dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental study In this experimental study, a power supply (GEN40-125 of GENESYS) and an active load (Chroma 63203) are used to apply a charging and discharging cycles to a supercapacitor following the current profile shown in Figure  2. A temperature-controlled chamber is used to conduct the experiment under a constant temperature of 65°. The bias voltage and charging/discharging current are measured and saved using the acquisition board NI cDAQ 9178 which convert analog signal (from current and bias voltage sensors) to digital signal with a 32-bit resolution. To facilitate results visualization and experience monitoring, the acquisition board is connected to a personal computer and a specific program developed in LabVIEW is used. Figure  1  shows the experimental bench used. The sampling time used for voltage/current measurements is set to 0.
Key Points Drawing on data from the World Health Organization International Clinical Trials Registry Platform and employing a difference-in-difference-in-differences estimation approach, our study builds on the existing literature and shows that the PRV does not show significant effect at generating trial activity. The policy does not seem to have triggered an increase in R&D for the intended diseases following its implementation, not even in late-phase trials, which are generally common in the neglected diseases era when repurposing drug indications or developing new treatment regimens. Accordingly, the benefit of the voucher, whether used or sold, does not appear a sufficiently strong incentive for pharmaceutical companies to embark on risky R&D. Three main policy recommendations arise from our study.
The voucher can only be awarded to products (drugs, vaccines, and devices) for one of the eligible diseases and for which previous trials, other than phase I trials, were registered in the US registry (ClinicalTrials. gov) within 21 days of enrolling the first patient in the trial. Eligible products are products that demonstrate their non-inferiority to existing products and contain no active ingredient that has been approved in any other application (although combination products with at least one new active moiety are eligible). Accordingly, translating these requirements to trial activity, eligible trials are trials other than phase I that ( Lastly, the program has faced structural changes since its implementation, in 2011 and 2014. Since 2011, a fee has to be paid to the FDA to compensate for the incurred added cost of conducting a priority review, which varies annually-from $2.
32 million in 2014 up to $5. 28 million in 2012  [9]. Before 2014, companies that were granted (or bought) the voucher and wanted to use it had to notify the FDA 365 days in advance, but as this was limiting the usefulness of the voucher, it was changed to a 90-day notice period. In that same year, it was decided that the voucher could be sold an unlimited number of times as opposed to once. 1 Medical countermeasures are medical products intended to diagnose, prevent, or treat diseases or conditions associated with chemical, biological, radiological, and nuclear (CBRN) threats and emerging infectious diseases  [25]. Between September 1999 and December 2011, 118,634 trials were registered in the US National Institutes of Health (NIH) registry (ClinicalTrials. gov), but only 1541 (1%) were for neglected diseases  [2].
Therefore, the objective of this study was to assess whether the PRV has successfully incentivized R&D for the intended tropical diseases that would have not taken place in its absence. We built on previous studies by employing a difference-in-difference-in-differences approach. As the policy targets specific diseases (Fig. 1 ) from a specific regulatory body/trial registry (i. e. the FDA/ClinicalTrials. gov), we defined two control groups: the diseases that are not targeted by the PRV (non-eligible diseases) and the registries that are not affected by the PRV (any registries other than Clinical-Trials. gov that are members of the WHO International Clinical Trials Registry Platform [ICTPR]; (see Online Resource 2 in the ESM).
Methods Data on clinical trials were retrieved from the WHO ICTRP, which gathers ongoing, completed, and terminated clinical trials from 18 registries (see Online Resource 1 in the ESM) and whose objective is to provide a comprehensive database on clinical trial activity to ultimately improve research transparency  [14]. Since its creation in 2005, registries have progressively entered the platform, conditional on fulfilling Each disease name was separately entered in the database to retrieve the data. In case a disease has more than one name (e. g. onchocerciasis/river blindness), all names were independently entered. We estimated a DDD by differencing (1) trial activity for the eligible diseases with non-eligible diseases in the registry affected by the policy (i. e. ClinicalTrials. gov), (2)  with trial activity for the eligible diseases with the non-eligible diseases in the control registries that were not affected by the policy (i.
e. any registries other than ClinicalTrials. gov from the WHO ICTRP). Since the disease eligibility requirement of the PRV is to be an infectious disease with no market in HICs, we defined non-eligible diseases as noncommunicable diseases (NCDs) with a significant market in HICs. Accordingly, we chose the NDCs that account for the highest number of disability-adjusted life-years (DALYs) in HICs according to the Global Burden of Disease project  [16] : ischemic heart disease, diabetes, lung cancer, and stroke. In this way, we can assure that the diseases in the control group have no chance of becoming eligible for the policy, nor is there a reason to believe in any spillover effects of the policy on these diseases. Accordingly, the validity of the DDD estimator resides on the relative outcome (i.
e. trial activity) of eligible diseases and non-eligible diseases in the treated registry (i. e. ClinicalTrials. gov) to trend the same way as the relative outcome of eligible diseases and non-eligible diseases in the control registries, in the absence of the policy  [17, 18]. We test for this assumption in the analysis, as explained below in more detail. Lastly, given the eligibility requirements on the products' trials for the eligible diseases, only interventional and noninferiority trials from phases II-III targeting either a drug or The voucher can be unsuccessfully used, which means that it was used to speed up the review of a product (most likely always for market approval) but was rejected by the US FDA. If successfully used, the voucher was used to speed up the review of a product that was approved by the FDA.
b Pretomanid tablets in combination with bedaquiline and linezolid is used for the treatment of a specific type of highly treatment-resistant tuberculosis of the lungs. Source: United States Government Accountability Office  [4]  Year a vaccine were kept. We discarded devices from the analysis as transparency regarding trial registration tends to be lower than for drugs or vaccines  [19]. Furthermore, incentives and regulations for devices differ from those of vaccines and therapeutic treatments. We focused our analysis on trial activity from 2005 to 2019 inclusive. Data on trial registration before 2005 are incomplete because before then it was not compulsory to register a trial to be eligible to publish its result in a journal that follows ICMJE recommendations  [20]. Indeed, in 2005 there was a peak in trial registration, both in ClinicalTrials.
gov and in the control registries, with a percentage increase between the years 2004 and 2005 of around 310% and 170%, respectively. Our analysis includes 13,803 trials: 7308 trials have been registered in ClinicalTrials. gov, of which 1029 trials were registered for the eligible diseases. About 6495 trials have been registered in the control registries, i. e. any registries other than ClinicalTrials. gov that are members of the WHO ICTRP, of which 704 were registered for the eligible diseases (refer to Fig. 2  for trial activity at the registry level). The database was then organized as follows: for each disease and each registry, we counted the number of clinical trials starting each year. Therefore, our dependent variable is the yearly number of starting clinical trials per registry and disease, resulting in a yearly panel of disease_registry (e.
The E y jit |X jit = exp 1 after it + 2 after it × eligible i + 3 after it × ClinicalTrials. gov + 4 after it × eligible i × ClinicalTrials. gov + 5 DALYs it + 6 User_fee jit-1 + j × i + t. variable after equals 1 from the year the disease became eligible for the PRV (i. e. 2007, 2014, 2015, 2016, and 2018 depending on the disease) and 0 otherwise. The variable eligible equals 1 if the disease is eligible for the PRV and 0 otherwise. The variable ClinicalTrials. gov equals 1 if the trial is registered in that registry and 0 otherwise. The coefficient 4 is the DDD coefficient and compares the change in trial registration for the eligible diseases with the noneligible diseases, in both the treated and control registries, before and after the policy. The coefficient 2 is the DD coefficient and picks up any diseases and year-specific effects that are correlated with the policy.
The variable User_fee takes a non-zero value only for trials targeting eligible diseases and registered in ClinicalTrials. gov from 2011 onwards. The fee has been increasing over the years and as it is unlikely to have an immediate impact on trial registration, we considered potential lagged effects. More precisely, in the main specification we assumed a 1-year lag effect of the user fee value on trial activity (e. g. the user fee of the fiscal year 2014 is assumed to affect trial registration in 2015, etc. ), but which we extended to further lags in the robustness section. We also tested for a longer delayed effect of the policy (up to 10-year lags) for diseases that were eligible since the policy started. As for the two policy modifications that took place in 2014, i. e. selling the voucher multiple times and the reduction to a 90-day notice to the FDA, these are captured as part of the overall policy effect for the diseases becoming eligible as of 2014.
1) demonstrate the non-inferiority of the tested product; (2) are interventional; and (3) are registered on ClinicalTrials. gov within 21 days of enrolling the first patient but after the program start date (27 September 2007). Trials registered on or before that date are also considered eligible if they were still ongoing as of 26 December 2007. For diseases eligible in 2014, 2015, and 2016, trials should have been initiated after their date of eligibility.
Triple-differences marginal effects on the impact of the priority review voucher on clinical trial registrations All regressions include the control variables, as well as registry_disease and year fixed effects (Poisson model). In Model 1, After × Eligible × ClinicalTrials. gov is the triple-difference estimator and captures the effect of the PRV on clinical trials registration. In Model 2, lags are bundled together by blocks of 2 years. The first block (Lag0-1) includes the policy year and the year after, and the second block (Lag2-3) includes years 2 and 3 post policy implementation, etc. Fixed effects capture time-invariant unobserved characteristics that are specific to the registry, disease and year.
Study design and data collection This is a descriptive, retrospective, single-centre study carried out at Toulouse University-Hospital Centre (UHC) and includes one hundred and one patients suffering from MSA. We used the cohort from the centre of reference in neurology service at Toulouse UHC, where the patient's follow-up rate is annual. This is one of the largest cohorts of patients with MSA, with a prospective monitoring database shared by Toulouse UHC and Bordeaux UHC and registered at the CNIL (Commission Nationale Informatique et Liberté, n° 1 338 780; CCTIRS, n° 10. 065). To be included, the patients had to be adults with MSA confirmed by a neurologist and have had an ENT examination in the voice and swallowing unit (unité de la voix et de la déglutition, UVD) of UHC. After having extracted the patients corresponding to these criteria from the list, in compliance with the regulations, we sent a letter providing information about the study to the patients who were still alive.
Our study was carried out in the year 2020 using computer software to manage the Toulouse UHC patients' files (ORBIS, Version 3. 5 from 13/07/2016, AGFA HEALTH-CARE France). This software manages UHC healthcare data via a healthcare data host. It enabled us to build a database comprising three sources of information: data extracted from the MSA centre of reference (BDAMS), data from the standardized ENT assessment reports produced by the specialists at UVD and archived video recordings of Pharyngolaryngeal examinations. The data collected from the BDAMS included information on the population characteristics (gender, BMI, status and cause of death, medicinal treatments) and the disease characteristics (phenotype and type of MSA, age at onset of symptoms, age at diagnosis, age at death) and data on the severity of the disease based on the different clinical scales, particularly a scale defined specifically for MSA: UMSARS (Unified Multiple System Atrophy Rating Scale)  [12].
These data are collected annually, if possible, in the context of follow-up care provision. UMSARS comprises 4 parts. It assesses the progression of the disease in terms of daily activities, motor and autonomic deficiency, and overall disability: • UMSARS I (Historical Review), total score out of 48: assesses the functional consequences of the disease on daily activities and the severity of some non-motor symptoms; We only retained parts I (daily activities) and II (motor examination), which are generally used to monitor the severity of the disease  [8]. To obtain the progression kinetics, we looked for and selected two scores in the BDAMS: • UMSARS ent : established during the consultation with a neurologist that was the closest in time to the ENT consultation. The maximum interval between the neurological and ENT consultations to define UMSARS ent was set at 100 days to limit the risk of an excessive clinical degradation in the interval between the two data collections.
• UMSARS pre : compiled during the preceding visit to the centre of reference in neurology. There was a maximum interval of 3 years between the two UMSARS collections. The Pharyngolaryngeal data were compiled by analyzing the correspondence of ENT specialists. All patients were assessed in the same way, using a standardized assessment that specifically targeted swallowing. The report follows the same format and systematically includes patient complaints (dysphagia, dysarthria, dysphonia), medical history, feeding and nutritional status (malnutrition, change in diet, feeding support), lung and respiratory status (pneumopathies, respiratory distress), fiberendoscopic Pharyngolaryngeal assessment with a swallowing test and, if necessary, fluoroscopic evaluation of swallowing. The main complaints, feeding status and swallowing problems are presented on a checklist.
We also collated the treatment of ventilation complications (non-invasive ventilation/NIV, tracheotomy, continuous positive airway pressure/CPAP). To finish, because we usually recorded the nasofibroscopic examination, we searched through the UVD's digital archives the video recording for details of the examinations of the patients included. Out of the 101 patients, we only found 22 video recordings. These examinations were reread by two ENT specialists, who had no knowledge of the patients' medical data using a report form describing the morphological and dynamic aspects of the pharyngo-larynx, and the characteristics of the swallowing difficulties.
Statistical analysis The descriptive results of the quantitative data are presented in the form of means and standard deviations (± SD) for the data following a normal distribution, and in the form of medians with their limits (minimum, maximum) or their interquartile ranges (Q1, Q3) for the data not following a normal distribution. For the qualitative data, the results are expressed in the form of percentages and frequencies of modalities with their confidence intervals [CI95%]. The descriptive data analysis was carried out for all the subjects since they all underwent an ENT consultation at Toulouse UHC, i. e. 101 patients with variations linked to the missing values: all the parameters were not systematically recorded despite the standardized data collection sheet. Missing data are less frequent for the Pharyngolaryngeal complaints (missing data for six patients, n = 95) and nasofibroscopy descriptions (missing data for seven patients, n = 94).
We were able to perform the bivariate analysis on 75 patients and the multivariate analysis on 33 patients because of the accumulation of missing data linked to the number of variables introduced in the model. • survival: determined by the number of months of progression between the date the symptoms appeared and the date of death. The multivariate analysis was done on 50 patients, which corresponds to the number of patients who had died. The data were analyzed with XLStat version 2021. 2. 2 software.
Software and code Policy information about availability of computer code Data collection No software was used for data collection Data analysis -Adapters and poor quality bases in short-reads data were removed with Trimmomatic v0. 33 -Draft genomes were assembled using Spades v3. 11. 1 -Summary statistics of the assembly was performed with Quast v4. 5 -Quality of the genomes was assessed using CheckM v1. 1. 2 -Species detection was performed using Centrifuge v1. 0. 3-beta and Kleborate v2. 0. 4 -Average nucleotide identity (ANI) was estimated between NCBI genome references and the genomes with discrepancies between Centrifuge and Kleborate species assignation. The ANI was estimated using FastANI v1. 32 -Depth coverage was obtained using BWA v0. 7. 17, Samtools v1. 9 and Bcftools v1. 8 -Sequence type was identified using multilocus sequence typing (MLST) scheme applied in ARIBA v2.
13. 5 -Phylogroups were obtained with ClermonTyping tool v1. 4. 1. -Short-reads for main STs (ST131, ST10 and ST117) were mapped against reference genomes using Snippy v4. 3. 6 -Pairwise SNP distances were obtained using snp-dists v0. 7. 0 -AMR genes and plasmid replicons were identified using the ResFinder and PlasmidFinder databases with ARIBA v2. 13. 5 and Abricate v0. 9. 8. -Mutations in gyrase genes and topoisomerase IV genes were obtained using ARIBA v2. 13. 5 -Prediction of contigs as plasmid or chromosomal origin was performed using MOB-suite v2. 0 and RFPlasmid. -Plasmid subtyping was performed using the Blast+ v2. 9. 0 and the plasmid MLST database (https://bitbucket. org/genomicepidemiology/ pmlst_db/) -Diversity analysis was performed using R packages vegan v2. 5-6 and iNEXT v2.
March 2021 -Pangenome was obtained with Roary v3. 12. 0 -SNPs were extracted from the core gene alignment with snp-sites v2. 5. 1 -Phylogenetic trees were constructed using IQ-tree v1. 6. 11 -Tree and metadata were plotted with ggtree v2. 0. 2 -Statistical analysis (Fisher's Exact Test, Kruskal-Wallis and Mann-Whitney U) were performed using rstatix's v0. 5. 0 -Plasmidome and pangenome network were analyzed using PANINI web tool (https://panini. cgps. group/) -Base-calling of the fast5 files and demultiplexing of the Nanopore reads were performed with Guppy Basecaller v4. 5. 4 and Guppy Barcoder v4. 5. 4. These tools are mentioned in 'Section 2' of the 'Supplemental Methods' available in the supplementary material file. -Long-reads were trimmed and filtered using Porechop v0. 2. 4 and Filtlong v0.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy Illumina sequence read data generated in this study have been deposited in the European Nucleotide Archive (ENA) database and are publicly available under BioProject PRJEB38235 [https://www. ebi. ac. uk/ena/browser/view/PRJEB38235], PRJEB42322 [https://www. ebi. ac. uk/ena/browser/view/PRJEB42322], PRJNA523640 [https://www. ebi. ac. uk/ena/browser/view/PRJNA523640], PRJNA556083 [https://www.
ebi. ac. uk/ena/browser/view/PRJNA556083], PRJNA740259 [https://www. ebi. ac. uk/ena/browser/view/PRJNA740259] and PRJEB50837 [https://www. ebi. ac. uk/ena/browser/view/PRJEB50837] and individual accession numbers are available in 'Source Data' file. Long-read data for the subset of 20 genomes are deposited in the ENA under BioProject PRJEB54884 [https:// www. ebi. ac. uk/ena/browser/view/PRJEB54884]; the accession numbers for each genome are placed in the 'Source Data' file. In the 'Source Data' file, accession numbers and hyperlinks for each genome analysed in this study, alongside with the isolate collection metadata, are included.
2009 ) and emission-line maps from the Siding Spring Southern Seyfert Spectroscopic Snapshot Survey (S7;  Dopita et al. 2015; Thomas et al. 2017). The S7 integral field spectroscopy datacube was obtained with the Wide Field Spectrograph  (Dopita et al. 2007 ). Their analysis confirmed the extended emission line regions along the front cone, but also revealed an optical view of the countercone on the far side of the galaxy  (Davies et al. 2016)  1 From the NASA Extragalactic Database: https://ned. ipac. caltech. edu which was originally found from extended soft X-ray emission  (Bianchi et al. 2007). Davies et al. (2020)  computed an ionized gas mass outflow rate of 0. 007 M yr -1 based on spectra from the VLT/Xshooter instrument covering the inner ∼300 pc. While the optical spectrum often only shows narrow lines, broad lines are present in the infrared regime  (e.
0 as part of ESO program 095. A-0934 (PI Juneau). We combined four exposures of 10 minutes, adding to a total of 40 minutes on-source. We changed the rotation angle by 90 degrees between each object exposure (O), and acquired sky frames (S) of 60s following the sequence O-O-S-O-O-S. Rotating the instrument between exposures was recommended by ESO in order to help correct for patterns of the slicers and channels when co-adding the exposures, and therefore obatin more uniform noise properties. The data were processed with the ESO Reflex  (Freudling et al. 2013, v1. 0. 5 ) implementation of the MUSE data reduction pipeline  (Weilbacher 2015). The reduced data cube has spaxels of 0. 2 × 0. 2 on the sky with wavelength spacing of 1. 25 Å, which corresponds to ∼ 55 -75 km s -1 over the wavelength range of interest.
The MUSE spectral resolution varies from 1750 at 4650 Å to 3750 at 9300 Å. The full spectral range covers 4750-9350 Å though we fit the spectra up to 8900 Å, and focus the emission-line analysis at λ <6800 Å. A stat cube of the same dimension contains the variance, which is helpful to check overlap with strong sky lines, and to mask them during spectral fitting (Section 4. 1). Our MUSE program supplements previous observations by providing improved sensitivity and a larger field-of-view, and in the case of S7, higher spatial resolution as well. Indeed, one can see a high level of details with very sharp edges defining the front ionization cone shown in green with solid arrows in Figure  1 , while also finding clear indications of the counter (back) cone between the dust lanes (dashed arrows).