dataesr/scientific-paragraphs-categorization huggingface dataset:
Data management DAH was responsible for data management and maintained the editorial rights. All identified records were saved into a Microsoft Excel master file where records were tracked through the screening and data collection process by a unique study identification code. A simple system of record annotation was implemented to capture reasons for exclusion. At the end of data collection, checking and formatting, a pdf copy of the master file was created as a 'locked' record so that there is a version of the data that cannot be edited in error  (7 December 2015). An editable Excel version of this document can be downloaded (see Additional file 2).
From this step, a further 170 records were excluded, leaving 251 for data extraction. It is interesting to note that almost one third of those records excluded at this step was due to the small sample size of the study (see Fig. 1 ). Twenty-two records were excluded because they recruited participants below 18 years of age. Moreover, 55 full texts were excluded because the sample size was less than 20 participants and 11 full texts were excluded because they were not available in English. Instead, these were published in national journals written in the native language. So that the reader can scrutinise the data for evidence of geographical bias in these three full-text exclusion criteria, details are broken down by country in Table  2. This information gives some indication for a risk of bias excluding tinnitus studies conducted in the USA since 21 were removed on the basis of small sample size, leaving only 39 records from the USA contributing to the systematic review.
There is also a risk of bias excluding tinnitus studies conducted in China since six were removed because they were published in Chinese, leaving only three records from China contributing to the systematic review. Note that language bias was avoided for studies registered on ISRCTN and ICTRP since an English language translation is given. Ten trials in Iran, seven in Japan and two in China, two in Brazil and one in the Republic of Korea were included via this route. At least two co-authors performed each key step (i. e. title screening, full-text screening, and data collection) independently for every record. Due to an error in allocating full texts to co-authors, some records had data collection by more than two co-authors (31 were completed by three co-authors, 11 by four and 9 by five).
Discrepancies between independent co-authors were rare and were mostly Fig. 1  Flow diagram of study records accountable by differences in terminology. These were resolved by DAH who was responsible for data management. As per the protocol  [16] , inter-rater agreement was not calculated, but all co-authors reviewed and approved the master file before data lock. We pieced together data from multiple reports of the same study by manually screening all included records using author names, study title and trial registration number. This step of consolidating records happened throughout the data collection process, and in particular during the data formatting check. Where there were multiple reports, the data extraction reflects the information provided in the report with the latest publication date.
Data collection was conducted by a pool of 20 project team members (number of extracted studies ranged from 5 to 228, median 19. 5). The primary reason for not limiting data collection to a smaller pool was to lessen the resource burden since we received no grant funding to conduct the research activity. To mitigate against observer bias, a full set of guidance notes was produced for the data collection procedure and calibration exercises were conducted with new members of the review team prior to any individual contribution to this review. Both the sheet and the guidance notes were developed and revised across several review authors during a 3-day workshop and through two iterations of piloting. Data collection was conducted independently and with at least two team members for every included record.
e. 'comparison') comprised: (1) a pulldown list of study design options (randomised controlled trials, before and after studies, non-randomised controlled trials or case-control studies and cohort studies) and (2) a record of the duration of each intervention, separately for each arm of the trial. Outcome data items were: (1) the outcome domain(s) specified by the investigators, (2) the instruments specified by the investigators, and (3) time frame. Information relating to these three data items was recorded separately for all primary and secondary outcomes. Where authors were not explicit about this distinction, we tried to tease this information out of the article by reading the Methods and Results sections of each record. But if this was not possible, then all information was entered as a primary data item.
A 'setting' data item reported the country where the study was conducted. Supplementary information was also extracted from each included trial on: (1) the name and email address of the corresponding author, (2) the date of study start, (3) the aim of the trial, (4) sample size calculation, with a full-text extraction of the reported details, (5) the sample size, (6) a description of any modifications to the methods, particularly any discrepancies between the trial protocol and the subsequent report of the findings, and (7) the date of publication. The protocol was amended so that if minimum age of eligibility or sample size estimate was not reported, then the data collection recorded the minimum age of the recruited participants or the recruited sample size as the 'next best alternative' , where this information was given.
An additional data item not planned in the protocol recorded whether the study authors specified any minimal clinically important difference, or related construct that was used to interpret the clinical significance of the findings. For example, Cima and et al. [8]  specified a pre-versus post-intervention change of 0. 065 (SD 0. 15) in health utility score measured using the 36-item short form Health Survey. This information is not reported here, but will be presented in a separate manuscript. If any information is not reported, then 'not stated' was recorded in the corresponding field. Where a trial record consolidated several pieces of information (such as a protocol and the published findings), the data items reported in the synthesis related to the most recent publication. For those records in which several pieces of information are consolidated into a single record, we sought to detect any modifications to the methods leading to inconsistencies between the protocol and the final reported study.
Risk of bias in individual studies Given that the primary objective of this systematic review concerns methodology (not therapeutic effects), we limited the assessment of risk of bias to the data collection methods for consolidated records rather than any analysis of those data. In particular, we investigated where there were inconsistencies between the outcomes defined in the trial registration and/or protocol and those given in the subsequent study report. Of the 228 studies selected for inclusion, 60 (26 %) had multiple records. We examined only those consolidated records with a protocol and study report(s) comparing data items across records. From this set, 21 were found to have descriptions of eligibility criteria (inclusion or exclusion), primary outcome measures, and/or secondary outcome measures that were altered retrospectively in the final report.
Domains For the first part of the analysis, we scrutinised the data collected under the data item relating to the primary outcome domain(s) specified by each set of investigators. There were 505 data entries describing 35 different types of primary domain (Table  3 ). Domain grouping was conducted by a subgroup of tinnitus experts (three ENT surgeons, one audio-vestibular physician, and two researchers) and was broadly informed by the Cochrane Effective Practice and Organization of Care (EPOC) recommendations  [22]. Patient outcomes concerned with health status, well-being and health behaviours constituted the largest category by far and so we expanded this into domains relating to (1) the tinnitus percept, (2) the impact of tinnitus, (3) other co-occurring complaints, (4) health-related quality of life, and (5) body structures and functions (Table  3 ).
Instruments The second part of the primary objective was to identify and evaluate the current reported outcome instruments and for this we interrogated the data collected under the data item relating to the primary outcome instrument(s) specified by each set of investigators. Overall, there were 505 data entries describing 78 different types of instrument (Table  4 ). We used a categorisation scheme based on the one for domains. Instruments were grouped according to whether the tests relate to: (1a) the tinnitus percept (investigator-administered), (1b) the tinnitus percept (numerical rating scale), (2a) the impact of tinnitus (patient-reported questionnaire), (2b) the impact of tinnitus (numerical rating scale), (3) other co-occurring complaints, (4a) healthrelated quality of life (patient-reported questionnaire), (4b) health-related quality of life (numerical rating scale),  (5)  Table  3  Summary of all primary and secondary outcome domains across all 228 clinical trials.
A numerical rating scale of loudness was also a common approach (n = 37, 8 %), but there was little consistency in the measurement scale used (e. g. Table  5 ). Other domains relating to the impact of tinnitus were evaluated using a numerical rating scale predominantly annoyance (n = 21, 4 %), awareness (n = 10, 2 %), and distress (n = 7, 1 %). Numerical rating scales with 0-10 and 0-100 point scales were popular. About 16 % (n = 78) of the data entries did not clearly report the instrument used. These were classified into five subheadings under Table  4 , category 9. On 20 occasions (4 %), we experienced difficulty in interpreting the data entry (referred to as 'cannot code'). One recurring example was where investigators did not state the provenance of the 'tinnitus questionnaire' which could be either a published Tinnitus Questionnaire  [24, 25] , or a translation of one of these or to an authors' own instrument.
Pattern of usage of primary outcome instruments across years We also examined the status of selected primary outcome instruments over the time frame of the review (Fig. 3 ). Due to the wide variety of instruments, analysis focused on the most frequently used that were highlighted in the previous section (Instruments). For meaningful analysis, we split the total time frame into three periods, using the best available information. The first period was from 1 January 2011 to 12 March 2015 (i. e. the date of the electronic searches) (n = 102). The second period was from 1 August 2006 to 31 December 2010 (n = 99). The third period included any remaining studies in which data was collected on or before 31 July 2006 (i. e. before the Tinnitus Research Initiative (TRI) consensus meeting)  [15] , but not published until after this date (n = 27).
Tinnitus severity With respect to the primary domains, 96 out of the 505 came from studies that specified a severe tinnitus as an inclusion criterion. In those studies, an objective criterion was defined as some sort of minimum score on a published tinnitus questionnaire. For this subgroup compared to all 228 studies, we expected there would be a greater proportion of primary domains evaluating the functional impact of tinnitus, but this was not the case. The pattern was not noticeably different from the full dataset.
Hearing loss Forty-seven of the 505 primary domains came from studies that specified a hearing loss as an inclusion criterion. Again, only studies were considered where an objective criterion had been defined and this was typically a minimum hearing level in dB at particular frequencies. Compared to the full dataset reported in Table  3 , the proportion assessing tinnitus distress was slightly lower (4 % compared to 7 %). We also noted that the only study to report on a speech-based primary measure was part of this hearing loss subgroup  [33]. Other audiological domains such as loudness and pitch had the same pattern of usage as the full dataset.
Conclusions We are the first group to conduct a systematic review that targets the reporting of outcome domains and instruments in clinical trial designs that evaluate interventions for tinnitus. The findings of this review have produced an extremely rich dataset that has enabled us to address a number of different primary and secondary questions concerning different aspects of good trial design. Our findings add important new insights pointing to the lack of awareness and understanding of good trial design in so far as this relates to outcomes. A general lack of consensus regarding the choice of outcomes did affect trial design, conduct and reporting with particular reference to lack of sample size calculation, and lack of robust interpretation of whether the intervention was therapeutically beneficial or not.
the database, as well as the number of records identified by the search strategy (in parentheses): PubMed (National Centre for Biotechnology Information) (n = 759), EMBASE (Ovid) (n = 244), Cumulative Index to Nursing and Allied Health Literature (CINAHL, EBSCO) (n = 145) and the Cochrane Central Register of Controlled Trials (CEN-TRAL) (n = 560). A number of different electronic trial registers were also searched: ClinicalTrials. gov (n = 141), the International Standard Randomised Controlled Trial Number registry (ISRCTN, BioMed Central) (n = 22), the International Clinical Trials Registry Platform (ICTRP, World Health Organisation) (n = 183), and the Cochrane Database of Systematic Reviews (CDSR) (n = 23). Electronic searches were run on 12 and 13 March 2015 by authors DAH and AJS, and were not updated.
During the last four years, several studies confirmed that Mg 2+ supplementation alleviates VC in both rodent and bovine models  [21, 22]. Two studies further detailed the mechanistic aspects involved in phosphate-induced VC of human aortic VSMC (HAVSMC)  [23, 24]. The various studies showed that Mg 2+ negatively regulates VC through transient receptor potential melastatin (TRPM)7 activity and modulates expression of calcification markers such as anticalcification proteins (Osteopontin, Matrix Gla Protein), osteogenic proteins (Osteocalcin, Bone Morphogenetic Proteins), and osteogenic and VC related transcription factors (Cbfa1/Runx2 and Osx). In the present study, we investigated a panel of miRs during Mg 2+ attenuated inorganic phosphate-(Pi-) induced VC (Table  1 ). This selection of miRs was extrapolated from the literature, which was sparse at the initiation of the study, as well as miRNA databases.
We chose miR-29b, -30b, -125b, -133a, -143, -204, and -223 because they were shown to play a key role in the course of VSMC phenotypic switch, osteogenesis, or VC. Additionally, after browsing miR databases, we selected miRs that could target key mediators of VC such as col1a1, cbfa1/Runx2, Smad1, and Osx. For the first time in a human model, we were able to show the role of several miRs in the course of VC and thus gain additional mechanistic insights into the mode of action and beneficial effects of Mg 2+ during this process.
Specific Controls. Media from the various experimental conditions were assessed for correct Ca 2+ , Pi, and Mg 2+ levels using an ADVIA 1800 Siemens autoanalyzer (Siemens Healthcare Diagnostics). No change in pH was observed at Pi 3 mM with or without addition of Mg 2+. This excludes a potential role of medium acidification in the observed decrease of mineralization. In previous experiments  [24] , we checked whether the effects of MgCl 2 on calcification reduction were due to chloride ions in MgCl 2 salt, but addition of 2. 4 mM NaCl (the maximum concentration of Cl -used) did not inhibit calcification after 10 and 14 days of culture in presence of 3 mM Pi using AR, VK, and OCP methods (data not shown).
Anyway, Mg 2+ did not seem to have a beneficial effect in restoring basal miR-223 expression. Thus, the data Figure  2 : miR-29b and miR-204 expression is not significantly altered. Pi-induced HAVSMC were assessed at the time points 4 h, 24 h, day 3, day 7, and day 10. miR-29b and miR-204 were quantified using TaqMan microRNA assays (Applied Biosystem) on total RNA by q-PCR analysis and compared to control cells treated with basal medium. The control condition was set to 1 (dashed line). The cumulated relative quantity (RQ) of the three different donors of HAVSMC is represented as a ratio of the control condition and is expressed as mean ± SEM for each condition ( = 6). U6 small nuclear RNA was used as an internal reference gene. Statistics were realised with the Mann-Whitney test. No statistical significance was found.
Based on literature and confirmed by miR databases, a selection of 7 miRs was assessed in our HAVSMC model during Pi-induced calcification at 4 h, 24 h, day 3, day 7, and day 10. Our results confirmed the implication of miR-30b in calcification and brought miR-133a as well as miR-143 from phenotypic switch and vascular remodeling into the field of VC. To briefly comment on the results of the other selected miRs, Goettsch et al. [15]  found a statistically significant increase of miR-125b in human coronary artery smooth muscle cells after 21 days in calcifying conditions (10 mM -glycerol phosphate/10 nM dexamethasone/100 M ascorbate phosphate). In our model (HAVSMC), miR-125b expression was found to be stable for 10 days in calcifying conditions (3 mM Pi), confirming a late involvement of miR-125b in the VC process.
A more appropriate solution would be analytic models of the NoC on a FPGA device. Mathematical modelling consists in identifying links between the NoC parameters and the FPGA resources using a database and in extracting relations between them. In this paper, we present a methodological framework to estimate the number of resources required for a given communication architecture and the task graph of the application. The framework contains 4 steps: the design or the selection of the NoC, the data collection, the data analysis from which a model is deduced. The database obtained in the data collection step contains the synthesized results of each NoC configuration. Two NoCs, with a mesh topology and different characteristics, are used to provide two databases. The methodological framework provides the most appropriate models that are identified using predictive modelling.
ORION is a tool designed for fast and accurate power and area model on Integrated Circuit (IC)  [5]. The tool explores the area occupied (mm 2 ) and the power (mW) by the NoC used on 65 nm chips when routers and links increase. The 3D Tezzaron design flow also explores the 3D NoC to reduce the area of the chip and interconnects on ASIC to optimize power  [13]. A system level approach is proposed to explore the NoC design space with an objective to minimize the energy consumption and link bandwidth (timing). These works concern power and timing evaluation for ASIC. A power model is formulated for links and switches for the Nostrum NoC using Synopsys Power Compiler in  [15]. In  [16]  authors propose a performance analysis and dimensioning method for NoC based platforms (NoC-ADM). A software tool model NoC based systems is developed to determine Bounds on delay, buffer size and throughput and verify timing constraints.
METHODOLOGICAL FRAMEWORK A model for system dimensioning requires accurate estimations of the resources required by the application. First, we describe the characteristics of the FPGA structures explored using our approach. Second, we present the methodological approach for resources dimensioning. The methodological framework is illustrated in figure  1. The methodological framework is split into four steps: 1) Selecting the appropriate NoC structure, 2) Collecting the date required for analysis, 3) Analyzing the data to identify the most appropriate mathematical models, 4) Exploring the models to define the most appropriate parameters of the NoC according to the algorithm requirements. The principle of the framework is to select or design the parameterized NoC structure used to connect the IP cores of the application.
C. Step 3:Data Analysis The database of observed results was analyzed to identify links between variables and LUT, MLUT, FF regarding the variables n 1 , n 2 , n 3 and n 4. The aim was to mathematically model the relationships between the input configuration of the NoC and material resources used without going through the synthesis step. Data mining is used to automatically locate useful information among large quantities of data. Data mining can be both predictive and descriptive: when predictive, the aim is to predict the value of a particular attribute given existing data, when descriptive; the aim is to derive patterns that summarize underlying relationships in the data. Data mining is thus an integral part of knowledge discovery, which is the overall process of converting raw data into knowledge by obtaining selecting useful information from the data.
B. Synthesis options on models Models obtained for the FPGA resources depend on the synthesis options used. Options can be used according to the type of dedicated SoC. In the previous experiments and analysis, the default options are set in the Xilinx http://journals. uob. edu. bh Synthesis Tool (XST). In such configuration, XST can use all available resources of the target FPGA. This can be used for heterogeneous platform using hardware IP or software IP (embedded processor). For some cases, some resources can be reserved for dedicated IPs: LUT, FF for hardware IPs (using also DSP blocks) and MLUT for software IP (for instruction and data memories). Therefore, models depend on the type of dedicated SoC and the FPGA resources. Another major resource is the RAM blocks that can be used by the NoC.
Expression Analysis Total RNAs were extracted by guanidinium thiocyanate/phenol/chloroform and converted to first strand cDNA using IScript cDNA synthesis kit (Biorad). Real-time PCR were performed in a 96-well plate using the IQ SYBR Green Supermix (Biorad). Data were quantified by the ΔΔ-Ct method and normalized to RPLP0 (36b4) mRNA. Sequences of the primers used in this study are shown on S1 Table. For western blot analysis, cells were lysed in RIPA buffer. Proteins (50 μg) were resolved on 10% SDS-PAGE, blotted onto PVDF (Millipore), probed with specific antibodies after saturation and revealed using an enhanced chemiluminescence detection system (ECL kit, Amersham Biosciences) with appropriate specific peroxydase conjugated Abs. Antibodies were from Gentex (ERRα, GTX108166), Enzo (hsp90, ADI-SPA-830) and Sigma (Flag, F-3165).
2P2Idb is a hand-curated structural database dedicated to protein-protein interactions with known small molecule orthosteric modulators. It compiles the structural information related to orthosteric inhibitors and their target [i. e. related 3D structures available in the RCSB Protein Data Bank (PDB)] and provides links to other useful databases. 2P2Idb includes all interactions for which both the protein-protein and protein-inhibitor complexes have been structurally characterized. Since its first release in 2010, the database has grown constantly and the current version contains 27 protein-protein complexes and 274 protein-inhibitor complexes corresponding to 242 unique small molecule inhibitors which represent almost a 5-fold increase compared to the previous version. A number of new data have been added, including new protein-protein complexes, binding affinities, molecular descriptors, precalculated interface parameters and links to other webservers.