dataesr/scientific-paragraphs-categorization huggingface dataset:
Approval distance does not predict accumulation of further evidence To explore how positive OS evidence has accumulated, we calculated the average E-score increase per year since the first PoE was published for all drugs in the database approved before 2010. More recently approved drugs were excluded from this analysis, because the limited number of years since approval inflated E-score per year estimates for these drugs. There was considerable variability in the rate of evidence accumulation: E-scores per year ranged from 0. 05 to 1. 45 for each treatment. A linear regression of E-score per year compared with approval distance yielded a marginally significant result (p = 0. 051) with an R 2 value of 0. 091. This suggests that the length of time between approval and first published evidence of OS is not the primary factor that determines the rate at which OS evidence will accumulate in subsequent years.
Discussion Long lag times between FDA approval and publication of OS data are commonincluding for many key treatments Using the PACE CII data and analytical tools, we presented a systematic analysis of the historical impacts of approving cancer treatments based on surrogate endpoints. Taken together, these analyses suggest that the availability of evidence of OS at or close to the time of FDA approval is not a reliable indicator of a drug's eventual impact. As an example, fluorouracil was first approved for use against breast, colorectal, gastric, and pancreatic cancers by the FDA in 1962. The evidence that it improved OS in any of the 10 solid tumors we examined was not published until 1979, when it was shown to nearly double survival in pancreatic cancer. Fluorouracil has since become a staple of combination chemotherapy treatments, with evidence of OS improvements in 5 of the 10 cancers studied here, and further evidence from studies of at least 28 different combination treatment regimens.
This leads to a paradox where the most effective treatments (i. e. those that are most likely to have extensive treatment switching from the comparator) can be less likely to generate significant OS effects  [23] , in some cases being accepted as standard of care even when no significant effect was detected 5 years later  [31]. Indeed, there have been attempts to prohibit treatment switching to preserve the fidelity of trial results, such as in the BRIM3 phase 3 trial of vemurafenib for BRAF-positive melanoma worldwide. Among the 15 WHO-listed treatments approved for use by the FDA against any of the 10 cancers we studied with at least one PoE as a single-agent treatment in the CII database, the median approval distance was 7 years. Eighty percent of the drugs had lag times of 3 years or more.
Balancing access and risk How should treatments be assessed to minimize risk without unduly limiting patient access? We believe two major research developments are needed. First, effectiveness data should be collected more quickly using more realistic patient populations by using real-world data and continuous learning systems in drug research. By providing the outcome data more quickly, the use of these data sources could reduce the risk of using surrogate endpoints for approval without limiting the patient access to promising treatments. Availability of real-world evidence and its consideration by regulatory bodies could especially speed the approval of new indications for treatments that already received initial marketing authorization. Learning from real-world evidence can also mitigate the risks to patients ISSN: 1740-4398 ORIGINAL RESEARCH -Surrogate endpoints for oncology drug approvals drugsincontext.
com The CII is publicly available at www. scoringprogress. com. Readers are encouraged to use the tool to conduct tailored analyses with their own assumptions and arrive at evidence-based estimates of risk compared with the benefit of regulatory approval that precedes evidence of OS. expedite the availability of survival data, as well as of designing fit-for-purpose endpoints that consider treatment goals and other contextual factors. These actions can improve the regulatory process by improving efficiency and minimizing the risks to patients inherent in withholding promising treatments until long-term outcome data are available.
3. 2. We focused our analyses on the 10 most common solid tumors: breast cancer, colorectal cancer, endometrial cancer, gastric cancer, liver cancer, melanoma, non-small-cell lung cancer (NSCLC), pancreatic cancer, prostate cancer, and renal cancer. We used the CII Approval Date Analysis tool, data downloaded from the CII platform, and records of nonsignificant OS findings in the CII database to explore the relationship between FDA approval and publication of OS evidence for single agent treatments for the 10 included cancers. This yielded a dataset comprising 71 approvals for 53 treatments between 1952 and 2016.
The PCRs were performed in a total volume of 10 μ l composed of 10 ng of DNA template, 0. 2 μ M of each primer, 5 μ l of Multiplex PCR Master Mix (Qiagen) and water. For PCR amplification, a thermal cycler (Applied Biosystems) was used with the following PCR profile: initial denaturation at 95 °C for 15 min (hot start), 40 cycles of 30 s at 94 °C, 90 s at 63 °C (first multiplex) and 60 °C (second multiplex), 90 s at 72 °C, followed by a final elongation step at 72 °C for 10 min. PCR products were genotyped on a CEQ 8000 DNA fragment analyser (Beckman Coulter) and genotypes were scored using the fragment analysis software CEQ TM.
A set of confidence intervals on the differences among the mean distance-to-centroid of the levels of the grouping factor with the specified family-wise probability of coverage were created. The intervals were based on the Studentized range statistic, Tukey's 'Honest Significant Difference' method. LMMs and PCA were carried out with SPSS 21 (IBM). Differences in dispersion of CHC profiles between I and UI ants were analyzed with R v. 3. 2. 5 (R Development Core Team) using the betadisper and TukeyHSD. betadisper functions in the vegan package  60  , a multivariate analogous of the Levene's test for comparing group variances  61. The graphs were carried out using the ggplot2 R package  62. b. Colony structure. The effect of colony size (no. of workers) and fungal infection on the number of queens was analysed using a generalized linear model approach (GLM, Poisson error, maximum likelihood fit) with number of workers as input variable and fungal infection as fixed factor.
The genotyped data were checked for amplification errors and presence of null alleles using Micro-checker Version 2. 2. 3  63. The conformance with Hardy-Weinberg expectations (HWE) was calculated in Genepop on the Web (v. 4. 2) 64 using an exact probability test (Markov chain parameters: 10000 dememorizations, 100 batches, 1000 iterations per batch) with Bonferroni correction, followed by a heterozygosity excess test with same parameters due to found deviation from HWE. Fixation index (F ST ) and mean pairwise within colony relatedness (Rel; mean across all loci and colony with correction for sample size) were calculated for groups (infected and uninfected colonies), and tested regarding their differences with a two-sided 1000 permutation test in FSTAT (v. 2. 9. 3)  65. We calculated mean pairwise within colony relatedness for each colony according to the algorithm of Queller and Goodnight 66 in Kingroup  67  , with allele frequency calculated from the whole dataset, to confirm the results obtained.
Acknowledgements We are grateful for the useful comments of  Abel Bernadou ,  Wojciech Czechowski ,  Jürgen Heinze  and  Lajos Rózsa , and two anonymous referees that helped us improve significantly the quality of the manuscript. Katalin Erős ,  Hanna Babik , and  Zsolt Czekes  helped considerably during the fieldwork and aggression tests. M. W. was supported by grant of  Polish National Science Centre , project no. 2015/17/B/NZ8/02492. E. C. and B. M. was supported by a grant of the  Romanian National Authority for Scientific Research and Innovation ,  CNCS -UEFISCDI , project no. PN-II-RU-TE-2014-4-1930. B. M. was also supported by the  Bolyai János scholarship of the Hungarian Academy of Sciences. C. L. was supported by the  PRES Centre Val de Loire Université  ( APR-IA 2012 ) and the  GDR3658 MediatEC.
Sequence data We compiled a data set of 1,610 publicly available full Ebola virus (EBOV) genomes sampled between 17 March 2014 and 24 October 2015 (see https://github/ebov/space-time/ data/ for full list and metadata). The number of sequences and the proportion of cases sequenced varies with country; our data set contains 209 sequences from Liberia (3. 8% of known and suspected cases), 982 from Sierra Leone (8. 0%) and 368 from Guinea (9. 2%) (Supplementary Table  1 ). Most (N=1,100) genomes are of high quality, with ambiguous sites and gaps comprising less than 1% of total alignment length, followed by sequences with between 1% and 2% of sites comprised of ambiguous bases or gaps (N=266), 98 sequences with 2-5%, 120 sequences with 5-10% and 26 sequences with more than 10% of sites that are ambiguous or are gaps.
Within-location generalized linear models Ebola virus disease (EVD) case numbers are reported by the WHO for every country division (region) at the appropriate administrative level, split by epidemiological week. For every region and for each epidemiological week four numbers are reported: new cases in the patient and situation report databases as well as whether the new cases are confirmed or probable. At the height of the epidemic many cases went unconfirmed, even though they were likely to have been genuine EVD. As such, we treat probable EVD cases in WHO reports as confirmed and combine them with lab-confirmed EVD case numbers. Following this we take the higher combined case number of situation report and patient databases. The latest situation report in our data goes up to the epidemiological week spanning 8 to 14 February 2016, with all case numbers being downloaded on 22 February 2016.
Computational details To fit the models described above we took advantage of the routines already built in BEAST (https://github. com/beast-dev/beast-mcmc) but in a non-phylogenetic setting. Once again, posterior distributions for the parameters were explored using Markov chain Monte Carlo (MCMC). We ran each chain for 50 million iterations and discarded at least 10% of the samples as burn-in. Convergence was checked by visual inspection of the chains and checking that all parameters had effective sample sizes (ESS) greater than 200. We ran multiple chains to ensure results were consistent. To make predictions, we used 50,000 Monte Carlo samples from the posterior distribution of coefficients and the overdispersion parameter (r) to simulate case counts for all locations with zero recorded EVD cases.
Acknowledgments The authors acknowledge support from:  European Union   Seventh Framework 278433-PREDEMICS  (P. L. , A. R. ) and  ERC 260864  (P. L. , A. R. , M. A. S. ) European Union Horizon 2020 643476-COMPARE (M. P. G. K. , A. R. ), 634650-VIROGENESIS (P. L. , M. P. G. K. ),  666100-EVIDENT  and  European Commission   IFS/2011/272-372 , EMLab (S. G. ),  National Institutes of Health   R01 AI107034 ,  R01 AI117011  and  R01 HG006139  and  National Science Foundation   IIS 1251151  and  DMS 1264153  (M. A. S. ),  NIH   AI081982 ,  AI082119 ,  AI082805 AI088843 ,  AI104216 ,  AI104621 ,  AI115754 ,  HSN272200900049C ,  HHSN272201400048C  (R. F. G. ),  National Health & Medical Research Council (Australia)  (E. C. H. ). The  Research Foundation -Flanders   G0D5117N  (G. B. , P. L. ), Work in Liberia was funded by the  Defense Threat Reduction Agency , the Global Emerging Infections System, and the Targeted Acquisition of Reference Materials Augmenting Capabilities (TARMAC) Initiative agencies from the  U.
bioinformatics. babraham. ac. uk/projects/) (3). A second 500 bp paired-end library was sequenced (2 ϫ 100 bp) as part of a HiSeq run, which generated 13 million paired 100 bp reads after QC check and trimming (3). The IonTorrent data were assembled de novo with Newbler 2. 8. The paired-end Illumina data was assembled de novo with velvet (4). The pooled IonTorrent and Illumina data was assembled de novo with MIRA (5) and Newbler. The resulting assemblies generated 27, 11, 2, and 2 contigs for IonTorrent, Illumina, and both hybrid assemblies, respectively. The scaffolds were connected by Sanger sequencing of PCR amplified fragments. The total size of the assembly is a single circular chromosome of 1,977,126 bp with an average GϩC content of 44. 6%. The sequence was automatically annotated by the NCBI  (6)  and is available on the MaGe platform for genomic comparisons  (7, 8).
In contrast to other Pyrococcus species, P. kukulkanii encodes the locus for formate-driven hydrogenogenesis growth described so far only in Thermococcus onnurineus, T. gammatoelrans, and T. barophilus  (11) (12) (13)  and present only in the other piezophile Pyrococcus yayanosii  (14). However, it lacks the CO dehydrogenase which allows hydrogenogenic lithoheterotrophic growth on CO as in these three Thermococcus species  (15). Accession number(s). The final annotated genome of Pyrococcus kukulkanii NCB100 is available in GenBank under accession number CP010835.
ACKNOWLEDGMENTS This work was supported in part by the  Agence Nationale de la Recherche  ( ANR-10-BLAN-Living deep ) to P. M. O. and M. J. and by a program of the  FR41  to C. O. -D. and P. M. O. Sequencing was performed in part at the  DTAMB (Université Claude Bernard Lyon 1)  and at the sequencing platform of the  Institut de Génomique Fonctionelle de Lyon  as part of a series of tests of the HiQ kit from IonTorrent.
, 2000)  aim at retaining this search capability on encrypted data. SSE scheme is a protocol between a client and a server. The client owns a sensitive data set but has limited computational power and storage capacity. The server has a large storage space and high processing power, but is not trusted by the client except for executing correctly the search protocol. The set of plaintext documents are stored in a DataBase (DB). An SSE scheme creates metadata that is protected in an Encrypted DataBase (EDB) and then stored by the server. From a keyword and his symmetric secret key the client creates a search token that is sent to the server who finds the encrypted documents matching the query with the help of EDB. Such documents are then sent back to the client for decryption. While the single keyword query is the basic functionality of an SSE scheme there exist SSE schemes which allow the client to add new encrypted documents to the encrypted database while retaining the search capability  (Cash et al.
These search and access patterns are used in inference attacks  (Islam et al. , 2012; Cash et al. , 2015; Pouliot and Wright, 2016)  whereas our passive attacks do not use these information. In this paper, we focus on the information revealed by the encrypted database regardless exchanges between the client and the server. This model assumes that the adversary can be the server himself or a malicious person who is able to access to the encrypted database stored on the server. Based on deployed SSE schemes,  (Cash et al. , 2015)  define four leakage profiles L4, L3, L2 and L1, L4 being the most leaky and L1 the least. Commercially available SSE solutions are L4 schemes such CipherCloud * and Skyhigh Networks † , L3 schemes such Bitglass ‡ or L2 schemes as ShadowCrypt  (He et al. , 2014)  and Mimesis  (Lau et al.
, 2014)  while proposed schemes in academic research are L1 schemes. L4-, L3and L2-SSE schemes can be used as a proxy for existing cloud solutions or as extensions in client-side and so do not require any modification on server-side. Assessing the practical impact of each of these profiles on the server knowledge of the protected data is critical for real life applications. We study the impact of a passive attacker. Our contributions. Only assuming the knowledge of a small sample of plaintexts in addition to the protected database given to the server, we design passive attacks on L4, L3 and L2 leakage profiles. In particular, our attacks do not rely on observing search queries. Our attacks exploit the leaked information the scheme on the encrypted database to find, starting from a sample of plain documents, their identifiers in the encrypted database.
Then, knowing these correspondences, the adversary tries to determine values of plain keywords in the encrypted database to recover other documents. Our attack on L4 schemes uses repetitions and order of keywords in each document, our attack on L3 schemes uses order of shared keywords between documents while our attack on L2 schemes uses only information on shared keywords between documents. The attack on L2 schemes Their efficiency and practicality are demonstrated on several real-world data sets such as the mailing-list of Lucene Apache project §. In fact, the knowledge of a small sample of plain documents by an adversary has a huge impact. With our passive attacks on L4and L3-SSE schemes, an adversary knowing only 1% of plain documents is able to reconstruct 90% of the protected data at 80%.
For an active adversary able to plant chosen documents in the database,  (Cash et al. , 2015)  present a partial document recovery attack on L3and L2-SSE schemes. With the extra ability to issue selected queries,  (Zhang et al. , 2016)  mount a query recovery attack that works on any dynamic SSE scheme. These active attacks are very efficient as few injected files reveal associations between keywords and search tokens but are different from ours since we consider only a passive adversary who is not able to plant document in the database. Inference attacks based on the observation of client queries and server responses have been also proposed. The first one is the IKK Attack, proposed in  (Islam et al. , 2012). Its goal is to associate search tokens to actual keywords, exploiting the data access pattern revealed by client queries and assuming the adversary has access to a co-occurrence matrix that gives the probability for two keywords to appear in a randomly chosen document.
As noted in  (Cash et al. , 2015) , this matrix needs to be so precise for the attack to succeed, that it seems legitimate to suppose the adversary has access to the number of documents in which every keyword appears. With this strong extra knowledge, they mount a more effective attack named the Count Attack  (Cash et al. , 2015). Both attacks target leakage profiles beyond L1, but the strength of their assumptions questions their practicality. In comparison our attacks do not rely on observing client queries but only consider the encrypted database as viewed by the adversary. We compare IKK and Count attacks to our passive PowerSet attack in Section 6. Additionally,  (Cash et al. , 2015)  propose a passive partial document recovery attack for L3-SSE schemes when the adversary knows plaintext-ciphertext pairs.
Our attacks suppose that we have not plaintext-ciphertext pairs initially. An other approach, called Shadow Nemesis Attack, is proposed in  (Pouliot and Wright, 2016). Using a training data set, this inference attack builds a co-occurrence matrix and reduce the problem of matching search tokens to keywords to the combinatorial optimization problem of weighted graph matching. This attack can be performed on L2-SSE schemes as our attacks. It uses the encrypted database and a training data set or partial knowledge on the original data set whereas our attacks use only partial knowledge on the original data set. We show in Section 6 that our PowerSet attack recovers more keywords with the same knowledge. Outline. In Section 2, we provide background on SSE schemes and their security. We recall in Section 3 the leakage hierarchy of  (Cash et al.
Definition Static SSE scheme. Given a symmetric encryption scheme (E • (•), D • (•)) where E • (•) denotes the encryption algorithm and D • (•) denotes the decryption algorithm, we define a static SSE scheme of security parameter λ as a quartet of polynomial-time algorithms Π = (Gen, Setup, SearchClient, SearchServer) by: (K, k) ← Gen(1 λ ) is a probabilistic algorithm run by the client. It takes as input a security parameter λ, and outputs two symmetric secret keys K and k which are both kept securely by the client. (EDB, DB * ) ← Setup(K, k, DB, E • (•) ) is an algorithm run by the client to set the scheme up. It takes as input secret keys K and k, the database DB and the encryption algorithm E • (•), and outputs both the protected metadata EDB and the encrypted documents DB * = (E k (d 1 ),.
, E k (d n )). w * ← SearchClient(K, w) is a deterministic algorithm run by the client to send a query to the server. It takes as input the secret key K and a keyword queried w ∈ W, and outputs the search token w * ∈ W * associated with w. Finally w * is sent to the server. EDB(w * ) ← SearchServer(EDB, w * ) is a deterministic algorithm run by the server to answer a client-query. It takes as input the protected metadata EDB and the client-generated search token w * and outputs EDB(w * ): the identifiers of the encrypted documents containing keyword w. This list is sent back to the client. This defines static SSE schemes. Static SSE schemes allow the client to initialize a protected database that supports keyword searches but cannot be updated by opposition to dynamic SSE schemes. We do not introduce dynamic schemes since their encrypted databases can be attacked at least as well as static schemes.
A LEAKAGE HIERARCHY We recall classes of SSE schemes according to how much information the protected database leaks, as first introduced in  (Cash et al. , 2015). L4 Leakage Profile. Without any semantic consideration, a document is characterized by its number of words, their order and their occurrence counts. Moreover, it is possible to know which words are shared with any other document. L4-SSE schemes used by commercial encryption products as Cipher-Cloud reveal these information, so nothing is lost about the plaintext non-semantic structure. A SSE scheme of leakage function L is of class L4 if and only if L(EDB) = (w * i 1 ,. , w * i i ) 1 i n. Example. We use the following setup as a running example to illustrate the different amounts of leakage revealed to the server. Let W = {as, call, i, if, me, possible, soon, you} and d 1 and d 2 , two documents defined over W where d 1 = (call, me, as, soon, as, possible) and d 2 = (i, call, you, if, possible).
the number N of document/keyword pairs of the data set. Thus L(EDB) = {N}. Example. Resuming the running example, the information revealed by an L1-SSE scheme looks like: w * α β γ δ ε ζ η θ ι κ Id. a b c d e f g h i j Greek (resp. Latin) letters represent tokens (resp. identifiers). The server has absolutely no clue about this correspondence, so it only knows N = 10. If the client searches for "soon" and "you", this reveals: w * α β γ δ ε 35 35 θ ι 78 Id. a b c d e id 1 id 2 h i id 1 Hence, the server learns that documents identified by id 1 and id 2 share the same keyword of token 35; keywords of tokens 14 and 35 are both in the document identified by id 1. Effect of Queries on the L1 Leakage Profile. We study what can be inferred from the protected database, but it is informative to reflect upon the effect of queries on the amount of information revealed to the server.
ATTACKS Our attacks aim at recovering information on encrypted documents from the knowledge of EDB stored on the server. Hence, the attacker can be a curious server or a malicious person who is able to access the server. These attacks are completely passive; the only assumption made here is that we know a (small) sample S of the plaintext documents. We emphasize that we do not know any pair of cipher/plain documents. We stress that this knowledge of a sample S is in practice a realistic assumption: for instance, data sets of mails might contain items that have been transferred outside the scope of the SSE scheme. We can also imagine a user having a part of its data on a server and he decides to encrypt all of its data using a SSE scheme. When the user uploads the encrypted database, the server has the knowledge of both the old plain data and the encrypted database.