dataesr/scientific-paragraphs-categorization huggingface dataset:
The query operation sharing is a guiding characteristic and at the same time impacted by the two other properties. Sharing computation among multiple concurrent queries was first studied by Sellis in 1988 in the context of multi-query optimization (MQO). Recently, this principle has been reproduced in the context of Cloud Databases under the name "Pay One, Get Hundreds for Free"  [39]. The identification of common subexpressions of queries is the key issue for the performance of multi-query processing, even for a small set of queries, which was the natural hypothesis of existing studies. Historically, the Problem of Multi-Query Optimization (PMQO) has been largely studied in the 80's  [12] ,  [54]  in the context of relational databases  [48]. This problem has been revisited in all database generations without any exception since it aims at optimizing the global performance of queries collectively instead of individually.
The PMQO has been combined with several important database problems such as caching  [44] , materialized view selection  [38] , reusing  [22] , indexing  [27] , data partitioning  [5] , optimizing exploratory queries  [30]. In the context of relational data warehouses, the PMQO has been amplified since OLAP workloads are a windfall of query sharing, where typical OLAP and reporting workloads are overlapping. Regarding the dynamic aspect of OLAP queries, the diversity of data analysis and the changing business directives make the query workload more dynamic  [51]. This dynamicity has contributed to increasing the research studies on self-tuning and autonomous databases  [2, 10, 45]. Several reactive approaches such as DynaMat  [31] , WATCHMAN  [52] , and Materialized Query Table (MQT) advisor  [47]  for dynamic materialized view selection have been proposed.
Related Work This section discusses the most important studies dealing with PMQO and VSP either in isolation or jointly. MQO is one of the main topics studied by the database community  [50]. A specific chapter on MQO has been reserved in the encyclopedia of Database Systems edited by Ling Liu and Tamer Özsu  [36]. Before connecting MQO to VSP, it would be wise to provide its separate generic formalization. For a given workload of queries to be optimized, where each query has a set of individual plans, the PMQO aims at finding the best merging of query plans where the global query processing cost is minimized. The PMQO has proven as NP-hard in  [57]  where its search space formulation was given. An A* algorithm with bounding functions and intelligent state expansion, based on query order, to eliminate states of little promise rapidly has been proposed and guarantees an optimal solution, for a small set of queries  [54].
Several variations of Sellis's algorithms have been proposed  [57]  to prune the search space of the PMQO. Genetic, simulated annealing and Depth-first Branch-and-Bound algorithms have also been experimentally analyzed to handle larger MQO problems that cannot be solved using A* in a reasonable time  [15]   [55]. Other algorithms are based on game theory  [3]. [50]  address the problem of extending top-down cost-based query optimizers to support multi-query optimization, and present greedy heuristics, as well as implementation optimizations. Their techniques were shown to be practical and to give good results. The adaptation of these findings has been reproduced in several generations of databases through their query languages: object-oriented databases (OQL)  [61] , semantic databases (SPARQL)  [34] , XML databases (XPATH)  [19] , distributed databases  [30] [40] , stream databases (CQL)  [17] , graph databases (Sparql 1.
1)  [18] , data mining  [42]  and SQL-on-Hadoop systems  [14]. MQO and physical design are the most active research topics in the field of databases and information systems. These two problems interact with the use of the query interaction which has a great impact on logical and physical optimizations. Despite their strong dependency, MQO and the instances of physical design have been tackled separately without really taking into account their interaction. The PMQO has usually been studied for a static set of queries, where several variations of the A* algorithm have been proposed for finding optimal solutions to the moderately sized (up to ten queries)  [55]. Other studies advocated the fact that rather than trying to obtain an optimal solution, finding near-optimal solutions in less time is suitable.
Background In this section, we present some fundamental notions and definitions related to hypergraphs and their ability to manage the three properties of analytical queries. Hypergraphs are powerful tools for representing complex and non-pairwise relationships. They contributed in several domains in capturing the interaction between studied objects such as data mining, text/image retrieval, bio-informatics, social mining, and machine learning  [26]. In the database fields, Hypergraphs have been used at logical and physical phases (e. g. , the detection of functional dependencies  [20] , data partitioning for optimizing OLTP workloads  [16]  and materialized views selection for optimizing largescale OLAP workloads  [7] ).
Efficiency Study In this section, we present an experimental validation of our approach using the following environment: a server with E5-2690V2, 3 GHz processor, 24 GB of main memory, and 1 TB of the hard disk. We generate a DW with 30 GB deployed in Oracle 12c DBMS and queries using SSB generator modules. Our approach has been compared against two approaches: (1) the approach proposed in  [58]  (that we name it YANG) which is considered the pioneer study in data warehouses that highlights the strong dependency among PMQO and VSP. For YANG we have de- Impact of selected views on query processing and materialization costs The goal of this experiment is to study the contributions of the selected materialized views by our approach and the other algorithms on the overall query processing and their materialization costs.
To perform this experiment, we have considered a data warehouse with 30 Gb and a workload of 100 queries using SSB Benchmark. As shown in Fig. 15a , there is not a big difference between our approach and Yang's algorithm, which demonstrate that our approach does not avoid the selection of the best-materialized views. (b) Dynamic Materialization with Query Scheduling: To perform this experiment, we have used the same above data by considering SSB data set with 30 Gb and a query workload of 100 queries generated randomly using the SSB generator. As shown in Fig. 15b , our approach outperforms largely Phan's algorithm. This is due to the minimal number of dropping in our approach than that of Phan. In addition, the materialized views selected by our approach are used maximally to optimize the appropriate queries before their dropping.
The main particularity of our approach is that it covers the two main categories of the dynamic selection of views. Our proposal implemented by a simulator called ProRes was validated theoretically using mathematical cost models and its results were directly implemented on the Oracle DBMS. The obtained results are encouraging and show the efficiency and effectiveness of our approach. Our work opens several challenges: (i) currently, we are integrating our proposal into PostgreSQL DBMS, (ii) considering other optimization techniques such as horizontal data partitioning and indexes, (iii) integrating the machine learning techniques for predicting the appropriate optimization structure for the ad-hoc queries. (iv) reproduce our proposal for using hypergraphs to deal with SPARQL queries in semantic databases.
Three additional individuals from the National Eye Institute, National Institutes of Health in the United States, were identified to carry the variant by assessment of custom capture nextgeneration sequencing data from commercial clinical genetic testing panels for STGD, MD or retinal dystrophy. For allele frequency (AF) calculations, an overall cohort of 525 Palestinian probands was considered. This final cohort includes the Palestinian probands present in cohorts 1 and 2 (175 and 38, respectively) and all 312 probands in cohort 3. Because cohort 4 is largely composed of affected siblings and relatives, it was not included in the calculation of the AF. A group of 1400 non-IRD Palestinian probands, for which WES data was available, were used as controls. Statistical analysis of the results was performed using a one-tailed Fisher's exact test.
Variant Selection by in Silico Analysis Variant selection from smMIPs sequencing data started with quality filtering on the overall coverage of the variants (>20 reads, given an average of 340 reads per nucleotide) and the percentage of variation at the position (variant present in ≥25% of all reads). Further variant prioritization was performed considering the AF, being <0. 005 in the dbSNP database and <0. 01 in general population databases, such as the Genome Aggregation Database (gnomAD; http://gnomad. broadinstitute. org/). In silico analysis was performed using the deep-learning tool SpliceAI to identify putative variants causing splice defects. 36 SpliceAI predictions were considered for each nucleotide position in ABCA4, and the script was set to return the five highest delta scores for acceptor gain, acceptor loss, donor gain, and donor loss, in a window of 5000 nucleotides (nt) up-and downstream from the selected position.
Likely pathogenic deep-intronic or noncanonical splice site (NCSS) variants were selected if the delta score was >0. 1 (range 0-1). Variants were considered to be part of the NCSS when located between -14 to -3 upstream of an exon and +3 to +6 downstream of an exon, or positioned within the first and last two nucleotides of an exon. Alamut Visual 2. 13 software and its splice defect prediction tools (SpliceSiteFinderlike,  37  MaxEntScan, 26 NNSPLICE 30 and GeneSplicer 27 ) were used as a visual aid to identify the position in which SpliceAI delta scores were predicted and to characterize the genomic context around the variant.
Transfection of the mutant construct was performed in duplicate. After 48 hours, RNA was obtained from the cells using the Nucleospin RNA kit (Machery-Nagel, Düren, Germany), and cDNA was synthesized from 1000 ng of RNA through the iScript cDNA Synthesis kit (Bio-Rad, Hercules, CA, USA). Reverse-transcription-polymerase chain reaction (RT-PCR), agarose gel analysis and Sanger sequencing were performed to assess the nature of splicing defects. RT-PCR conditions were as follows: 94°C for two minutes, followed by 35 cycles of 30 seconds at 94°C, 30 seconds at 58°C, and five minutes at 72°C, with a final extension step of two minutes at 72°C. Details on the primers used for mutagenesis, PCR, and Sanger sequencing can be found in Supplementary Table  S1. Ratios between different RNA products were assessed by semi-quantification using Fiji software  38  after agarose gel electrophoresis.
WES and Targeted Sanger Sequencing WES was performed using 3billion Inc. (Seoul, South Korea) and Variantyx Inc. (Framingham, MA, USA) sequencing services for 250 and 63 additional samples, respectively. Sanger sequencing (Macrogen Europe B. V. , Amsterdam, the Netherlands; analysis using Chromas software) was performed to confirm the presence of the c. 859-25A>G variant in this cohort and for 200 additional Palestinian IRD probands. PCR was performed using the following conditions: 95°C for two minutes; 40 cycles of 15 seconds at 95°C, 15 seconds at 60°C, and 45 seconds at 72°C; final extension at 72°C for five minutes. Details on the primers used for PCR and Sanger sequencing can be found in Supplementary Table  S1.
Haplotype Analysis for the ABCA4 Locus Haplotype analysis was performed on WES data for 16 probands using Automap tool (https://automap. iob. ch/) to show homozygous regions in chromosome 1. For haplotype identification within these regions, BAM files (generated using https://usegalaxy. org/) were uploaded into Integrative Genomics Viewer software (IGV v. 2. 9. 4)  39, 40  and were manually screened for each genomic location within the homozygous regions defined by Automap. The ABCA4 locus was screened manually. Variant calling format files (vcf) provided by 3billion were used to generate annotated genetic variants spreadsheets using ANNOVAR online tool (https://wannovar. wglab. org/). Within the ABCA4 region, haplotype information for 23 single nucleotide polymorphisms spanning over 125 kb was obtained.
Clinical Phenotyping All individuals were examined by an experienced ophthalmologist for clinical analysis. The phenotype data collected included the following: anamnestic information on disease onset, symptoms and progression, age at last examination, best-corrected Early Treatment of Diabetic Retinopathy Study (ETDRS) chart visual acuity converted to logMAR (31/33 probands), clinical ocular exam by slit lamp biomicroscopy, kinetic and/or static perimetry, fundus photos, fundus autofluorescence (FAF) and optical coherence tomography (OCT). When available, age at electroretinography (ERG) and ERG respecting International Society for Clinical Electrophysiology of Vision standards were collected for both probands and affected relatives (20/56). 41 For most patients, information on a specific age of onset was not available; thus age at last examination was reported instead.
Identification of ABCA4 Intron 7 Variant c. 859-25A>G and In Silico Splice Defect Predictions Incorporation of SpliceAI predictions in the annotation of smMIPs sequencing data led to the identification of the c. 859-25A>G (chr1(GRCh37):g. 94,546,299T>C) variant in 10 patients of Palestinian descent in the 876 STGD and STGD-like probands of cohort 1 (Table  1 ). This novel variant is absent in control populations in gnomAD. The position of the variant outside the SAS consensus sequence, but still close to the intron-exon junction, suggested a putative effect on other splicing elements, such as the branchpoint sequence (BPS). Therefore, an in-depth in silico analysis of the variant was performed using modified settings for the deep learning tool SpliceAI. Using a "scanning" window of 5000 nt upstream and 5000 nt downstream of c.
859-25A>G, the strongest prediction entailed the skipping of exon 8 (SAS loss with a delta score of 0. 39), together with a new SAS gain 138 nt upstream of exon 8 (delta score 0. 31) (Supplementary Table  S2 ). Interestingly, a donor loss with a delta score of 0. 09 was predicted at the SDS of intron 7. Although this delta score did not reach the threshold of significance, it might be a contributing factor to exon 8 skipping (Supplementary Fig. S1A ). Considering that previous smMIPs sequencing datasets were not analyzed with the aid of SpliceAI, we re-examined the sequencing results of 606 genetically unsolved STGD or STGD-like probands (cohort 2)  17  and identified c. 859-25A>G in nine additional cases (Table  1 ). Similarly, analysis of cohorts 3 and 4 lead to the identification of seven and five additional probands carrying this variant, respectively.
c. 859-25A>G Resides on a Founder Haplotype Homozygosity mapping was performed using WES data from 16 c. 859-25G>A homozygous probands. Homozygous regions in probands carrying c. 859-25G>A varied in size from 85 kb to 108 Mb. The identity-by-descent (IBD) region starts at g. 94,546,299 (c. 859-25G>A) and ends at position g. 94,486,667 (rs933073), resulting in a shared homozygous region of 59. 6 kb (Fig. 2  and Supplementary Table  S5 ). Because WES data does not cover the ABCA4 locus completely, the exact boundaries of the IBD region cannot be determined. Intervals of 18. 2 kb and 10. 1 kb lacking SNP data to assess the haplotype are present at the 5ʹ (telomeric) and 3ʹ (centromeric) side, respectively. Thus the IBD region spans between 59. 6 and 87. 9 kb. In the 5ʹ region of the ABCA4 gene, four haplotypes can be distinguished (Supplementary Table  S5 ), suggesting that at least four different recombination events occurred in the ancestors of the STGD1 cases between variant c.
Our results are in line with the recently reported performance assessment of SpliceAI for intronic ABCA4 splicing variants. 60 Further confirmation and implementation of visualization tools to check the genetic context around the predicted effects is still advisable. Nevertheless, use of SpliceAI allows one to bypass more lengthy and spurious research of variants, which would otherwise entail the use of multiple tools focused on predicting single aspects of splicing, such as effects on NCSS, BPS, enhancer and silencer motifs separately. As for analysis parameters, the window of flanking sequences considered by the program has a significant impact on the predicted splicing defects of a variant, thus should be carefully taken into account. Both partial intron inclusions observed in the in vitro splice assay were predicted by SpliceAI, but a small window of analysis (e.
g. , ±100 nt) would not have identified either of these inclusions as they are located 113 and 660 nt upstream of the variant. Notably, the considerable distance of the cryptic SASs also prevented their detection in all other programs used in this study. In particular, analysis of the effect of c. 859-25A>G using Alamut Visual prediction tools resulted in a reduction of 1. 9 in the predicted intron 7 SAS strength (wild-type, 3. 7; mutant 1. 8 [range 0-21]) on GeneSplicer, which on its own would not be considered sufficient to select the variant for further testing. In this study, the effect of the variant was assessed at the RNA level using an in vitro splice assay using HEK293T cells. There is ample evidence that this in vitro model is able to correctly recapitulate splice defects affecting the consensus splice site sequences at the junctions of exons and introns.
On the other hand, it remains to be seen whether blocking these new SASs would lead to a correct transcript. We hypothesize, partially based on SpliceAI predictions, that this approach may lead to more exon 8 skipping. Experimental testing of potential AONs is needed to investigate this further. Outside of AON-based rescue, other therapeutic approaches that could be implemented are gene augmentation or gene editing. Gene augmentation therapy is successful for some retinal disorders (e. g. RPE65-associated Leber congenital amaurosis or early onset RP)  66  but remains challenging for STGD1. The relatively large size of the ABCA4 transcript prevents delivery with a single adenoassociated virus vector, which has a maximum packaging capacity of about 5 kb. 8] [69] [70]  An attractive alternative to gene replacement for ABCA4 is RNA-editing, which allows the correction of point mutations at the transcript level with low risk of permanent off-target effects.
Acknowledgments The authors thank  Moien Kanaan  for allele frequency data from 1,400 Palestinian persons who do not show inherited retinal dystrophies. We thank  Bin Guan ,  Ehsan Ullah , and  Chelsea Bender  for technical assistance, and  Stéphanie S. Cornelis  for help with Hardy-Weinberg calculations. Supported by the  RetinaUK , grant no. GR591  (to FPMC), a  Horizon 2020 ,  Marie Sklodowska - Curie Innovative Training Network entitled European Training Network to Diagnose, Understand and Treat Stargardt Disease , a  Frequent Inherited Blinding Disorder-StarT  ( 813490 ) (to FPMC), the  Foundation Fighting Blindness USA , grant no. PPA-0517-0717-RAD  (to FPMC and SR), the  Rotterdamse Stichting Blindenbelangen , the  Stichting Blindenhulp , and the  Stichting tot Verbetering van het Lot der Blinden  (to FPMC), and by the  Landelijke Stichting voor Blinden en Slechtzienden , the  Macula Degeneratie fonds  and the  Stichting Blinden-Penning  that contributed through Uitzicht  2016-12  (to FPMC).
Materials and methods QyScore® is a CE-marked and FDA-cleared software, developed by Qynapse (https:// www. qynap se. com/), that provides segmentation and volumetric measurements of grey matter (GM), white matter (WM), hippocampus (HP), and amygdala (AM) from 3DT1 images, as well as white matter hyperintensities (WMHs) from 3DT1 and FLAIR images. In addition, z-scores and percentiles are obtained from the comparison with a large normative database of healthy controls. The normative database includes cognitively intact individuals between the ages of 20 and 90 years, coming from European and North American databases  [10, 11]  (https:// brain-devel opment. org/ ixi-datas et; https:// www. human conne ctome. org/ study/ hcp-young-adult). The full panel of MRI markers described above is quantified in 15 min per patient.
Introduction Neurological disorders represent a major public health problem in Europe and the rest of the world  [1]. A systematic analysis for the Global Burden of Disease Study 2016 showed that neurological disorders were the leading cause of disability-adjusted life-years (worldwide 276 million) and the second leading cause of death (worldwide 90 million)  [2]. Magnetic resonance imaging (MRI) technology and the development of MRI markers of neurological diseases have been improved substantially in both research and clinical environments over the last 30 years  [3]. Automated MRI segmentation methods have been used in addition to visual analysis and manual segmentation assessments  [4] [5] [6] ,  1 3  Neuroimaging Initiative (ADNI)  [10] , the Open Access Series of Imaging Studies (OASIS)  [14] , the KIKI2009/ Kirby  [15] , the Parkinson Progression Markers Initiative (PPMI)  [11] , the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI), the ANMerge dataset  [16] , and a public segmentation database of Multiple Sclerosis patients (LIMTS)  [17].
Data from three additional cohorts were incorporated to enrich the sample: the Clinically Isolated Syndrome-COGnitive (SCI-COG) cohort, the REAC-TIV database, and the MEMORA cohort. We created three different databases: one for the measurement of GM and WM volumes, one for HP and AM volumes, and one for WMH volumes. Subjects in each database were well-balanced according to age (ranging from 20 to 90 years old), sex, scanner field strength (1. 5 T/3 T), and type of MRI acquisition (2D/3D for FLAIR images only). To ensure as broad a sample as possible in terms of volumetric measurements, we selected a heterogeneous population composed of healthy controls (HCs), mild cognitive impairment (MCI), Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and multiple sclerosis (MS) patients.
The ADNI was launched in 2003 as a public-private partnership led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, positron emission tomography (PET), biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and AD. For up-to-date information, see http:// adni. loni. usc. edu/ www. adni-info. org. The Open Access Series of Imaging Studies (OASIS) is a project aimed at making MRI data sets of the brain freely available to the scientific community. OASIS is made available by the Washington University Alzheimer's Disease (CDR) = 1 for AD  [14, 18]. PD was defined according to the features described by Marek and colleagues  [11]. FTD was defined according to frontotemporal dementia consortium criteria  [19]  and MS, according to Polman and colleagues  [20].
MRI data QyScore® analysis can be performed using MRI sequences with recommended acquisition parameters on 1. 5-and 3-Tesla (1. 5 T and 3 T) scanners, as reported in Table  1. More specifically, the software retrieves DICOM MRI data (non-contrast 3DT1 series acquired using 1. 5 T or 3 T scanners and T2 fluid attenuated inversion recovery (FLAIR) series acquired using 3-Tesla scanners) from a DICOM server and sends them to an analysis server. From the analysis server, before computing MRI analysis, QyScore® performs a quality check of MRI parameters to verify that the parameters are in line with the ones recommended (Table  1 ). The range of recommended parameters was selected for their suitability in a clinical routine setting. The analysis is performed if the parameters are within the "ideal" or "tolerated" ranges, as described in Table  1.