dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSIONS AND FUTURE WORK The proposed e-infrastructure addresses the challenge of supporting userdriven design and prototyping of innovative e-infrastructure services and applications. It particularly tries to meet the needs of the scientific and technological communities that work on the multi-disciplinary and multidomain problems related to agriculture and food. It will use, adapt and evolve existing open e-infrastructure resources and services, in order to demonstrate how fast prototyping and development of innovative data-and computing-intensive applications can take place. In order to realize its vision, AGINFRA+ will achieve the following objectives:  identify the requirements of the specific scientific and technical communities working in the targeted areas, abstracting (wherever possible) to new AGINFRA services that can serve all users;  design and implement components that serve such requirements, by exploiting, adapting and extending existing open e-infrastructures (namely, OpenAIRE, EUDAT, EGI, and D4Science), where required;  define or extend standards facilitating interoperability, reuse, and repurposing of components in the wider context of AGINFRA;  establish mechanisms for documenting and sharing data, mathematical models, methods and components for the selected application areas, in ways that allow their discovery and reuse within and across AGINFRA and served software applications;  increase the number of stakeholders, innovators and SMEs aware of AGINFRA services through domain-specific demonstration and dissemination activities.
Although several reconstruction algorithms have been developed for 3D SMLM, only few can handle a variety of engineered PSFs  8, 12, 15, 20, 21. Among those, algorithms based on ad-hoc interpolation functions require extensive calibration data to achieve optimal precision, while those based on optical models rarely adopt an optimal PSF estimation strategy that takes into account the Poisson statistics of the images  22. Furtherm re, to the best of our knowledge, a complete user-friendly and free software for 3D SMLM image reconstruction with flexible PSFs is still lacking. Here, we describe Zernike Optimized Localization Approach in 3D (ZOLA-3D, or simply ZOLA), an integrated computational and optical strategy that offers an affordable, easyto-use and versatile tool for 3D SMLM. We demonstrate ZOLA by imaging mitochondria, nuclear pores and microtubules over multiple micrometer depth, with water or oil immersion objectives, and with optimal 3D resolution.
Discussion In summary, ZOLA-3D is a versatile, easy-to-use optical and computational imaging system for 3D SMLM that achieves theoretically optimal resolution over adjustable axial ranges from <1 μm up to at least 5 μm. Even larger axial ranges can in principle be achieved using different PSFs, although their larger lateral extent implies that the density of active fluorophores must be kept very low to maintain high localization precision. Note also that ZOLA may be used to track single particles or molecules in 3D in live cells and that the use of reflective, rather than refractive optics will facilitate applications to multicolor imaging. Our software is freely available from https://github. com/imodpasteur/ZOLA-3D, together with sample data and instructions. We anticipate that ZOLA-3D will greatly facilitate 3D imaging of entire nuclei and cells with super-resolution.
Super-resolution image rendering. Super-resolution images are rendered as 2D or 3D average shifted histograms of 3D localizations. Color can be used to encode the axial coordinate to facilitate interpretation (see Figs. 1h  and 2b, c, e ). We also used the ViSP software to create animated 3D visualizations (Supplementary Movies 2 and 3)  36. Users can import and export localization tables. For each localization, these tables indicate estimated position, photon number, background, χ 2 of the residual, CRLB, and estimated drift (if applicable). Software and computing hardware. ZOLA-3D is provided as an ImageJ plugin available via the Fiji update system  28, 37. Instructions on how to obtain, install and use ZOLA-3D are available at https://github. com/imodpasteur/ZOLA-3D, along with sample data.
Acknowledgements The authors thank  M. Lelek ,  W. Ouyang  and  X. Hao  for discussions and help on experimental and computational aspects,  C. Leduc  and  C. Leterrier  for help with microtubule immunolabeling,  J. Bai  for TOM22 antibodies,  F. Di Nunzio  for Nup133 antibodies,  C. Sieben  and  S. Manley  for sCMOS data. This work was funded by  Institut Pasteur ,  Région Ile de France (DIM Malinf) ,  Fondation pour la Recherche Médicale (Equipe FRM ,  DEQ 20150331762 ), and  Agence Nationale de la Recherche  (grant  ANR   14 CE10 0018 02 ). A. A. is supported by a  Roux-Cantarini fellowship  from  Institut Pasteur.
The work initially started as a test to retrace the Quasar Main Sequence diagram [13] using TOPCAT, where they (and references therein) claimed that the parameter R FeII , which defines the Eigenvector 1 (EV1) is driven by the Eddington ratio alone. We subsequently construct a refined (error and redshift limited) sample from the original QSO catalog  [12]. Based on our hypothesis -the main driver of the Quasar Main Sequence is the maximum of the accretion disk temperature (T BBB ) defined by the Big Blue Bump on the Spectral Energy Distribution. We select five extreme sources that have R FeII ≥ 4. 0 and use the SED modelling code CIGALE to fit the multi-band photometric data obtained using Vizier photometric viewer and available in literature for these sources. Incorporating the prescription for broad emission-line species dependent virial factors, we derive the black hole masses for the entire QSO catalog and compare their distribution with the black hole masses in the original catalog.
Background: One of the 3 tracks of iDASH Privacy & Security Workshop 2017 competition was to execute a whole genome variants search on private genomic data. Particularly, the search application was to find the top most significant SNPs (Single-Nucleotide Polymorphisms) in a database of genome records labeled with control or case. In this paper we discuss the solution submitted by our team to this competition. Methods: Privacy and confidentiality of genome data had to be ensured using Intel SGX enclaves. The typical use-case of this application is the multi-party computation (each party possessing one or several genome records) of the SNPs which statistically differentiate control and case genome datasets. Results: Our solution consists of two applications: (i) compress and encrypt genome files and (ii) perform genome processing (top most important SNPs search).
Related works DNA is the molecule that stores genetic instructions used by any living organism in their growth, development and functioning. The DNA molecules are organized in chains which form the genome. Studying human genome has plenty of practical applications in the medical, social, legal fields, etc. Any two individuals share about 99. 9% of their genomic DNA and the remaining 0. 1% track the differences between them. The vast majority of these differences take the form of single-nucleotide polymorphism (SNP). A SNP is a substitution of one base pair at a certain location when compared to a reference genome. Genome SNP variations are studied in order to track disease genes or heritable traits. One important genomic application is the search for top most significant SNPs, in a dataset labeled with control and case, which are chosen according to the statistical χ 2 test.
Homomorphic encryption is a solution which can ensure genomic data privacy while being able to perform computations. Homomorphic property of group based cryptography was stated in  [10]. The first fully homomorphic encryption scheme (supporting both addition and multiplication) was introduced by Gentry in  [11]. Since then, several other authors proposed new and more efficient homomorphic encryption schemes  [12] [13] [14]. The most recent one  [15]  being able to execute a 2-input Boolean gate in less than 13 milliseconds. On a side note, this encryption scheme was used by 2 teams in the third track of iDASH 2017 competition  [1]. From an applicative point of view, the authors of  [16] [17] [18] [19] [20]  introduced and discussed the use of homomorphic encryption to genomic data processing (e.
Overview of intel SGX Intel's Software Guard Extensions (SGX) was first introduced in 2015 on the Skylake micro-architecture. The aim of this extension is to provide a Trusted Execution Environment (TEE) in which applications can protect critical code and data against malicious privileged system code (operating system, hyper-visor, BIOS, etc. ). The trusted part of the application is called an enclave in SGX dialect. The key point is that enclave code and data inside the CPU perimeter runs in the clear, but are encrypted outside. Figure  1  illustrates the execution of an application using SGX. SGX is built on three components: • 17 new CPU instructions, • a Memory Encryption Engine (MEE) to encrypt/decrypt on the fly, • a MEE buffer of 128MB, in which 96MB are available to the application.
VCF file format The Variant Call Format (VCF) is a format of text files used for storing genome variations. Compared to other file formats which store lots of redundant data (as mentioned earlier 99. 9% of genome is shared between individuals), a VCF file tracks only differences from a reference genome. In this work we suppose that VCF files contain only SNP gene differences. A sample of VCF file (first 8 lines) is given below: A VCF file contains meta-information lines (starting with two "#" symbols), one header line (starting with a "#" symbol) and then one data line per SNP. Each SNP information line contains exactly 8 fields. First 5 fields are: chromosome identifier (CHROM), position within chromosome (POS), unique SNP identifier (ID), reference (REF) and alternate (ALT) base. We consider that chromosome and position fields are integers.
Use-case An important step towards better understanding of human genome is the share of genomic data between entities possessing genome databases (research institutions, state agencies, etc. ). This does not necessarily imply an actual share of genomic databases between two or more entities, which can be a cumbersome and even impossible due to legal restrictions. Legislation in many countries impose a strict regulation on human genome privacy and confidentiality when storing, sharing and manipulating genomic databases. It can materialize itself in carrying out analyzes on a joined view of individual databases and sharing only the results of these analyses. The whole genome variants search for the top most important SNPs introduced previously is a good example of such analysis. Obtained results will have smaller statistical error because of a larger size input dataset when compared to an analysis performed over individual datasets.
Application: top most important SNPs search The algorithm (a high-level view) for finding the top K most important SNPs in a genomic dataset labeled with control and case consists in the following steps: 1 compute SNP presence counters CTRL_CNT and CASE_CNT in control and respectively case VCF files, 2 compute the χ 2 statistic for each found SNP, 3 return the top K most important SNPs. CTRL_CNT and CASE_CNT are maps which associate to each SNP an integer value designating the number of control and respectively case VCF files this SNP is found in. For example CTRL_CNT[SNP] gives the number of control VCF files containing genome difference SNP. We recall that homozygous SNPs count twice in these presence maps. χ 2 statistic The Pearson's χ 2 test allows to determine if there is significant difference between observed and expected frequencies in one or more categories.
In the context of genome search use-case the χ 2 test is used to find which SNP distributions have the largest evidence of statistical difference between case and control datasets. Let s be a SNP found in a VCF file. Let n ctrl and n case denote the number of times SNP variation s appears in control and respectively case files (zero if not present). We have n ctrl = CTRL_CNT[ s] and n case = CASE_CNT[ s]. Let N ctrl and N case be respectively the number of control and the number of case files multiplied by 2 (as homozygous counts twice). SNP s observed frequencies O and expected frequencies E are given by: O = [n ctrl , n case , N ctrl -n case , N case -n case ] E = N ctrl • f , N case • f , N ctrl • 1 -f , N case • 1 -f Here, f = (n ctrl +n case ) (N ctrl +N case ) is s frequency in both datasets.
The χ 2 test statistic value is equal to i (O i -E i ) 2 E i. The p-value is the probability that a random variable following a χ 2 distribution will be larger than the above test statistic value (i. e. the survival function). The SNPs which have the largest p-values are those whose distributions differ the most in case and control datasets. To find the top K most significant SNPs one needs to compute p-values for each SNP in input genome dataset and to return K SNPs with largest p-values.
MAC is the message authentication code output of AES-GCM encryption needed to prove block authenticity. Followed by the encrypted stream of binary formatted SNPs. In a compressed VCF file all the blocks contain the same number of SNPs, except for the last one. Compressed and encrypted files are smaller when compared to initial ones, therefore the network traffic between the actors and the computation server is also lower. Another advantage is that using binary format input files, inside an enclave, is less prone to side-channel information leakage.
A drawback is that input VCF files need to be accessed several times. The fact that SNPs are ordered (by chromosome and position) in input VCF files allows to reduce the number of VCF files accesses. The block diagram of the processing application is shown in Fig. 3. The process starts with enclave creation and initialization (step 1). In step 2 enclave registers case and control VCF files. It simply memorizes VCF file identifiers together with boolean flags indicating whether this file belongs to case or control dataset. Horizontal partitioning of input dataset is performed in the main loop (label "A") of the application. During each main loop iteration a specific range of SNPs is processed. The 4 blocks of the first VCF file serve as reference range (step 4 in diagram). Thus, the SNP range to treat starts at the first SNP from the first block and ends at the last SNP of the fourth block.
Rust programming language Rust is a new system programming language supported by Mozilla research. The aim of Rust is to be a language for highly concurrent and safe systems. Unlike C/C++, Rust has been designed with safety in mind from ground up. Mozilla describe Rust as a "safe, concurrent and practical language". The performance of idiomatic Rust programs is comparable to ones written in C/C++. The most important strengths of Rust as a programming language are: • zero-cost abstraction, • type safety, • guaranteed memory safety, • threads without data races. Rust is influenced by safe functional programming languages like Haskell and OCaml, and its syntax is very close to the ML family languages.
Rust-SGX SDK Intel provides a Software Development Kit (SDK) for implementing SGX application. This SDK is a set of libraries and tools that allow developers to write and debug SGX applications using C/C++ language. As previously said C/C++ are unsafe languages. Developers should be very careful when implementing SGX applications in C/C++ in order to prevent memory bugs (buffer overflow, use-after-free, phantom references, etc. ), which could lead to vulnerabilities and thus compromise enclave application security. Using Rust it is possible to circumvent this pitfall without sacrificing execution performance. Rust-SGX SDK  [31]  is a framework that allows to implement SGX applications in Rust. This framework is developed by Baidu-X lab and is available at  [32]. The framework provides a preconfigured docker image which easies its use.
Discussion In this section we describe in more details the applications we have implemented and the obtained execution results on a sample genomic dataset. The sample dataset has 1000 case and 1000 control VCF files. The size of dataset is approximatively 27 GB. It was provided by contest organizers for testing purposes. Final evaluation datasets were similar to this one. All the applications have been executed on a 5-th generation Intel(R) Xeon(R) CPU E3-1240 (3. 50GHz) processor with 16 GB of RAM memory and an SSD disk.
Compress & encrypt The first implemented application builds a compressed and encrypted version of each VCF file given as input. It starts by parsing a given number (i. e. block size) of VCF data lines, encoding them in binary format and encrypting them using AES. We recall that each data line is an SNP variation. In our study, we have chosen to encode 2080 SNPs per block, 2080 being a common multiple of binary format SNP size (10 bytes) and AES block size (16 bytes). A block of SNPs has a size of approximatively 20 KB. OpenSSL library  [33]  is used to perform AES encryptions. The compress and encrypt application uses 4 threads. Each thread treats an input VCF file. Newly obtained encrypted VCF files are written to disk. Disk input/output bandwidth is the bottleneck of this application, which has to read 27 GB and write 5.
Conclusions In this paper we have discussed the solution submitted to the second track of the 2017 iDASH competition. The goal of this track was to develop an application for searching the top most important SNPs in a genomic dataset. It was requested to use Intel SGX in order to ensure the privacy and the confidentiality of genomic data. Our discourse begins with an introduction of a global view of the solution: a typical use-case and the block diagram. Afterwards we describe some implementation details and execution results on a sample dataset. Shared memory parallelism (i. e. threads) was heavily used to increase execution performance. Aggregated execution time of compression & encryption and processing applications is about 1 min. The compression & encryption step is the heaviest part, needing approximatively 50 s (mainly due to I/O bandwidth bottleneck).
In the typical use-case we have discussed about, this execution time is evenly distributed over implied parties (i. e. each party encrypts its own VCF files). Data space size of our processing application was smaller than processor's L3 cache size which contributed a lot to minimizing the number of costly page evictions. Intel SGX enclave mechanism is not the "holy grail" for privacy preserving computations. Several works from the literature describe side-channel attacks on SGX enclave applications, all these attacks being possible because of code vulnerability. Even if it was outside of contest's goal, we have given a particular attention to lower information leakage from application execution in SGX enclave. We have implemented our enclave application and in particular two software blocks, hash table and χ 2 statistics computation, with small information leakage.
Sample size Because each patient is his/her own control, the randomized observational-phase design is, as a cross-over design, much more powerful than a two-parallel group design. To our knowledge, we have no published data considering both our primary outcome and the target population. We plan to recruit 50 patients. Considering performed after 15 days of sirolimus and then once a month 6 adverse events will be recorded from when the informed consent form is signed VEGF vascular endothelial growth factor a correlation of 0. 5 between the outcomes assessed under the observational and sirolimus period, this sample size will provide the same power as a 200-sample size two-parallel group randomized trial (i. e. , 80% power to detect a 0. 4 effect size, with a two-sided Type I error of 5%).
Blinding The trial will be open-label. With the very nature of the design, blinding is artificial in some way. Indeed, everyone knows that the patient begins with an observational period and ends with a period during which they are treated. Moreover, sirolimus requires biologic monitoring to detect AEs and for dose adjustment (by measuring serum level of sirolimus). Blinding would have involved children undergoing numerous useless blood tests and also important logistical constraints because the people managing the sirolimus adjustment would have to be different from care providers. To compensate for the need for an open-label design, the primary outcome data are collected with blinding (i. e. , centralized interpretation of MRI images performed by the same radiologist who will be blinded to physical assessment and treatment period).
Data management An e-CRF will be developed by using Ennov Clinical© software. The e-CRF will be managed in agreement with INSERM CIC 1415 Standardized Operating Procedures (SOP). Data from investigating centres will be entered by using a secure web site monitored by clinical research associates, and queries will be edited by data managers, in agreement with an a priori-specified data-management plan. Blinded review will be performed before locking the database. The database will be locked in agreement with INSERM CIC 1415 SOPs and data will be extracted in a SAS format or other, according to statistical requirements. Raw data will be stored in a XML format.
Dissemination policy INSERM CIC 1415 Tours will analyze the data provided by the study centres. Results will be displayed in a written report that will be submitted to the sponsor. At the end of the analysis, results will be published in ClinicalTrials. gov. The international rules for writing and publication (Vancouver Agreement, February 2006) will be followed. In accordance with law no. 2002-303 of March 4, 2002, patients will be informed, at their request, about the overall results of the study  [25].
Therefore, assessment of clinical efficacy and self-assessment on symptoms and complications are secondary outcomes of high importance. Safety data will be a focus in this pediatric phase 2 trial, because decisions for treatment in other children with VMs will be based on the benefit/risk ratio. Hence, the main benefit of the study will be to resolve uncertainty concerning the efficacy and safety of sirolimus in children with slow-flow VMs, a subgroup of VAs that includes heterogeneous entities. If we demonstrate a benefit of sirolimus, the remaining question will be how long treatment has to be maintained.
Acknowledgements We are indebted to  Mrs. Aurélie Darmaillacq  from the  Délégation à la Recherche Clinique et à l'Innovation, University Hospital Centre (CHU) Tours ;  Carine Coffre ,  Samia Brit  and  Michèle Carriot ,  Clinical Investigation Center of CHU Tours ; and  Hélène Bourgoin , the  Pharmacy of CHU Tours, France , for their technical help. We also deeply thank  Dr. Lebrun-Vignes ,  Assistance-Publique-Hôpitaux de Paris ;  Pr Franck-Saint-Marcoux ,  CHU Limoges ; and  Dr. Alice Phan ,  CHU Lyon , for being members of the Data Safety Monitoring Board of PERFORMUS.
The oblique angle of view of the data is offering a challenge to classical photogrammetric tools, but the use of modern structurefrom-motion (SfM) photogrammetry offers an efficient and quantitative way to process these data into terrain models. In this paper, we propose a good practice method for processing historical oblique imagery using free and open source software (MicMac and Python) and illustrate the process using images of the Svalbard archipelago acquired in 1936 by the Norwegian Polar Institute. On these data, our workflow provides 5 m resolution, high-quality elevation data (SD 2 m for moderate terrain) as well as orthoimages that allow for the reliable quantification of terrain change when compared to more modern data.