dataesr/scientific-paragraphs-categorization huggingface dataset:
subtilis (blue, A chain of PDB code 4JGO; pink, the ligand of this protein, pyruvate)  (48). c, superimposition of Tll0287 and a methyl-accepting chemotaxis protein (Mcp) (purple, A chain of PDB code 3C8C; cyan, the ligand of this protein, alanine). d, superimposition of Tll0287 and the dimer of the non-membrane part of GSU0935 (PDB code 3B42)  (62). than the cytoplasmic PAS domain, which has a 1␣-2␤-4␣-3␤ folding pattern  (58). Previous studies have suggested that the sensor domain of KinD has an extra ligand between the ␣-helix and ␤-strands, and the ligand binding site may accommodate small molecules related to metabolism such as pyruvate or propionate  (48). In the electron density map of Tll0287, we indeed found an extra density at the similar position between the ␣-helix and ␤-strands (Fig.
8 ), which is in the concave pocket shown in Fig. 4. It seems that the extra density is surrounded by the guanidine group of Arg 87 and the carboxyl group of Asp 105 and Thr 179. To identify the possible ligand corresponding to the extra density, we used the Arp/wARP program  (60)  for prediction of the extra ligand, which suggested that the extra ligand may be 2-oxoglutaric acid, although its abundance may be low in the thylakoid lumen. On the other hand, MALDI-TOF/MS mea-surements of the Tll0287 sample after trypsin digestion showed extra masses of 617, 74, and 118 Da (data not shown). The former two correspond to the masses of heme c and chlorine, respectively, whereas the mass of 118 Da has no corresponding molecules and may therefore be derived from the extra ligand. This mass is similar to those of homoserine, valeric acid such as norvaline, or betaine, which was used as an additive in the purification buffer.
H, helix; E, strand; L, coil. O 2 was not produced, the expression level of Tll0287 increased in the WT*2 strain, at least transiently. A second approach searching for structural similarities was done by using the Phyre 2 server (61), which revealed a good structural similarity with GSU0935, a methyl-accepting chemotaxis protein from Geobacter sulfurreducens (PDB code 3B42) (62) (Fig. 6d ). This b-type heme protein also shows a PAS-type fold but exhibits axial ligations (His-water and His-Met) different from that of Tll0287 with a high-spin configuration in the oxidized state. The overexpressed non-membrane part of GSU0935 was crystallized as a dimer  (62). Interestingly, as shown in Fig. 6d , the heme part of one monomer of GSU0935 fits very well with the heme part of Tll0287, and the two ␣-helices of the opposite monomer fit very well with two of the ␣-helices, ␣1 and ␣2 of Tll0287, although they do not have counterparts in the first monomer.
Two distinct scenarios can be proposed based on this pattern of cooccurrence: (a) the sulfide dehydrogenase in the clade where "SQR" and Tll0287 homologs co-occur is actually a sulfide:cytochrome-oxidoreductase rather than a sulfide:quinone-oxidoreductase, and the Tll0287 gene product would in fact correspond to the cytochrome subunit of an H 2 S-oxidizing enzyme, reminiscent of so-called flavocytochrome c-sulfide dehydrogenase observed in a number of prokaryotes  (64) ; and (b) the association of Tll0287-homologous genes to sulfide metabolism is more indirect and potentially even related to some kind of sensory mechanism (it could be noted here that several of the hemo proteins with His-Cys axial ligation are involved in the sulfide metabolism). We presently consider the first possibility as less likely because (a) no co-purification of Tll0287 and SQR has been observed so far, and (b) the sequence motif involved in forming the quinone-binding pocket in SQR (GQMTEE in Caenorhabditis elegans, deduced from the three-dimensional structure of Aquifex aeolicus SQR; PDB code 3HYW) is highly conserved in both subclades arguing for true SQR-type functional characteristics of all enzymes in both clades.
Crystallization and X-ray data collection Initial crystallization conditions were screened with an automatic dispensing machine (Mosquito, TTP Labtech) with over 800 commercially available screening conditions at a concentration of 2 mg protein/ml. One of these conditions composed of Na 2 HPO 4 , K 2 HPO 4 , (NH 4 ) 2 HPO 4 , and Tris, pH 8. 5, produced small crystals with a red color of Tll0287. These small crystals were used as seeds to grow larger crystals under the same condition for 2 weeks at 20 °C. The crystals obtained were needle type, and they were cryo-protected using a solution containing 0. 7 M Na 2 HPO 4 , 0. 7 M K 2 HPO 4 , 0. 1 M (NH 4 ) 2 HPO 4 , and 0. 1 M Tris, pH 8. 6, in either the presence or the absence of 25% (w/v) glycerol in a stepwise fashion, and flash frozen under liquid nitrogen gas.
Structure determination Diffraction data were indexed, integrated, and scaled by XDS and XSCALE  (67). The initial phases were obtained using SHELX C/D/E (68). The iron site identified was provided to the program SHARP/autoSHARP  (69, 70)  for density modification and automatic model building, which results in a model having 155 residues. The rest of the model was manually built with the program COOT  (71, 72) , and structural refinement was performed with the program Phenix  (73). Ramachandran plot was calculated with the MolProbity  (74). The statistics of data collection and structural refinement were summarized in Table  1. For prediction of the extra ligand in the Tll0287 protein, the software Arp/wARP (60) was used. The mF o -DF C map and 2mF o -DF c map without introducing any ligands were calculated using FFT in the CCP4 suite  (75).
Equilibrium redox titrations were performed by applying potentials to the working gold grid from Ϫ100 to Ϫ500 mV versus NHE with 20-mV intervals using a potentiostat (PRGE, Tacussel Electronique). For each potential step, the sample was allowed to equilibrate for 5 min. Spectra from 400 to 650 nm were recorded using a modified Cary 14 spectrophotometer equipped with a xenon light source and a UV-enhanced silicon detector. The data were analyzed by calculating the integrated Soret absorption (422-457 nm) obtained from the difference of the spectrum recorded at a given potential minus the spectrum of fully oxidized Tll0287 obtained at Ϫ100 mV versus NHE. The electrochemical cell was thermostatted at 15 °C with a water circulation system, and the sample compartment was purged with dry air. Buffers at different pH values contained 200 mM NaCl, 0.
Acknowledgments-We thank the staff members at Beamlines  BL41XU  and  BL44XU  of SPring-8 for help in data collection, the staff members of CCP4 structure solution workshop for prediction of the extra ligands, and  Shinichiro Yonekura  for help in performing the immunoblot analysis. Winfried Leibl ,  Anja Krieger ,  Frauke Baymann ,  Ghada Ajlani ,  Pierre Sétif ,  Alain Desbois , and  Fabrice Rappaport  are gratefully acknowledged for investment in some periods of this work and for helpful suggestions.
In this paper, we analyze and propose a solution for the challenges that come with personalized genomic and, most importantly, of performing queries on sequenced dataset sitting on a cloud server. This work provides scenarios for its application in personalized genomic medicine, and tests homomorphic encryption technique to assist in improving the strength of their privacy at non-prohibitive performance cost. By experimental testing using HElib, we make a first step towards performing practical computation over the relevant portions of the genomic dataset of an individual for a first round of practical diagnosis rules.
INTRODUCTION Physicians and researchers think that understanding how genes influence disease will require genetic and health datasets to be collected from millions of people. Such a massive task will probably require harnessing the processing power of networked cloud computers, but online security breaches in the past few years illustrate the dangers of entrusting huge, sensitive datasets to the cloud. Genomic dataset analysis is increasingly incorporated in a variety of domains, including personalized medicine, biomedical research, direct-to-consumer services, and forensics. Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. As a result, genome sequencing may soon become a routine tool for clinical diagnosis and therapy selection.
In the (near) future, personalized medicine will result in "right drug at the right time" according to their patients' genome dataset. The implementation of genomic-based medicine is the challenge of transmitting clinically useful information to health-care practitioners. In this study, we choose personalized medicine case scenario as a model setting for an implementation of privacypreserving genetic testing and results reporting. Genomic Dataset Privacy Issues. The whole genome sequencing was initiated at the U. S. National Institutes of Health (NIH) in 1990 and the first full sequence was released years later at a total cost of $3 billion. Yet, sequencing technology has evolved and costs have plummeted, such that the price for a whole genome sequence is $5K as of July 2014 and can be completed in two to three days.
The "$1K genome in 1 day" will soon be a reality  (Naveed et al. , 2015). As the cost of sequencing the human genome drops, more and more genomic dataset will become available for research and study. At the same time, researchers are developing new methods for analyzing genomic dataset across populations to look for patterns and find correlations. Such research may help identify genetic risk factors for diseases, suggest treatments, or find cures. To make this dataset available for scientific study, patients expose themselves to risks from invasion of privacy  (Ayday et al. , 2013a). Decreases in sequencing costs have coincided with an escalation in genomics as a research discipline with explicit application possibilities.
Personalized Medicine. Personalized medicine promises to revolutionize healthcare through treatments tailored to an individual's genomic makeup and genome-based disease risk tests that can enable early diagnosis of serious diseases such as diagnosis of suspected mendelian conditions and for targeting cancer treatments. The current rise of personalized medicine is based on increasing affordability and availability of individual genome sequencing. As mentioned in Subsection 1, the cost to sequence an entire human genome continues to fall, the potential exists for rapid advances in wellness and health care resulting from this new technology. Essential to achieving those advances is the need to outsource, compare, and aggregates the genome dataset. However, as the ease with which the acquisition and outsourcing of genome sequencing information increases, so we will have questions and concerns about privacy, security, and efficiency.
Contributions of this Paper In this paper, we propose an architecture and its application in personalized medicine case scenario. We test homomorphic encryption techniques to assist in improving the strength of their privacy at non-prohibitive performance cost. We experimentally analyse our personalized medicine case scenario architecture using the HElib library, and HElib achieves near practical computation cost. We show that the proposed solution have used real genomic rules, which have been generated by the geneticists in our team (authors 2 and 3). Our main contributions are (i) to keep genomic datasets secure while still enabling cloud-based analyses needed to make meaningful diagnosis, (ii) provide acceptable level of privacy requirements in each step of handling of genomic datasets, collecting, analyzing, storing or sharing the genetic informations and (iii) provide a characterization of various threat models that are addressed at each step.
THE CURRENT SOLUTIONS Privacy issues caused by forensic, medical and other uses of genomic dataset have been studied in the past few years  (Jiang et al. , 2014) , and  (Naveed et al. , 2015). Homomorphic encryption technique is quickly becoming more relevant due to its great potential for privacy computation on encrypted genomic datasets. This technique has a number of other advantages, allowing for more flexible case scenarios, and requiring less interaction, thereby reducing the communication complexity. The cryptographic overhead consists of the time to perform operations for each gate of the circuit as well as other maintenance operations. Unfortunately, it is hard to characterize simply the cryptographic overhead of fully homomorphic encryption (FHE) because there are a lot of parameters that affect its performance, such as the multiplicative depth, the security parameter, the plaintext size, the exact FHE scheme used, the performance of various operations in the finite fields used.
Lepoint and Naehrig (Lepoint and Naehrig, 2014)  and Halevi  (Halevi, 2013)  provide performance measurements for various settings of these parameters. A number of key optimizations and batch techniques  (Halevi and Shoup, 2014) ,  (Zhou and Wornell, 2014)  have been introduced to reduce overall computation complexity and increase efficiency of these homomorphic based schemes. Recently, homomorphic encryption techniques  (Ayday et al. , 2014) ,  (Ayday et al. , 2013b) ,  (Lauter et al. , 2015)  have been used to encrypt genomic datasets in such a way that storage can be outsourced to an untrusted cloud, and the datasets can be computed on in a meaningful way in encrypted form, without requiring access to decryption keys. These protocols have some drawbacks such as being computationally intensive, leaking more than necessary and being unscalable; mainly due to the very large size of genomic datasets.
However, a number of optimization techniques  (Halevi and Shoup, 2014) ,  (Zhou and Wornell, 2014)  have been presented to overcome the limitations of using homomorphic based solutions. Building practical systems that compute on encrypted genomic datasets are a challenging task. One reason is that homomorphic encryption method remains too slow for running arbitrary functions or for enabling the complex systems we have today. Another reason is that many systems take advantage of fast search data structures (such as database indexes), and a practical system must preserve this performance over encrypted dataset. We present a cryptographic solution for genomic datasets storage and outsourcing, and maintaining patient privacy. All encrypted genomic datasets are stored in an untrusted cloud server.
To allow meaningful computation on the encrypted genomic datasets, we use HElib  (Halevi, 2013) ,  (Halevi and Shoup, 2014). Specially, we take basic genomic algorithms which are commonly used in genetic association studies and show how they can be made to work on encrypted genotype and phenotype datasets. We also tackled the insider attack situation in an untrusted cloud server where the attacker is assumed to have access to the content of the disk as well as the CPU and the memory.
OUR ARCHITECTURE The system architecture is designed for enhanced privacy protection of encrypted genomic datasets downloaded to a medical organization/doctor from the cloud server, such as in the case of personalized medicine. The proposed system (Figure  1 ) involves five components: (i) the patients; (ii) Healthcare Providers (HPs) have patients' biological samples; (iii) a Sequencing Facility (SF) responsible for sequencing, management of cryptographic keys, and encryption of patients' sequenced genomic datasets; (iv) a Cloud Server (CS) where the encrypted form of "sequenced genomic datasets" (EGDS) are stored, and all computations and communications held on; and (v) Medical Organizations (MOs), or doctors, wishing to perform genetic tests on the patients' genomic datasets. Our full architecture includes several patients, several HPs, and several MOs/doctors, all accessing the one server.
The CS has a component which we refer to as Service Manager (SM), which is responsible for dataset storage in the CS, performs computation on requested query to get the result, and sends query result to the MO/doctor. To simplify the explanations, in the system model here we present only one patient, one HP, one MO/doctor, as shown in Figure  1. In our architecture, MO/doctor is a part of the HP. The MO/doctor has the secret key to decrypt EGDS and get the sequenced EGDS for further analysis and research. Patient can also have secret key to get the EGDS from the CS. To secure the communication, the participants (patient, a HP, and the MO/doctor) are authenticated, and the connection is protected using authorization process and risk management policies. We deploy access control policies at the CS.
Data Contributor Module The data contributor module has three components: a patient, a HP, and a SF. Patient. Each patient has history of medical records and biological samples to the HP, which is containing sensitive data such as blood samples. Patient has the secret key to see the content of his EGDS, which is stored on the CS. Healthcare Provider (HP). The HP is responsible for good medical treatment of each patient. The HP sends biological samples such as blood sample to the SF, in order to sequence genome datasets for better medical treatment. Sequencing Facility (SF). The main component on the data contributor is the SF. Now, the SF has biological samples, which is received from the HP for sequencing analysis. We note that the SF currently would have access to unprotected raw genetic variants and therefore must be a trusted entity.
The SF is responsible for genome sequencing received from the HP, encrypting genome datasets using strong symmetric data encryption, and sending encrypted form of sequenced genomic datasets (SGDS) to the CS for dataset storage. In addition, we are assuming a model where the encrypted SGDS are stored in a centralized untrusted CS rather than at the HP, which maximizes efficiency and security. To maintain privacy of SGDS on the CS with no storage overhead, transciphering techniques  (Canteaut et al. , 2016)  have to be used to translate these datasets into the homomorphic Towards Confidentiality-strengthened Personalized Genomic Medicine Embedding Homomorphic Cryptography domain, where they can be processed with respect to the public key of a given MO/doctor (that way, FHE overhead is paid only transiently in the server memory during the homomorphic calculation).
Cloud Server Module Cloud computing provides massive computation power and storage capacity which enables a user to deploy applications without infrastructure investment  (Zhang et al. , 2013). We do not deal with the CS's internal working here (See Figure  1 ). The service manager (SM) is a function of the CS which handles each send/receive process on a CS. The SM accepts EGDS from the SF and stores in the data repository. The SM privately processes queries, gives 'private' feedback to MO or doctor, and maintains the credential revocation. At the CS, we also use access control mechanisms to provide additional layer of security for insider threat. Risk Assessment. Insiders use many approaches and factors to launch attacks. Two of the most risky factors that can be used are insiders' knowledgebase and dependencies among datasets.
THE BGV SCHEME The Brakerski-Gentry-Vaikuntanathan (BGV) scheme  (Brakerski et al. , 2012)  stands today as one of the most efficient somewhat FHE scheme. The implementation of this scheme has been discussed in the literature  (Brakerski et al. , 2012) , which is focused on the evaluation of AES  (Gentry et al. , 2010). In our setup, it involves encrypting genomic datasets on a SF, then uploading EGDS to the CS. Computations on EGDS are performed in the CS and an encrypted result is then sent back to a MO/doctor as requested data from the CS. The MO/doctor decrypts an answer using secret key. If attackers were to intercept EGDS at any point along the way, the underlying data would remain safe. We use HElib software library for our experimental analysis and results (see in Subsection 6. 2), which implements the BGV scheme, along with many optimizations to make homomorphic evaluation runs faster.
INSIDER THREAT MODEL This section addresses that we prevent the CS from launching the insider attacks. In our architecture, the SM manages and stores EGDS from the SF. These datasets may have dependencies that can be determined by an insider. In addition insiders can retrieve general knowledge about the types of datasets being stored. Use of such dependencies among datasets and the knowledgebase of insiders  (Nogoorani and Jalili, 2016) , (Q. Yaseen et al. , 2013)  may enable the insider to infer sensitive information. We choose the following criteria for the establishment of protection against insider attacks: 1. To prevent the SM from reading plaintext genomic datasets. As presented in Subsection 3. 2, the SF employs computationally strong homomorphic encryption that makes the possibility of an exposure negligible even if insiders get access to the encrypted datasets.
To prevent any kind of information leakage, we use the whole datasets are encrypted. The SM performs all computation on the EGDS. Since the original dataset always remains encrypted on the CS, there is no direct way in which the insider can access this data unless he is able to break the encryption method. With the privacy model and the analysis of attacks, we find that our EGDS is strongly secure from the insiders. 2. We did not present the query processing steps between the CS and the MO/doctor in detail in this paper. However, based on the formal model for security of BGV scheme provides privacy of the MO/doctor as long as the underlying homomorphic encryption scheme is secure. 3. Prevention of abuse of PEP-side caching in Sub-subsection 3. 2, we describe the risks involved because of the standard caching methods used in the CS.
EXPERIMENTAL RESULTS This section presents the computation cost analysis of genomic datasets encryption, computation and decryption time. Our objective is to prove that the computation cost of homomorphic encryption scheme is close to be practical. We explain how to set the parameters for homomorphic evaluations and present our experimental results. We use HElib software library that implements BGV scheme. HELib is written in C++ and based on the arithmetic library NTL (http://www. shoup. net/ntl/) over GMP (https://gmplib. org/). HElib Library. Due to use of BGV system we can evaluate many such instances in parallel using batching. We further use additional optimization including a systematic use tree-structured multiplications to achieve very low multiplicative depth on the specific kind of algorithms involved in our study.
Datasets and ABO Rules We utilize the public available dataset Blood Group Antigen Gene Mutation Database (BGMUT), which is an online repository of allelic variations in genes that determine the antigens of various human blood group systems. Currently, the database documents sequence variations of a total of 1251 alleles of all 40 gene that together are known to affect antigens of 30 human blood group systems. BGMUT is a part of the dbRBC resource of the National Center for Biotechnology Information, and is available at (https://www. ncbi. nlm. nih. gov/projects/gv/mhc/xslcg i. cgi?cmd=bgmut/home). We develop two ABO rules using BGMUT datasets. We run our HElib-based prototype on these datasets and evaluate our ABO rules to see the presence of ABO blood type for each patient. We have two ABO rules labeled as ABO-1, and ABO-2.