent to the SGIker Sequencing Service of the University of Basque Country for sequencing. Once sequenced, fragments were aligned to obtain a consensus sequence and analyzed using CAP3 (http://pbil. univ-lyon1. fr/cap3. php) and ClustalW2 tools. All sequences obtained have been published in GenBank for Atlantic chub mackerel (JQ928632); Atlantic mackerel (JQ928631), blue whiting (KC191719), European hake (JQ928630), European pilchard (JQ928634), horse mackerel (KC191721) and megrim (JQ928633), and.
Statistical Analysis The statistical analyses were undertaken using SPSS (SPSS Inc. , Chicago, Illinois). Significant differences in RNA levels between both sexes were evaluated by the non-parametric Mann-Whitney U-test. Significant differences in RNA levels, when ovarian developmental stages (and testis) in megrim were compared, were analysed applying one-way ANOVA. Significant differences between groups, in terms of qPCR gene transcription levels, were evaluated using the nonparametric Jonckhere-Terpstra test when a priori ordering was assumed for more than two independent samples (PV>CA>AV). In all cases, significant differences were established at p< 0. 05. Minimal data set for generation of figures and statistical analysis is provided in S1 and S2 Tables.
Several recent studies have demonstrated that people show large behavioural uniqueness. This has serious privacy implications as most individuals become increasingly re-identifiable in large datasets or can be tracked, while they are browsing the web, using only a couple of their attributes, called as their fingerprints. Often, the success of these attacks depends on explicit constraints on the number of attributes learnable about individuals, i. e. , the size of their fingerprints. These constraints can be budget as well as technical constraints imposed by the data holder. For instance, Apple restricts the number of applications that can be called by another application on iOS in order to mitigate the potential privacy threats of leaking the list of installed applications on a device. In this work, we address the problem of identifying the attributes (e.
Introduction People exhibit large behavioural uniqueness. The way how they move around  [10] , purchase goods  [11] , configure their browser  [12] , or browse the web  [24]  make them unique in very large populations. Even various combinations of seemingly innocuous behavioural attributes, such as in which shops they used to buy goods or where they live and work make them unique  [27]. For example, it has been demonstrated that only four spatio-temporal positions are enough to uniquely identify an individual 95% of the times in a dataset of one and a half million users  [10]. As many devices record people's behavioural patterns today, it becomes relatively easy for third parties to access such personal information. This has detrimental effect on individuals' privacy, as behavioural attributes can be easily used for the purpose of reidentification and tracking in different contexts.
5%)  [7]. Several countermeasures have been introduced against fingerprinting in the context of web-tracking  [7]. A simple solution is to completely disable all techniques which allow a tracker to fingerprint visitors, such as the execution of Javascript and Flash programs in a browser. However, this causes significant degradation of user experience and hence it is a less appealing option. Another, more flexible approach is to enable but constrain the usage of such techniques. For example, only limited number of system fonts can be used/queried by an entire website  [20] , or only limited number of applications can be opened from another application on a smartphone  [2]. Hence, the goal of an adversary is to identify at most a given number of attributes (e. g. , fonts or applications), whose values make either a targeted user or every user as unique as possible in the dataset, i.
e. , the number of users sharing the same values of these attributes is minimized. In this paper, we show that the practice of limiting the number of attribute queries per user to mitigate the privacy risks of fingerprinting is often inadequate. We construct efficient greedy fingerprinting algorithms given constraints on the maximum size of the finger- print. We provide essentially the best fingerprinting algorithms under standard computational assumptions. In particular, we address two general problems. Suppose a positive integer s. First, given a targeted user and all his attribute values, we want to know the subset of attributes with cardinality at most s, called the individual fingerprint of the user, whose values make this user as unique as possible in a given dataset. This is referred to as the targeted fingerprinting problem and analogous to the re-identification (de-anonymization) problem when, for example, a user of the Tor Browser is re-identified using some of his/her attributes which can be queried by visited websites.
Finally, we must note that our problems are different from the one in  [21] , which is about finding a minimal subset of attributes that provides a certain separation of all the users in a dataset. That is, given a separation value α, the minimum subset of attributes which provides separation α (i. e. , it separates at least α fraction of all possible record pairs) is approximated in  [21]. By contrast, in our problems, we are given a threshold s and we aim to give a subset of attributes with size at most s which provides the best separation. Obviously, the larger s the better separation is provided. Moreover, as we describe in Section 4. 2, our general fingerprinting algorithm solves some problems in  [21]  more efficiently than prior algorithms. We also note that the naive approach of using a limited number of random attributes as a fingerprint, which is also employed by prior re-identification studies  [4]  [10]  [11] , is inferior to our solution.
Algorithm 1: Greedy Approximation for Targeted Fingerprinting Input: Fingerprint size s, Universe of items U , Dataset B, Targeted user's profile u Output: Fingerprint items K  1  and s = 3. First, we select the attribute which makes U 5 the most unique. U 5 shares the same values of A 1 , A 2 , A 3 and A 4 with 4, 3, 3, and 2 other users, respectively, hence we select A 4 in the first iteration. As a result, U 4 and U 6 still remain indistinguishable from U 5. In the next iteration, we select the attribute which distinguishes U 5 from U 4 and U 6 the most. We have two options: A 2 or A 3 , as A 1 are identical for all of them. We select, say, A 2 , which means that U 5 and U 6 are still indistinguishable. Hence, in the final iteration, we select A 2 , which yields {A 2 , A 3 , A 4 } as the individual fingerprint of U 5.
Algorithm 2: Greedy Approximation for General Fingerprinting Input: Fingerprint size s, Universe U , Dataset B Output: Fingerprint items K 1 K := ∅ 2 items[u] := {i | i ∈ U ∧ u i = True} for all u ∈ B 3 users[i] := {u | u ∈ B ∧ u i = True} for all i ∈ U 4 C := B // 9 for i ∈ u∈S items[u] do 10 t[i] := |{u | u ∈ S ∧ i ∈ items[u]}| 11 separation[i] := separation[i]+t[i]•(|S|-t[i]) 12 isep := arg max i∈U \K separation[i] 13 // if V := {u | u ∈ S ∧ isep ∈ items[u]} 20 T := T ∪ {{V }, {S \ V }} 21 C := T 22 K := K ∪ {isep} 23 return K Finally, we note that Algorithm 2 can also be adapted to compute the minimum number of items which separate all users in B (this is referred to as the Minimum Key Problem in  [21] ). To do so, we only need to set s to |U| in Algorithm 2, which means that the running complexity becomes O(|U | 2 |B|).
Applications In this section, we describe some potential applications of targeted and general fingerprinting in practice. The first application relates to fingerprinting smartphone applications, which has also been partially studied in  [4]. The second application shows how to fingerprint users using the list of (non-)installed fonts in order to track them while they are browsing the web. Finally, we show a use-case of mass de-anonymization in location datasets using our fingerprinting techniques, which can serve as a potential investigation tool for authorities.
Smartphone app fingerprinting It has been shown that knowing the presence of four randomly chosen applications on a device are enough to reidentify users in a dataset of 54,893 users  [4]. Moreover, installed apps also reveal a lot about the owner's interests, and Twitter also used this information for targeted interest-based advertising among others  [19]. In this section, we show how a third party can use such information for tracking. The main idea is to install a malicious application on the user's device which executes the fingerprint queries and identifies the fingerprint value of the user. As some operating systems, such as iOS, limit the number of apps whose existence can be checked by another app, the queries should be optimized such that their result distinguish as many users as possible.
Finally, the tracker redirects to the initial website, and the device can be tracked across websites. {U1, U2, U3, U4, U5, U6} A4 = 1 A4 = 0 {U1, U2, U3} {U4, U5, U6} A2 = 1 A2 = 0 A2 = 1 A2 = 0 {U1} {U2, U3} Iteration 1: isep = A4 K = {A4} Iteration 2: isep = A2 K = {A4, A2} Iteration 3: isep = A3 K = {A4, A2, A3} A3 = 1 A3 = 0 {U4} {U5, U6} {U2} {U3} A3 = 1 A3 = 0 {U5} {U6} Fig. 1. General fingerprinting on the dataset of Table  1 : Greedy algorithm builds a binary tree of partitions, where the leaves constitute the final anonymity sets of the fingerprint K = {A 2 , A 3 , A 4 }. (b) Linking attack: the tracker app is installed with the general fingerprint apps which distinguish all users the most 1. After the fingerprint value is computed by checking the existence of the fingerprint apps on the device 2 , a cookie is created by the tracker which is bound to this fingerprint value.
Retrieving the fingerprint apps on iOS Equipped with the list of the fingerprint apps, the malicious application needs to check their existence on the device. To the best of our knowledge, Android allows to retrieve the complete list of installed apps, but Apple has introduced some limitations with the release of iOS 9 [2]. On iOS, applications can use the canOpenURL() call to determine if another application is available on the system (i. e. , whether it can be called). That is, each fingerprint app query corresponds to a call of canOpenURL() specifying a URL. The call returns a boolean value indicating whether or not the specified URL's scheme can be handled by some app installed on the device. However, Apple introduced two restrictions that apply when the program is compiled on iOS 9 [2].